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Results with Julia v1.2.0
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Resolving package versions...
Installed Missings ──────────────────── v0.4.3
Installed DataAPI ───────────────────── v1.1.0
Installed ConstructionBase ──────────── v1.0.0
Installed TableTraits ───────────────── v1.0.0
Installed BinaryProvider ────────────── v0.5.8
Installed DiffEqBase ────────────────── v6.7.0
Installed DataFrames ────────────────── v0.19.4
Installed Modia ─────────────────────── v0.3.0
Installed DocStringExtensions ───────── v0.8.1
Installed DataValueInterfaces ───────── v1.0.0
Installed PooledArrays ──────────────── v0.5.2
Installed Requires ──────────────────── v0.5.2
Installed InvertedIndices ───────────── v1.0.0
Installed FunctionWrappers ──────────── v1.0.0
Installed Reexport ──────────────────── v0.2.0
Installed Compat ────────────────────── v2.2.0
Installed Tables ────────────────────── v0.2.11
Installed Roots ─────────────────────── v0.8.3
Installed TreeViews ─────────────────── v0.3.0
Installed OrderedCollections ────────── v1.1.0
Installed Parsers ───────────────────── v0.3.10
Installed RecipesBase ───────────────── v0.7.0
Installed IterativeSolvers ──────────── v0.8.1
Installed DataStructures ────────────── v0.17.6
Installed DiffEqDiffTools ───────────── v1.5.0
Installed RecursiveFactorization ────── v0.1.0
Installed JSON ──────────────────────── v0.21.0
Installed ArrayInterface ────────────── v2.0.0
Installed RecursiveArrayTools ───────── v1.2.0
Installed MuladdMacro ───────────────── v0.2.1
Installed StaticArrays ──────────────── v0.12.1
Installed Parameters ────────────────── v0.12.0
Installed ModiaMath ─────────────────── v0.5.2
Installed IteratorInterfaceExtensions ─ v1.0.0
Installed Unitful ───────────────────── v0.18.0
Installed SortingAlgorithms ─────────── v0.3.1
Installed CategoricalArrays ─────────── v0.7.3
Installed MacroTools ────────────────── v0.5.2
Installed Sundials ──────────────────── v3.8.1
Updating `~/.julia/environments/v1.2/Project.toml`
[cb905087] + Modia v0.3.0
Updating `~/.julia/environments/v1.2/Manifest.toml`
[4fba245c] + ArrayInterface v2.0.0
[b99e7846] + BinaryProvider v0.5.8
[324d7699] + CategoricalArrays v0.7.3
[34da2185] + Compat v2.2.0
[187b0558] + ConstructionBase v1.0.0
[9a962f9c] + DataAPI v1.1.0
[a93c6f00] + DataFrames v0.19.4
[864edb3b] + DataStructures v0.17.6
[e2d170a0] + DataValueInterfaces v1.0.0
[2b5f629d] + DiffEqBase v6.7.0
[01453d9d] + DiffEqDiffTools v1.5.0
[ffbed154] + DocStringExtensions v0.8.1
[069b7b12] + FunctionWrappers v1.0.0
[41ab1584] + InvertedIndices v1.0.0
[42fd0dbc] + IterativeSolvers v0.8.1
[82899510] + IteratorInterfaceExtensions v1.0.0
[682c06a0] + JSON v0.21.0
[1914dd2f] + MacroTools v0.5.2
[e1d29d7a] + Missings v0.4.3
[cb905087] + Modia v0.3.0
[67ccffd1] + ModiaMath v0.5.2
[46d2c3a1] + MuladdMacro v0.2.1
[bac558e1] + OrderedCollections v1.1.0
[d96e819e] + Parameters v0.12.0
[69de0a69] + Parsers v0.3.10
[2dfb63ee] + PooledArrays v0.5.2
[3cdcf5f2] + RecipesBase v0.7.0
[731186ca] + RecursiveArrayTools v1.2.0
[f2c3362d] + RecursiveFactorization v0.1.0
[189a3867] + Reexport v0.2.0
[ae029012] + Requires v0.5.2
[f2b01f46] + Roots v0.8.3
[a2af1166] + SortingAlgorithms v0.3.1
[90137ffa] + StaticArrays v0.12.1
[c3572dad] + Sundials v3.8.1
[3783bdb8] + TableTraits v1.0.0
[bd369af6] + Tables v0.2.11
[a2a6695c] + TreeViews v0.3.0
[1986cc42] + Unitful v0.18.0
[2a0f44e3] + Base64
[ade2ca70] + Dates
[8bb1440f] + DelimitedFiles
[8ba89e20] + Distributed
[9fa8497b] + Future
[b77e0a4c] + InteractiveUtils
[76f85450] + LibGit2
[8f399da3] + Libdl
[37e2e46d] + LinearAlgebra
[56ddb016] + Logging
[d6f4376e] + Markdown
[a63ad114] + Mmap
[44cfe95a] + Pkg
[de0858da] + Printf
[3fa0cd96] + REPL
[9a3f8284] + Random
[ea8e919c] + SHA
[9e88b42a] + Serialization
[1a1011a3] + SharedArrays
[6462fe0b] + Sockets
[2f01184e] + SparseArrays
[10745b16] + Statistics
[4607b0f0] + SuiteSparse
[8dfed614] + Test
[cf7118a7] + UUIDs
[4ec0a83e] + Unicode
Building Sundials → `~/.julia/packages/Sundials/MllUG/deps/build.log`
Testing Modia
Status `/tmp/jl_y3XTCY/Manifest.toml`
[4fba245c] ArrayInterface v2.0.0
[b99e7846] BinaryProvider v0.5.8
[324d7699] CategoricalArrays v0.7.3
[34da2185] Compat v2.2.0
[187b0558] ConstructionBase v1.0.0
[9a962f9c] DataAPI v1.1.0
[a93c6f00] DataFrames v0.19.4
[864edb3b] DataStructures v0.17.6
[e2d170a0] DataValueInterfaces v1.0.0
[2b5f629d] DiffEqBase v6.7.0
[01453d9d] DiffEqDiffTools v1.5.0
[ffbed154] DocStringExtensions v0.8.1
[069b7b12] FunctionWrappers v1.0.0
[41ab1584] InvertedIndices v1.0.0
[42fd0dbc] IterativeSolvers v0.8.1
[82899510] IteratorInterfaceExtensions v1.0.0
[682c06a0] JSON v0.21.0
[1914dd2f] MacroTools v0.5.2
[e1d29d7a] Missings v0.4.3
[cb905087] Modia v0.3.0
[67ccffd1] ModiaMath v0.5.2
[46d2c3a1] MuladdMacro v0.2.1
[bac558e1] OrderedCollections v1.1.0
[d96e819e] Parameters v0.12.0
[69de0a69] Parsers v0.3.10
[2dfb63ee] PooledArrays v0.5.2
[3cdcf5f2] RecipesBase v0.7.0
[731186ca] RecursiveArrayTools v1.2.0
[f2c3362d] RecursiveFactorization v0.1.0
[189a3867] Reexport v0.2.0
[ae029012] Requires v0.5.2
[f2b01f46] Roots v0.8.3
[a2af1166] SortingAlgorithms v0.3.1
[90137ffa] StaticArrays v0.12.1
[c3572dad] Sundials v3.8.1
[3783bdb8] TableTraits v1.0.0
[bd369af6] Tables v0.2.11
[a2a6695c] TreeViews v0.3.0
[1986cc42] Unitful v0.18.0
[2a0f44e3] Base64 [`@stdlib/Base64`]
[ade2ca70] Dates [`@stdlib/Dates`]
[8bb1440f] DelimitedFiles [`@stdlib/DelimitedFiles`]
[8ba89e20] Distributed [`@stdlib/Distributed`]
[9fa8497b] Future [`@stdlib/Future`]
[b77e0a4c] InteractiveUtils [`@stdlib/InteractiveUtils`]
[76f85450] LibGit2 [`@stdlib/LibGit2`]
[8f399da3] Libdl [`@stdlib/Libdl`]
[37e2e46d] LinearAlgebra [`@stdlib/LinearAlgebra`]
[56ddb016] Logging [`@stdlib/Logging`]
[d6f4376e] Markdown [`@stdlib/Markdown`]
[a63ad114] Mmap [`@stdlib/Mmap`]
[44cfe95a] Pkg [`@stdlib/Pkg`]
[de0858da] Printf [`@stdlib/Printf`]
[3fa0cd96] REPL [`@stdlib/REPL`]
[9a3f8284] Random [`@stdlib/Random`]
[ea8e919c] SHA [`@stdlib/SHA`]
[9e88b42a] Serialization [`@stdlib/Serialization`]
[1a1011a3] SharedArrays [`@stdlib/SharedArrays`]
[6462fe0b] Sockets [`@stdlib/Sockets`]
[2f01184e] SparseArrays [`@stdlib/SparseArrays`]
[10745b16] Statistics [`@stdlib/Statistics`]
[4607b0f0] SuiteSparse [`@stdlib/SuiteSparse`]
[8dfed614] Test [`@stdlib/Test`]
[cf7118a7] UUIDs [`@stdlib/UUIDs`]
[4ec0a83e] Unicode [`@stdlib/Unicode`]
Welcome to Modia - Dynamic Modeling and Simulation with Julia
Version 0.3.0 (2019-04-07)
Type "?Modia" for help.
Importing ModiaMath Version 0.5.2 (2019-07-10)
PyPlot not available (plot commands will be ignored).
Try to install PyPlot. See hints here:
https://github.com/ModiaSim/ModiaMath.jl/wiki/Installing-PyPlot-in-a-robust-way.
Test match
assign = [8, 1, 2, 7, 4, 5, 3, 0]
Singular system
assign = [0, 3, 1, 0]
(invAssign, unAssignedVariables) = ([3, 0, 2], [1, 4])
(ass, unAssignedEquations) = ([0, 3, 1, 0], [2])
Test Tarjans strong connect
components = Any[Any[6], Any[7, 5, 4, 3], Any[8, 2, 1]]
Fixed-length pendulum
assign = [5, 4, 1, 2, 0, 0, 3, 0, 0]
Assigned original equations:
Test diagnostics for too many equations
Gbig = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 2], [1, 10], [2, 10]]
EGbig = Any[[3, 5, 11], [4, 6, 11], [1, 7, 9, 11], [2, 8, 9, 11], [1, 2, 11], [1, 10, 11], [2, 10, 11], [1, 5, 11], [2, 6, 11], [3, 7, 11], [4, 8, 11]]
componentsBig = Any[Any[5, 10, 3, 4, 11, 2, 9, 7, 6, 8, 1]]
Test diagnostics for too many variables
Gbig = Any[[3, 5], [4, 6], [1, 7, 9], [10, 8, 9], [1, 10]]
EGbig = Any[[3, 5], [4, 6], [1, 7, 9], [10, 8, 9], [1, 10], [1, 5], [2, 6], [3, 7], [4, 8], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]
componentsBig = Any[Any[5, 4, 9, 2, 7, 10, 6, 3, 8, 1]]
Test diagnostics for too few equations
Gbig = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9]]
EGbig = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 5], [2, 6], [3, 7], [4, 8], [1, 2, 3, 4, 5, 6, 7, 8, 9]]
componentsBig = Any[Any[6, 2, 8, 4, 9, 7, 3, 5, 1]]
Check consistency of equations by matching extended equation set
EG = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 2], [1, 5], [2, 6], [3, 7], [4, 8]]
assign = [5, 7, 1, 9, 6, 2, 8, 4, 3]
Perform index reduction
G = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 2], [1, 2, 5, 6], [3, 5, 7, 10], [4, 6, 8, 11], [1, 2, 5, 6, 10, 11]]
assign = [0, 0, 0, 0, 1, 2, 7, 4, 3, 9, 8]
A = [5, 6, 7, 8, 10, 11, 0, 0, 0, 0, 0]
B = [7, 8, 0, 0, 6, 9, 0, 0, 0]
------------------------------------------------------
vActive = Bool[0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]
assign = [0, 5, 0, 2, 1, 6, 7, 4, 3, 9, 8]
components = Any[Any[1], Any[5], Any[6], Any[2], Any[4, 8, 9, 7, 3]]
------------------------------------------------------
All unknowns:
All equations:
Assigned equations:
Sorted equations:
Build augmented system.
AG = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 2], [1, 2, 5, 6], [3, 5, 7, 10], [4, 6, 8, 11], [1, 2, 5, 6, 10, 11], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]]
assignAG = [5, 4, 1, 2, 6, 8, 3, 10, 11, 7, 9]
componentsAG = Any[Any[11, 3, 7, 9, 8, 2, 10, 4, 5, 6, 1]]
Assigned augmented equations:
Sorted augmented equations:
Set initial conditions on x and y. Should fail.
IG1 = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 2], [1, 2, 5, 6], [3, 5, 7, 10], [4, 6, 8, 11], [1, 2, 5, 6, 10, 11], [1], [2]]
assignIG1 = [10, 5, 1, 2, 6, 8, 3, 4, 0, 7, 9]
componentsIG1 = Any[Any[10], Any[5], Any[4], Any[3], Any[7, 9, 2, 8, 6, 1], Any[11]]
Set initial conditions on x and w.
IG2 = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 2], [1, 2, 5, 6], [3, 5, 7, 10], [4, 6, 8, 11], [1, 2, 5, 6, 10, 11], [1], [3]]
assignIG2 = [10, 5, 11, 2, 1, 6, 3, 8, 4, 7, 9]
componentsIG2 = Any[Any[11], Any[1], Any[10], Any[5], Any[6], Any[2], Any[7, 9, 8, 4, 3]]
Sorted IG2 equations:
Set initial conditions on w and z.
IG3 = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 2], [1, 2, 5, 6], [3, 5, 7, 10], [4, 6, 8, 11], [1, 2, 5, 6, 10, 11], [3], [4]]
assignIG3 = [6, 5, 10, 11, 1, 2, 3, 8, 4, 7, 9]
componentsIG3 = Any[Any[10], Any[1], Any[11], Any[2], Any[5, 6], Any[7, 9, 8, 4, 3]]
Sorted IG3 equations:
Fixed-length pendulum
Perform index reduction
Set initial conditions on x and w.
IG = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 2], [1, 2, 5, 6], [3, 5, 7, 10], [4, 6, 8, 11], [1, 2, 5, 6, 10, 11], [1], [3]]
assignIG = [10, 5, 11, 2, 1, 6, 3, 8, 4, 7, 9]
componentsIG = Any[Any[11], Any[1], Any[10], Any[5], Any[6], Any[2], Any[7, 9, 8, 4, 3]]
Exothermic Reactor Model
assign = [0, 0, 1, 7, 3, 2, 8, 6]
A = [3, 4, 7, 0, 8, 0, 0, 0]
B = [6, 0, 7, 5, 8, 0, 0, 0]
components = Any[Any[3], Any[1], Any[8], Any[6], Any[7], Any[2], Any[4], Any[5]]
----------------------
----------------------
Big tests, n = 5000
Big test: diagonal
0.058774 seconds (76.44 k allocations: 51.765 MiB, 20.62% gc time)
Big test: band
0.064841 seconds (108.39 k allocations: 52.149 MiB, 20.58% gc time)
Big test: full, n=1000
1.652966 seconds (4.80 M allocations: 105.997 MiB, 27.93% gc time)
Test solve
Solve: x from: y = x
x = y
Solve: x from: y = x + z
x = y - z
Solve: x from: y = x + z + v + w
x = y - (z + v + w)
Solve: z from: y = x + z + v + w
z = (y - x) - (v + w)
Solve: v from: y = x + z + v + w
v = ((y - x) - z) - w
Solve: w from: y = x + z + v + w
w = ((y - x) - z) - v
Solve: x from: y = x - z
x = y + z
Solve: x from: y = (x - z) - w
x = (y + w) + z
Solve: x from: y = -(x, z, v, w)
x = y + (z + v + w)
Solve: v from: y = -(x, z, v, w)
v = ((x - y) - z) - w
Solve: x from: y = z - x
x = z - y
Solve: x from: y = x * z
x = y / z
Solve: x from: y = x * z * z * z
x = y / (z * z * z)
Solve: x from: y = /(x, z, w)
x = y * (z * w)
Solve: z from: y = /(x, z, w)
z = (x / y) / w
Solve: x from: y = x / z
x = y * z
Solve: z from: y = x / z
z = x / y
Solve: x from: y = x \ z
NOT SOLVED
x \ z = y
----------------------
Test differentiate
Equation: x + 5 + z = w
Differentiated: der(x) + der(z) = der(w)
Equation: der(x) + der(z) = der(w)
Differentiated: der(der(x)) + der(der(z)) = der(der(w))
Equation: +x = w
Differentiated: der(x) = der(w)
Equation: 2 + 3 = w
Differentiated: 0.0 = der(w)
Equation: -x = w
Differentiated: -(der(x)) = der(w)
Equation: (x - 5) - z = w
Differentiated: der(x) - der(z) = der(w)
Equation: 5x = w
Differentiated: 5 * der(x) = der(w)
Equation: x * 5 * z = w
Differentiated: der(x) * 5 * z + x * 5 * der(z) = der(w)
Equation: 4 * 5 * 6 = w
Differentiated: 0.0 = der(w)
Equation: y = x / y
Differentiated: der(y) = der(x) / y + (x / y ^ 2) * der(y)
Equation: y = x / 5
Differentiated: der(y) = der(x) / 5
Equation: y = 5 / y
Differentiated: der(y) = (5 / y ^ 2) * der(y)
Equation: y = [1, x]
Differentiated: der(y) = [0.0, der(x)]
Equation: y = [2x 3x; 4x 5x]
Differentiated: der(y) = [2 * der(x) 3 * der(x); 4 * der(x) 5 * der(x)]
Equation: y = [2x 3x; 4x 5x] * [1, x]
Differentiated: der(y) = [2 * der(x) 3 * der(x); 4 * der(x) 5 * der(x)] * [1, x] + [2x 3x; 4x 5x] * [0.0, der(x)]
Equation: y = transpose(B) + B´
Differentiated: der(y) = transpose(der(B)) + der(B´)
Equation: y = x[5, 6]
Differentiated: der(y) = (der(x))[5, 6]
Equation: y = x[5:7]
Differentiated: der(y) = (der(x))[5:7]
Equation: y = sin(x)
Differentiated: der(y) = cos(x) * der(x)
Equation: y = cos(x)
Differentiated: der(y) = -(sin(x)) * der(x)
Equation: y = tan(x)
Differentiated: der(y) = (1 / cos(x) ^ 2) * der(x)
Equation: y = exp(x)
Differentiated: der(y) = exp(x) * der(x)
Equation: y = x ^ y
Differentiated: der(y) = y * x ^ (y - 1) * der(x) + x ^ y * log(x) * der(y)
Equation: y = log(x)
Differentiated: der(y) = (1 / x) * der(x)
Equation: y = asin(x)
Differentiated: der(y) = (1 / sqrt(1 - x ^ 2)) * der(x)
Equation: y = acos(x)
Differentiated: der(y) = (-1 / sqrt(1 - x ^ 2)) * der(x)
Equation: y = atan(x)
Differentiated: der(y) = (1 / (1 + x ^ 2)) * der(x)
Equation: y = f(x, 5, z)
Derivative function f_der_1 not found.
Derivative function f_der_3 not found.
Differentiated: der(y) = f_der_1(x, 5, z) * der(x) + f_der_3(x, 5, z) * der(z)
Equation: y = f(x, 5, g(z))
Derivative function f_der_1 not found.
Derivative function g_der_1 not found.
Derivative function f_der_3 not found.
Differentiated: der(y) = f_der_1(x, 5, g(z)) * der(x) + f_der_3(x, 5, g(z)) * (g_der_1(z) * der(z))
Equation: y = if true
x
else
y
end
Differentiated: der(y) = if true
der(x)
else
der(y)
end
Equation: y = time
Differentiated: der(y) = 1.0
Equation: y = a * x
Differentiated: der(y) = a * der(x)
----------------------
... Test two coupled inertias (all unknowns can be solved for)
Variables of _x vector (length=2):
_x[1]: J1_phi
_x[2]: der(J1_phi) # = der(_x[1])
Variables of _der_x vector (length=2):
_der_x[1]: --- # = _x[2] = der(J1_phi)
_der_x[2]: der2(J1_phi)
Sorted equations (length(_r) = 2, nc = 0):
_r[1] = _der_x[1] - _x[2]
J2_phi = < solved from eq.5 >
der(J2_phi) = < solved from eq.7 = der(eq.5) >
J1_w = < solved from eq.1 >
J2_w = < solved from eq.3 >
der2(J2_phi) = < solved from eq.10 = der2(eq.5) >
der(J1_w) = < solved from eq.8 = der(eq.1) >
der(J2_w) = < solved from eq.9 = der(eq.3) >
J2_tau = < solved from eq.4 >
J1_tau = < solved from eq.6 >
_r[2] = < residue of eq.2 >
... Test two coupled inertias (only a subset of unknowns can be solved for)
Variables of _x vector (length=2):
_x[1]: J2_phi
_x[2]: der(J2_phi) # = der(_x[1])
Variables of _der_x vector (length=2):
_der_x[1]: --- # = _x[2] = der(J2_phi)
_der_x[2]: der2(J2_phi)
Sorted equations (length(_r) = 2, nc = 0):
_r[1] = _der_x[1] - _x[2]
J1_phi = < solved from eq.5 >
der(J1_phi) = < solved from eq.7 = der(eq.5) >
J1_w = < solved from eq.1 >
J2_w = < solved from eq.3 >
der2(J1_phi) = < solved from eq.10 = der2(eq.5) >
der(J1_w) = < solved from eq.8 = der(eq.1) >
der(J2_w) = < solved from eq.9 = der(eq.3) >
J2_tau = < solved from eq.4 >
J1_tau = < solved from eq.6 >
_r[2] = < residue of eq.2 >
... Test two coupled inertias (no unknowns can be solved for)
Variables of _x vector (length=9):
_x[1]: J1_phi
_x[2]: J2_phi
_x[3]: der(J1_phi) # = der(_x[1])
_x[4]: der(J2_phi) # = der(_x[2])
_x[5]: J2_w
_x[6]: J1_w
_x[7]: --- # integral of lambda variable
_x[8]: --- # integral of lambda variable
_x[9]: --- # integral of mue variable
Variables of _der_x vector (length=9):
_der_x[1]: --- # = _x[3] = der(J1_phi)
_der_x[2]: --- # = _x[4] = der(J2_phi)
_der_x[3]: der2(J1_phi)
_der_x[4]: der2(J2_phi)
_der_x[5]: der(J2_w)
_der_x[6]: der(J1_w)
_der_x[7]: J2_tau # lambda variable
_der_x[8]: J1_tau # lambda variable
_der_x[9]: --- # mue variable associated with equation eq.7 = der(eq.5)
Sorted equations (length(_r) = 9, nc = 4):
_r[1] = _der_x[1] - _x[3]
_r[2] = _der_x[2] - _x[4]
_r[6] = < residue of eq.5 >
_r[7] = < residue of eq.1 >
_r[9] = < residue of eq.7 = der(eq.5) >
_r[8] = < residue of eq.3 >
_r[3] = < residue of eq.4 >
_r[4] = < residue of eq.6 >
_r[5] = < residue of eq.2 >
... Test simple sliding mass model with Tearing
Variables of _x vector (length=3):
_x[1]: s
_x[2]: der(s) # = der(_x[1])
_x[3]: sf
Variables of _der_x vector (length=3):
_der_x[1]: --- # = _x[2] = der(s)
_der_x[2]: der2(s)
_der_x[3]: der(sf)
Sorted equations (length(_r) = 3, nc = 0):
_r[1] = _der_x[1] - _x[2]
r = < solved from eq.1 >
der(r) = < solved from eq.6 = der(eq.1) >
v = < solved from eq.2 >
der2(r) = < solved from eq.7 = der2(eq.1) >
der(v) = < solved from eq.8 = der(eq.2) >
u = < solved from eq.5 >
_r[2] = < residue of eq.9 >
f = < solved from eq.3 >
_r[3] = < residue of eq.4 >
... Test Multi-Index DAE without tearing
Variables of _x vector (length=21):
_x[1]: x7
_x[2]: x6
_x[3]: der(x7) # = der(_x[1])
_x[4]: der(x6) # = der(_x[2])
_x[5]: der2(x7) # = der(_x[3])
_x[6]: der2(x6) # = der(_x[4])
_x[7]: x1
_x[8]: x2
_x[9]: x3
_x[10]: der(x1) # = der(_x[7])
_x[11]: der(x2) # = der(_x[8])
_x[12]: der(x3) # = der(_x[9])
_x[13]: x4
_x[14]: x8 # algebraic variable
_x[15]: --- # integral of lambda variable
_x[16]: --- # integral of mue variable
_x[17]: --- # integral of mue variable
_x[18]: --- # integral of mue variable
_x[19]: --- # integral of mue variable
_x[20]: --- # integral of mue variable
_x[21]: --- # integral of mue variable
Variables of _der_x vector (length=21):
_der_x[1]: --- # = _x[3] = der(x7)
_der_x[2]: --- # = _x[4] = der(x6)
_der_x[3]: --- # = _x[5] = der2(x7)
_der_x[4]: --- # = _x[6] = der2(x6)
_der_x[5]: der3(x7)
_der_x[6]: der3(x6)
_der_x[7]: --- # = _x[10] = der(x1)
_der_x[8]: --- # = _x[11] = der(x2)
_der_x[9]: --- # = _x[12] = der(x3)
_der_x[10]: der2(x1)
_der_x[11]: der2(x2)
_der_x[12]: der2(x3)
_der_x[13]: der(x4)
_der_x[14]: --- # derivative of algebraic variable
_der_x[15]: x5 # lambda variable
_der_x[16]: --- # mue variable associated with equation eq.14 = der(eq.6)
_der_x[17]: --- # mue variable associated with equation eq.15 = der(eq.7)
_der_x[18]: --- # mue variable associated with equation eq.16 = der2(eq.6)
_der_x[19]: --- # mue variable associated with equation eq.17 = der2(eq.7)
_der_x[20]: --- # mue variable associated with equation eq.9 = der(eq.1)
_der_x[21]: --- # mue variable associated with equation eq.11 = der(eq.2)
Sorted equations (length(_r) = 21, nc = 12):
_r[1] = _der_x[1] - _x[3]
_r[2] = _der_x[2] - _x[4]
_r[3] = _der_x[3] - _x[5]
_r[4] = _der_x[4] - _x[6]
_r[5] = _der_x[7] - _x[10]
_r[6] = _der_x[8] - _x[11]
_r[7] = _der_x[9] - _x[12]
_r[11] = < residue of eq.6 >
_r[12] = < residue of eq.7 >
_r[16] = < residue of eq.14 = der(eq.6) >
_r[17] = < residue of eq.15 = der(eq.7) >
_r[18] = < residue of eq.16 = der2(eq.6) >
_r[19] = < residue of eq.17 = der2(eq.7) >
_r[13] = < residue of eq.1 >
_r[14] = < residue of eq.2 >
_r[20] = < residue of eq.9 = der(eq.1) >
_r[21] = < residue of eq.11 = der(eq.2) >
_r[15] = < residue of eq.3 >
_r[10] = < residue of eq.8 >
_r[8] = < residue of eq.4 >
_r[9] = < residue of eq.5 >
... Test Multi-Index DAE WITH tearing
Variables of _x vector (length=8):
_x[1]: x7
_x[2]: der(x7) # = der(_x[1])
_x[3]: der2(x7) # = der(_x[2])
_x[4]: x2
_x[5]: der(x2) # = der(_x[4])
_x[6]: x8 # algebraic variable
_x[7]: --- # integral of mue variable
_x[8]: --- # integral of mue variable
Variables of _der_x vector (length=8):
_der_x[1]: --- # = _x[2] = der(x7)
_der_x[2]: --- # = _x[3] = der2(x7)
_der_x[3]: der3(x7)
_der_x[4]: --- # = _x[5] = der(x2)
_der_x[5]: der2(x2)
_der_x[6]: --- # derivative of algebraic variable
_der_x[7]: --- # mue variable associated with equation eq.15 = der(eq.7)
_der_x[8]: --- # mue variable associated with equation eq.17 = der2(eq.7)
Sorted equations (length(_r) = 8, nc = 4):
_r[1] = _der_x[1] - _x[2]
_r[2] = _der_x[2] - _x[3]
_r[3] = _der_x[4] - _x[5]
x6 = < solved from eq.6 >
_r[6] = < residue of eq.7 >
der(x6) = < solved from eq.14 = der(eq.6) >
_r[7] = < residue of eq.15 = der(eq.7) >
der2(x6) = < solved from eq.16 = der2(eq.6) >
_r[8] = < residue of eq.17 = der2(eq.7) >
x1 = < solved from eq.1 >
x3 = < solved from eq.2 >
der(x1) = < solved from eq.9 = der(eq.1) >
der(x3) = < solved from eq.11 = der(eq.2) >
x4 = < solved from eq.3 >
der3(x6) = < solved from eq.18 = der3(eq.6) >
der2(x1) = < solved from eq.10 = der2(eq.1) >
der2(x3) = < solved from eq.12 = der2(eq.2) >
der(x4) = < solved from eq.13 = der(eq.3) >
x5 = < solved from eq.5 >
_r[5] = < residue of eq.8 >
_r[4] = < residue of eq.4 >
TestVariableTypes: Demonstrating the handling of various variable types
Simulating model: TestVariableTypes1
Number of equations: 9
Number of variables: 11
Number of continuous states: 2
final i = 1
Simulation OK
(result["f"])[end] = 1.0
(result["b"])[end] = true
(result["i"])[end] = 1
(result["s"])[end] = "asdf"
(result["c"])[end] = 2.0 + 3.0im
(result["re"])[end] = 2.0
(result["im"])[end] = 3.0
Simulating model: TestArrays1
Number of equations: 5
Number of variables: 7
Number of continuous states: 2
final i = [1, 2]
Simulation OK
keys(result) = AbstractString["f", "c1", "time", "der(f)", "der(c1)", "b", "s", "i"]
(result["f"])[end, :] = [2.999999999999999, 5.999999999999998, 8.999999999999996]
(result["der(f)"])[end, :] = [2.0, 4.0, 6.0]
(result["b"])[end] = Bool[0, 1]
(result["i"])[end] = [1, 2]
(result["s"])[end] = ["asdf", "qwerty"]
(result["c1"])[end, :] = [2.999999999999999, 5.999999999999998]
(result["der(c1)"])[end, :] = [2.0, 4.0]
storeEliminated = false
Simulating model: TestVariableTypes2
Number of equations: 10
Number of variables: 10
Number of continuous states: 0
Variable(T=Array{Float64,1}; args...) does not work with storeEliminated=true!
logTranslation = true
removeSingularities = false
storeEliminated = false
Log file: /root/ModiaResults/Test.txt
Simulating model: Test
Number of equations: 9
Number of variables: 10
Number of continuous states: 1
Time=0.0: size of u: (10, 10)
Time=0.0: size of u: (10, 10)
Time=0.0: size of u: (10, 10)
Time=0.0: size of u: (10, 10)
Time=0.0: size of u: (10, 10)
Time=0.0: size of u: (10, 10)
Time=5.005005005005005e-6: size of u: (10, 10)
Time=5.005005005005005e-6: size of u: (10, 10)
Time=1.991991991991992: size of u: ()
Time=1.996996996996997: size of u: ()
Time=2.002002002002002: size of u: ()
Time=2.007007007007007: size of u: ()
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Simulating model: Resistor
Number of equations: 6
Number of variables: 6
Number of continuous states: 0
Simulating model: ParallelResistors
Number of equations: 12
Number of variables: 12
Number of continuous states: 0
Simulating model: ParallelCapacitors
Number of equations: 15
Number of variables: 16
Number of continuous states: 1
Number of non states: 2
TestFilter: Tests various features of the symbolic handling.
Simulating model: LPfilter
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
storeEliminated = false
logSimulation = true
Log file: /root/ModiaResults/LPfilter.txt
Simulating model: LPfilter
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: LPfilter
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ C.v │ 0.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 2.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.55 s (init: 0.54 s, integration: 0.0024 s)
startTime = 0.0 s
stopTime = 2.0 s
interval = 0.002002002002002002 s
tolerance = 0.0001
nEquations = 1
nResults = 1000
nSteps = 58
nResidues = 92 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 22
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 1
h0 = 1.3e-07 s
hMin = 1.3e-07 s
hMax = 0.24 s
orderMax = 5
sparseSolver = false
final C.v = 9.996843043929996
Simulation OK
aliasElimination = true
Simulating model: LPfilter
Number of equations: 12
Number of variables: 13
Number of continuous states: 1
logName = "LPfilter aliasElimination"
aliasElimination = true
Simulating model: LPfilter
Number of equations: 12
Number of variables: 13
Number of continuous states: 1
final C.v = 9.996843043929996
Simulation OK
logName = "LPfilter aliasElimination removeSingularities"
aliasElimination = true
removeSingularities = true
Simulating model: LPfilter
Number of equations: 12
Number of variables: 13
Number of continuous states: 1
final C.v = 9.996843043929996
Simulation OK
logName = "LPfilter aliasElimination removeSingularities"
aliasElimination = true
removeSingularities = true
Simulating model: LPfilter
Number of equations: 12
Number of variables: 13
Number of continuous states: 1
removeSingularities = true
Simulating model: LPfilterWithoutGround
Number of equations: 18
Number of variables: 19
Number of continuous states: 1
final C.v = 9.996843043929996
Simulation OK
Simulating model: LPfilterAndSineSource
Number of equations: 20
Number of variables: 22
Number of continuous states: 2
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
removeSingularities = true
Simulating model: HPfilter
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
final C.v = 7.16540372163548
Simulation OK
removeSingularities = true
Simulating model: NewFilter
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
final C.v = 9.999596486913553
Simulation OK
removeSingularities = true
Simulating model: CondFilter
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
final C.v = 3.6787780967777772
Simulation OK
removeSingularities = true
Simulating model: CondFilter2
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
final C.v = 3.6787780967777772
Simulation OK
removeSingularities = true
Simulating model: FilterModels
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
final C.v = 3.6787780967777772
Simulation OK
Simulating model: FilterComponents
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
final C.v = 3.6787780967777772
Simulation OK
Simulating model: TenCoupledFilters
Number of equations: 188
Number of variables: 198
Number of continuous states: 10
final F10.C.v = 1.232726022885834e-5
Simulation OK
aliasElimination = true
Simulating model: TenCoupledFilters
Number of equations: 115
Number of variables: 125
Number of continuous states: 10
final F10.C.v = 1.232726022885834e-5
Simulation OK
TestArrayOfComponents: Demonstrating the handling of arrays of components
Simulating model: TwoFilters
Number of equations: 40
Number of variables: 42
Number of continuous states: 2
final F[2].C.v = 3.2967996078157973
Simulation OK
Simulating model: ManyFilters
Number of equations: 200
Number of variables: 210
Number of continuous states: 10
final F[1].C.v = 9.816758325302478
Simulation OK
Simulating model: ManyDifferentFilters
Number of equations: 200
Number of variables: 210
Number of continuous states: 10
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Simulating model: AdvancedLPfilter
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
Rectifier: Demonstrating conditional components
logTranslation = true
Log file: /root/ModiaResults/ConditionalLoad.txt
Simulating model: ConditionalLoad
Number of equations: 39
Number of variables: 41
Number of continuous states: 2
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Simulating model: NoExtraLoad
Number of equations: 33
Number of variables: 35
Number of continuous states: 2
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Demonstrating conditional equations
logTranslation = true
removeSingularities = false
Log file: /root/ModiaResults/Conditional.txt
Simulating model: Conditional
Conditional equation:
if !steadyState
der(x) + 2x = u
else
0 + 2x = u
end
condition = true
Conditional equation:
if !cond
y = 1
else
y = 2
end
condition = true
Number of equations: 3
Number of variables: 4
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = false
Log file: /root/ModiaResults/ConditionalInstance1.txt
Simulating model: ConditionalInstance1
Conditional equation:
if !steadyState
der(x) + 2x = u
else
0 + 2x = u
end
condition = false
Conditional equation:
if !cond
y = 1
else
y = 2
end
condition = false
Number of equations: 3
Number of variables: 3
Number of continuous states: 0
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = false
Log file: /root/ModiaResults/ConditionalInstance2.txt
Simulating model: ConditionalInstance2
Conditional equation:
if !steadyState
der(x) + 2x = u
else
0 + 2x = u
end
condition = true
Conditional equation:
if !cond
y = 1
else
y = 2
end
condition = true
Number of equations: 3
Number of variables: 4
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = false
removeSingularities = false
Simulating model: Conditional2
Conditional equation:
if !steadyState
der(x) + 2x = u
else
0 + 2x = u
end
condition = true
Conditional equation:
if cond
y = 1
end
condition = false
Number of equations: 2
Number of variables: 3
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Simulating model: SpatialDiscretization
Number of equations: 1
Number of variables: 2
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
storeEliminated = false
Log file: /root/ModiaResults/SpatialDiscretization2.txt
Simulating model: SpatialDiscretization2
Number of equations: 3
Number of variables: 4
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
WARNING: redefining constant n
logTranslation = true
storeEliminated = false
Log file: /root/ModiaResults/SpatialDiscretization4.txt
Simulating model: SpatialDiscretization4
Number of equations: 3
Number of variables: 4
Number of continuous states: 1
1.430767 seconds (976.21 k allocations: 68.517 MiB, 3.28% gc time)
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Demonstrating merging modifiers
logTranslation = true
removeSingularities = false
Log file: /root/ModiaResults/M.txt
Simulating model: M
Number of equations: 1
Number of variables: 2
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = false
Log file: /root/ModiaResults/MInstance.txt
Simulating model: MInstance
Number of equations: 1
Number of variables: 2
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = false
Log file: /root/ModiaResults/MInstance2.txt
Simulating model: MInstance2
Number of equations: 1
Number of variables: 2
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
WARNING: replacing module TestTearing.
TestTearing: Tests tearing algorithm of the symbolic handling.
logTranslation = true
removeSingularities = true
tearing = true
logSimulation = true
Log file: /root/ModiaResults/Tearing1.txt
Simulating model: Tearing1
Number of equations: 3
Number of variables: 4
Number of continuous states: 1
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Tearing1
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ x1 │ 0.0 │ 0 │ 1.0 │
│ 2 │ x2 │ 0.0 │ 0 │ 1.0 │
│ 3 │ x3 │ 1.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
x1 = 0.0 changed to -0.07042253512258778
x2 = 0.0 changed to 0.3802816900817252
Simulation started
Simulation is terminated at time = 1.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.04 s (init: 0.038 s, integration: 0.0023 s)
startTime = 0.0 s
stopTime = 1.0 s
interval = 0.001001001001001001 s
tolerance = 0.0001
nEquations = 3
nResults = 1000
nSteps = 52
nResidues = 168 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 35
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 9.7e-13 s
hMin = 9.7e-13 s
hMax = 0.11 s
orderMax = 3
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
logSimulation = true
Log file: /root/ModiaResults/Tearing1B.txt
Simulating model: Tearing1B
Number of equations: 3
Number of variables: 4
Number of continuous states: 1
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Tearing1B
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ x1 │ 0.0 │ 0 │ 1.0 │
│ 2 │ x3 │ 1.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
x1 = 0.0 changed to 0.2631578952872082
Simulation started
Simulation is terminated at time = 1.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.048 s (init: 0.046 s, integration: 0.0026 s)
startTime = 0.0 s
stopTime = 1.0 s
interval = 0.001001001001001001 s
tolerance = 0.0001
nEquations = 2
nResults = 1000
nSteps = 54
nResidues = 138 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 36
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 1
h0 = 9.8e-13 s
hMin = 9.8e-13 s
hMax = 0.095 s
orderMax = 4
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
logSimulation = true
Log file: /root/ModiaResults/Tearing2.txt
Simulating model: Tearing2
Number of equations: 3
Number of variables: 4
Number of continuous states: 1
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Tearing2
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ x1 │ 0.0 │ 0 │ 1.0 │
│ 2 │ x3 │ 1.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
x1 = 0.0 changed to -2.3941317512897893
Simulation started
Simulation is terminated at time = 0.3 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.03 s (init: 0.028 s, integration: 0.0027 s)
startTime = 0.0 s
stopTime = 0.3 s
interval = 0.0003003003003003003 s
tolerance = 0.0001
nEquations = 2
nResults = 1000
nSteps = 46
nResidues = 122 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 34
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 7.4e-13 s
hMin = 7.4e-13 s
hMax = 0.051 s
orderMax = 3
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
logSimulation = true
Log file: /root/ModiaResults/Tearing3.txt
Simulating model: Tearing3
Number of equations: 4
Number of variables: 5
Number of continuous states: 1
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Tearing3
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ x3 │ 0.0 │ 0 │ 1.0 │
│ 2 │ x4 │ 1.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
x3 = 0.0 changed to 0.2933845009458145
Simulation started
Simulation is terminated at time = 0.3 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.052 s (init: 0.046 s, integration: 0.0059 s)
startTime = 0.0 s
stopTime = 0.3 s
interval = 0.0003003003003003003 s
tolerance = 0.0001
nEquations = 2
nResults = 1000
nSteps = 45
nResidues = 129 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 36
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 9.5e-13 s
hMin = 9.5e-13 s
hMax = 0.065 s
orderMax = 2
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
logSimulation = true
Log file: /root/ModiaResults/Tearing4.txt
Simulating model: Tearing4
Number of equations: 5
Number of variables: 6
Number of continuous states: 1
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Tearing4
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ x3 │ 0.0 │ 0 │ 1.0 │
│ 2 │ x4 │ 0.0 │ 0 │ 1.0 │
│ 3 │ x5 │ 1.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
x3 = 0.0 changed to 0.20486182844879683
x4 = 0.0 changed to 1.4702013267955072
Simulation started
Simulation is terminated at time = 0.3 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.11 s (init: 0.1 s, integration: 0.0074 s)
startTime = 0.0 s
stopTime = 0.3 s
interval = 0.0003003003003003003 s
tolerance = 0.0001
nEquations = 3
nResults = 1000
nSteps = 47
nResidues = 164 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 36
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 7.5e-13 s
hMin = 7.5e-13 s
hMax = 0.046 s
orderMax = 2
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
logSimulation = true
Log file: /root/ModiaResults/TearingCombined.txt
Simulating model: TearingCombined
Number of equations: 14
Number of variables: 18
Number of continuous states: 4
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: TearingCombined
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├────┼────────┼─────────┼───────┼─────────┤
│ 1 │ x1 │ 0.0 │ 0 │ 1.0 │
│ 2 │ x2 │ 0.0 │ 0 │ 1.0 │
│ 3 │ x3 │ 1.0 │ 1 │ 1.0 │
│ 4 │ x11 │ 0.0 │ 0 │ 1.0 │
│ 5 │ x13 │ 1.0 │ 1 │ 1.0 │
│ 6 │ x21 │ 0.0 │ 0 │ 1.0 │
│ 7 │ x23 │ 1.0 │ 1 │ 1.0 │
│ 8 │ x31 │ 0.0 │ 0 │ 1.0 │
│ 9 │ x33 │ 1.0 │ 1 │ 1.0 │
│ 10 │ x35 │ 0.0 │ 0 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
x1 = 0.0 changed to -0.0704225352112676
x2 = 0.0 changed to 0.38028169014084506
x11 = 0.0 changed to 0.26315789473684215
x21 = 0.0 changed to 0.523777476412269
x31 = 0.0 changed to 0.523777476412269
x35 = 0.0 changed to 0.523777476412269
Simulation started
Simulation is terminated at time = 1.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.11 s (init: 0.1 s, integration: 0.0079 s)
startTime = 0.0 s
stopTime = 1.0 s
interval = 0.001001001001001001 s
tolerance = 0.0001
nEquations = 10
nResults = 1000
nSteps = 53
nResidues = 415 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 35
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 8.5e-13 s
hMin = 8.5e-13 s
hMax = 0.11 s
orderMax = 3
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
logSimulation = true
Log file: /root/ModiaResults/Tearing5.txt
Simulating model: Tearing5
Number of equations: 4
Number of variables: 5
Number of continuous states: 1
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Tearing5
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ x2 │ 0.0 │ 0 │ 1.0 │
│ 2 │ x3 │ 0.0 │ 0 │ 1.0 │
│ 3 │ x4 │ 1.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
x2 = 0.0 changed to -0.014925372682878572
x3 = 0.0 changed to -1.044776118048636
Simulation started
Simulation is terminated at time = 0.3 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.034 s (init: 0.031 s, integration: 0.003 s)
startTime = 0.0 s
stopTime = 0.3 s
interval = 0.0003003003003003003 s
tolerance = 0.0001
nEquations = 3
nResults = 1000
nSteps = 45
nResidues = 162 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 36
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 9.4e-13 s
hMin = 9.4e-13 s
hMax = 0.065 s
orderMax = 2
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
logSimulation = true
Log file: /root/ModiaResults/Tearing6.txt
Simulating model: Tearing6
Number of equations: 6
Number of variables: 7
Number of continuous states: 1
Number of non states: 2
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Tearing6
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ u1 │ 1.0 │ 1 │ 1.0 │
│ 2 │ der_u2 │ 0.0 │ 0 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 1.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.032 s (init: 0.031 s, integration: 0.0018 s)
startTime = 0.0 s
stopTime = 1.0 s
interval = 0.001001001001001001 s
tolerance = 0.0001
nEquations = 2
nResults = 1000
nSteps = 21
nResidues = 59 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 19
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 1e-06 s
hMin = 1e-06 s
hMax = 0.48 s
orderMax = 2
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = false
tearing = true
automaticStateSelection = true
logSimulation = true
Log file: /root/ModiaResults/TwoConnectedInertias.txt
Simulating model: TwoConnectedInertias
startValues = Any[1.0, 1.0, 0.0, 0.0]
fixedFlags = Any[false, false, false, false]
names = Any[:w1, :w2, :tau, :t, "der(t)", "der(w1)", "der(w2)"]
Avar = [6, 7, 0, 5, 0, 0, 0]
stateIndices = [2, 4]
Gsolvable = Any[Any[5], Any[3], Any[3], Any[1, 2], Any[]]
stateIndices = [2, 4]
stateNames = ["w2", "t"]
realStates = Any[this.t, this.w1, this.w2]
Number of equations: 5
Number of variables: 7
Number of continuous states: 2
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: TwoConnectedInertias
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ w2 │ 1.0 │ 1 │ 1.0 │
│ 2 │ t │ 0.0 │ 1 │ 1.0 │
│ 3 │ der_w1 │ 0.0 │ 0 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 3.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.031 s (init: 0.029 s, integration: 0.0019 s)
startTime = 0.0 s
stopTime = 3.0 s
interval = 0.003003003003003003 s
tolerance = 0.0001
nEquations = 3
nResults = 1000
nSteps = 37
nResidues = 96 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 16
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 2
h0 = 3e-06 s
hMin = 3e-06 s
hMax = 0.27 s
orderMax = 4
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
automaticStateSelection = true
logSimulation = true
Log file: /root/ModiaResults/TwoInertiasConnectedViaIdealGear.txt
Simulating model: TwoInertiasConnectedViaIdealGear
startValues = Any[1.0, 1.0, 0.0, 0.0]
fixedFlags = Any[false, false, false, false]
names = Any[:w1, :w2, :tau, :t, "der(t)", "der(w1)", "der(w2)"]
Avar = [6, 7, 0, 5, 0, 0, 0]
stateIndices = [2, 4]
Gsolvable = Array{Int64,1}[[5], [], [3], [1], []]
stateIndices = [2, 4]
stateNames = ["w2", "t"]
realStates = Any[this.t, this.w1, this.w2]
Number of equations: 5
Number of variables: 7
Number of continuous states: 2
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: TwoInertiasConnectedViaIdealGear
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ w2 │ 1.0 │ 1 │ 1.0 │
│ 2 │ t │ 0.0 │ 1 │ 1.0 │
│ 3 │ der_w1 │ 0.0 │ 0 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 3.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.038 s (init: 0.036 s, integration: 0.0023 s)
startTime = 0.0 s
stopTime = 3.0 s
interval = 0.003003003003003003 s
tolerance = 0.0001
nEquations = 3
nResults = 1000
nSteps = 36
nResidues = 101 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 17
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 3
h0 = 3e-06 s
hMin = 3e-06 s
hMax = 0.39 s
orderMax = 4
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
automaticStateSelection = false
logSimulation = true
Log file: /root/ModiaResults/ParallelCapacitors1.txt
Simulating model: ParallelCapacitors1
Number of equations: 15
Number of variables: 16
Number of continuous states: 1
Number of non states: 2
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: ParallelCapacitors1
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼──────────┼─────────┼───────┼─────────┤
│ 1 │ C1.v │ 1.0 │ 1 │ 1.0 │
│ 2 │ C2.p.i │ 0.0 │ 0 │ 1.0 │
│ 3 │ der_C2.v │ 0.0 │ 0 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 1.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.043 s (init: 0.04 s, integration: 0.0032 s)
startTime = 0.0 s
stopTime = 1.0 s
interval = 0.001001001001001001 s
tolerance = 0.0001
nEquations = 3
nResults = 1000
nSteps = 21
nResidues = 78 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 19
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 1e-06 s
hMin = 1e-06 s
hMax = 0.48 s
orderMax = 2
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = false
tearing = true
automaticStateSelection = true
logSimulation = true
Log file: /root/ModiaResults/ParallelCapacitors2.txt
Simulating model: ParallelCapacitors2
startValues = Any[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
fixedFlags = Any[false, false, false, false, false, false, false, false, false, false, false, false, false, false]
names = Any[Symbol("C1.v"), Symbol("C1.i"), Symbol("C1.p.v"), Symbol("C1.p.i"), Symbol("C1.n.v"), Symbol("C1.n.i"), Symbol("C2.v"), Symbol("C2.i"), Symbol("C2.p.v"), Symbol("C2.p.i"), Symbol("C2.n.v"), Symbol("C2.n.i"), Symbol("ground.p.v"), Symbol("ground.p.i"), "der(C1.v)", "der(C2.v)"]
Avar = [15, 0, 17, 0, 18, 0, 16, 0, 19, 0, 20, 0, 21, 0, 0, 0, 0, 0, 0, 0, 0]
stateIndices = [9]
Gsolvable = Any[Any[1, 3, 5], Any[4, 6], Any[2, 4], Any[2], Any[7, 9, 11], Any[10, 12], Any[8, 10], Any[8], Any[13], Any[5, 13], Any[5, 11], Any[6, 12, 14], Any[3, 9], Any[4, 10], Any[], Any[], Any[], Any[], Any[], Any[]]
stateIndices = [9]
stateNames = ["C2.p.v"]
realStates = Any[this.C1.v, this.C2.v]
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: ParallelCapacitors2
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────────┼─────────┼───────┼─────────┤
│ 1 │ C2.p.v │ 0.0 │ 1 │ 1.0 │
│ 2 │ der_C1.v │ 0.0 │ 0 │ 1.0 │
│ 3 │ der_C1.p.v │ 0.0 │ 0 │ 1.0 │
│ 4 │ der_C2.v │ 0.0 │ 0 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 1.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.058 s (init: 0.041 s, integration: 0.017 s)
startTime = 0.0 s
stopTime = 1.0 s
interval = 0.001001001001001001 s
tolerance = 0.0001
nEquations = 4
nResults = 1000
nSteps = 21
nResidues = 97 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 19
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 1e-06 s
hMin = 1e-06 s
hMax = 0.48 s
orderMax = 2
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
automaticStateSelection = false
logSimulation = true
Log file: /root/ModiaResults/ParallelCapacitors2b.txt
Simulating model: ParallelCapacitors2b
Number of equations: 6
Number of variables: 7
Number of continuous states: 1
Number of non states: 2
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: ParallelCapacitors2b
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ u1 │ 1.0 │ 1 │ 1.0 │
│ 2 │ der_u2 │ 0.0 │ 0 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 1.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.033 s (init: 0.031 s, integration: 0.0023 s)
startTime = 0.0 s
stopTime = 1.0 s
interval = 0.001001001001001001 s
tolerance = 0.0001
nEquations = 2
nResults = 1000
nSteps = 21
nResidues = 59 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 19
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 1e-06 s
hMin = 1e-06 s
hMax = 0.48 s
orderMax = 2
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = false
tearing = true
automaticStateSelection = true
logSimulation = true
Log file: /root/ModiaResults/TwoInertiasConnectedViaIdealGearWithPositionConstraints.txt
Simulating model: TwoInertiasConnectedViaIdealGearWithPositionConstraints
startValues = Any[1.0, 1.0, 1.0, 1.0, 0.0, 0.0]
fixedFlags = Any[true, false, true, true, false, false]
names = Any[:phi1, :phi2, :w1, :w2, :tau, :t, "der(t)", "der(phi1)", "der(phi2)", "der(w1)", "der(w2)"]
Avar = [8, 9, 10, 11, 0, 7, 0, 12, 13, 0, 0, 0, 0]
stateIndices = [2, 9, 6]
Gsolvable = Any[Any[7], Any[3, 8], Any[4, 9], Any[], Any[5], Any[1], Any[], Any[], Any[], Any[]]
alias = 4
i = 2
stateIndices = [2, 4, 6]
stateNames = ["phi2", "w2", "t"]
realStates = Any[this.t, this.phi1, this.phi2, this.w1, this.w2]
Number of equations: 10
Number of variables: 13
Number of continuous states: 3
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: TwoInertiasConnectedViaIdealGearWithPositionConstraints
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼──────────────┼─────────┼───────┼─────────┤
│ 1 │ phi2 │ 1.0 │ 1 │ 1.0 │
│ 2 │ w2 │ 1.0 │ 1 │ 1.0 │
│ 3 │ t │ 0.0 │ 1 │ 1.0 │
│ 4 │ der_der_phi2 │ 0.0 │ 0 │ 1.0 │
│ 5 │ der_der_phi1 │ 0.0 │ 0 │ 1.0 │
│ 6 │ der_w1 │ 0.0 │ 0 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 3.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.049 s (init: 0.046 s, integration: 0.003 s)
startTime = 0.0 s
stopTime = 3.0 s
interval = 0.003003003003003003 s
tolerance = 0.0001
nEquations = 6
nResults = 1000
nSteps = 37
nResidues = 145 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 16
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 2
h0 = 3e-06 s
hMin = 3e-06 s
hMax = 0.28 s
orderMax = 4
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
after plot
----------------------
Number of simulations OK : 16
Number of simulations NOT OK: 0
Log category statistics:
StaticModel: 3
DynamicModel: 50
CalculatedResult: 16
----------------------
CurrentController: Demonstrating the ability to simulate mixed domain models
removeSingularities = false
tearing = true
Simulating model: CurrentController
Number of equations: 83
Number of variables: 91
Number of continuous states: 8
Number of non states: 2
final load.w = 0.07929150274932795
Simulation OK
Simulating model: CurrentController
Number of equations: 81
Number of variables: 89
Number of continuous states: 8
Number of non states: 3
final load.w = 0.07927277295604353
Simulation OK
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Rectifier: Demonstrating the ability to simulate models with state events
logTranslation = true
Log file: /root/ModiaResults/Rectifier.txt
Simulating model: Rectifier
Number of equations: 33
Number of variables: 35
Number of continuous states: 2
final C.v = 0.47739155081386353
Simulation OK
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
TestCauerLowPassFilter: Demonstrating the ability to simulate an electrical model translated from Modelica Standar Library
logTranslation = false
removeSingularities = false
logTiming = true
Simulating model: CauerLowPassOPV
Instantiate: 16.821184 seconds (14.22 M allocations: 687.805 MiB, 2.74% gc time)
Flatten: 0.006221 seconds (9.43 k allocations: 513.828 KiB)
Consistency check: 0.000145 seconds (436 allocations: 131.125 KiB)
Pantelides: 0.001524 seconds (2.00 k allocations: 244.141 KiB)
Matching: 0.000139 seconds (469 allocations: 155.547 KiB)
Number of equations: 234
Number of variables: 240
Number of continuous states: 6
Number of non states: 4
BLT: 0.000209 seconds (1.13 k allocations: 69.391 KiB)
Symbolic processing: 0.064584 seconds (156.23 k allocations: 8.908 MiB, 19.30% gc time)
Code generation and simulation:
ModiaMath: 0.366672 seconds (741.15 k allocations: 18.291 MiB, 3.78% gc time)
ModiaMath: 0.060760 seconds (606.78 k allocations: 9.957 MiB)
2.421841 seconds (2.15 M allocations: 70.064 MiB, 1.56% gc time)
Total time: 19.499 seconds
final C9.v = -0.5003269853778406
Simulation OK
LinearSystems: Demonstrates type and size deduction.
logTranslation = true
Log file: /root/ModiaResults/MySISOABCD.txt
Simulating model: MySISOABCD
Number of equations: 2
Number of variables: 3
Number of continuous states: 1
final x = 0.5000001198147007
Simulation OK
storeEliminated = false
Simulating model: MyMIMOABCD
Number of equations: 5
Number of variables: 6
Number of continuous states: 1
final x = 0.004778441608750007
Simulation OK
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
SynchronousExamples: Demonstrating the ability to simulate models with synchronous semantics
storeEliminated = false
logSimulation = true
Simulating model: SpeedControl
Number of equations: 6
Number of variables: 9
Number of continuous states: 3
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: SpeedControl
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ x │ 0.0 │ 1 │ 1.0 │
│ 2 │ v │ 0.0 │ 1 │ 1.0 │
│ 3 │ fobs │ 0.0 │ 1 │ 1.0 │
in Clock, nr = 1 (isInitial)
nextEventTime = 0 s, integrateToEvent = true
in sample, nr = 1 (initialize sample store)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Time event at time = 0.0 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.1 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 0.0 changed to 4.9504950495049505
restart = Restart
Time event at time = 0.1 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.2 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 499.99999999999994 changed to 497.53708727326847
restart = Restart
Time event at time = 0.2 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.3 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 251.24581460012237 changed to 250.0327282685336
restart = Restart
Time event at time = 0.30000000000000004 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.4 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 128.7240951096561 changed to 128.1266042418583
restart = Restart
Time event at time = 0.4 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.5 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 68.37751746207857 changed to 68.08325834556372
restart = Restart
Time event at time = 0.5 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.6 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 38.657346694078356 changed to 38.512391609218206
restart = Restart
Time event at time = 0.6 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.7 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 24.016883123203456 changed to 23.945525114776522
restart = Restart
Time event at time = 0.7 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.8 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 16.809724272083102 changed to 16.774558695741685
restart = Restart
Time event at time = 0.7999999999999999 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.9 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 13.258001061599884 changed to 13.240670823454916
restart = Restart
Time event at time = 0.8999999999999999 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 11.507647008958202 changed to 11.499100919972314
restart = Restart
Time event at time = 0.9999999999999999 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.1 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 10.644492021383485 changed to 10.64027421898659
restart = Restart
Time event at time = 1.0999999999999999 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.2 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 10.218493979297065 changed to 10.216413738546947
restart = Restart
Time event at time = 1.2 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.3 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 10.008389663535127 changed to 10.00736376269226
restart = Restart
Time event at time = 1.3 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.4 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 1.4000000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.5 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 1.5000000000000002 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.6 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 1.6000000000000003 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.7 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 1.7000000000000004 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.8 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 1.8000000000000005 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.9 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 1.9000000000000006 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.0000000000000004 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.1 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.1000000000000005 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.2 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.2000000000000006 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.3 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.3000000000000007 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.4 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.400000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.5 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.500000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.6 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.600000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.7 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.700000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.8 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.800000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.9 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.9000000000000012 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.0000000000000013 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.1 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.1000000000000014 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.2 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.2000000000000015 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.3 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.3000000000000016 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.4 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.4000000000000017 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.5 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.5000000000000018 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.6 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.600000000000002 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.7 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.700000000000002 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.8 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.800000000000002 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.9 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.900000000000002 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.000000000000002 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.1 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.100000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.2 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.200000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.3 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.300000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.4 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.4 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.5 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.5 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.6 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.6 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.7 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.699999999999999 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.8 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.799999999999999 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.9 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.899999999999999 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 5 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 5.0 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 5.1 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Simulation is terminated at time = 5.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.12 s (init: 0.081 s, integration: 0.038 s)
startTime = 0.0 s
stopTime = 5.0 s
interval = 0.005005005005005005 s
tolerance = 0.0001
nEquations = 3
nResults = 1100
nSteps = 1408
nResidues = 4419 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 904
nTimeEvents = 51
nStateEvents = 0
nRestartEvents = 51
nErrTestFails = 20
h0 = 8.8e-13 s
hMin = 8.8e-13 s
hMax = 0.049 s
orderMax = 5
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
storeEliminated = false
logSimulation = false
Simulating model: SpeedControlPI
Number of equations: 8
Number of variables: 11
Number of continuous states: 3
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
(result["v"])[end] = 100.28482401529581
ElectricalVehicleAndCharger: Demonstrates the ability to change models from Julia.
Simulating model: Charger
Number of equations: 17
Number of variables: 17
Number of continuous states: 0
Simulating model: ElectricVehicle
Number of equations: 17
Number of variables: 18
Number of continuous states: 1
Simulating model: ElectricalVehicleWithCharger
Super Charger
Number of equations: 34
Number of variables: 35
Number of continuous states: 1
Connecting Electric Vehicle to Charger
logStatistics = false
Super Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Disconnect
logStatistics = false
Super Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Connecting Electric Vehicle to Charger
logStatistics = false
Super Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Disconnect
logStatistics = false
Standard Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Connecting Electric Vehicle to Charger
logStatistics = false
Standard Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Disconnect
logStatistics = false
Standard Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Connecting Electric Vehicle to Charger
logStatistics = false
Standard Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Disconnect
logStatistics = false
Standard Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Connecting Electric Vehicle to Charger
logStatistics = false
Super Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Disconnect
logStatistics = false
Standard Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
CollidingBalls: Demonstrating the use of allInstances to set up contact force between any number of balls
expandArrayIncidence = true
storeEliminated = false
Simulating model: Balls3
Number of equations: 24
Number of variables: 36
Number of continuous states: 12
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Simulating model: HeatTransfer
Number of equations: 1
Number of variables: 2
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
----------------------
Number of simulations OK : 6
Number of simulations NOT OK: 0
Log category statistics:
StaticModel: 1
DynamicModel: 22
CalculatedResult: 6
----------------------
Test Summary: | Pass Total
RunTests | 320 320
Testing Modia tests passed
Results with Julia v1.3.0
Testing was successful .
Last evaluation was ago and took 9 minutes.
Click here to download the log file.
Click here to show the log contents.
Resolving package versions...
Installed SortingAlgorithms ─────────── v0.3.1
Installed Roots ─────────────────────── v0.8.3
Installed Unitful ───────────────────── v0.18.0
Installed DataStructures ────────────── v0.17.6
Installed StaticArrays ──────────────── v0.12.1
Installed Sundials ──────────────────── v3.8.1
Installed Modia ─────────────────────── v0.3.0
Installed ModiaMath ─────────────────── v0.5.2
Installed DocStringExtensions ───────── v0.8.1
Installed BinaryProvider ────────────── v0.5.8
Installed InvertedIndices ───────────── v1.0.0
Installed Compat ────────────────────── v2.2.0
Installed CategoricalArrays ─────────── v0.7.3
Installed Missings ──────────────────── v0.4.3
Installed FunctionWrappers ──────────── v1.0.0
Installed Parsers ───────────────────── v0.3.10
Installed MacroTools ────────────────── v0.5.2
Installed TreeViews ─────────────────── v0.3.0
Installed TableTraits ───────────────── v1.0.0
Installed OrderedCollections ────────── v1.1.0
Installed ConstructionBase ──────────── v1.0.0
Installed JSON ──────────────────────── v0.21.0
Installed DataValueInterfaces ───────── v1.0.0
Installed Parameters ────────────────── v0.12.0
Installed RecipesBase ───────────────── v0.7.0
Installed ArrayInterface ────────────── v2.0.0
Installed DataAPI ───────────────────── v1.1.0
Installed Tables ────────────────────── v0.2.11
Installed DiffEqDiffTools ───────────── v1.5.0
Installed DiffEqBase ────────────────── v6.7.0
Installed Requires ──────────────────── v0.5.2
Installed IteratorInterfaceExtensions ─ v1.0.0
Installed PooledArrays ──────────────── v0.5.2
Installed MuladdMacro ───────────────── v0.2.1
Installed RecursiveArrayTools ───────── v1.2.0
Installed RecursiveFactorization ────── v0.1.0
Installed Reexport ──────────────────── v0.2.0
Installed DataFrames ────────────────── v0.19.4
Installed IterativeSolvers ──────────── v0.8.1
Updating `~/.julia/environments/v1.3/Project.toml`
[cb905087] + Modia v0.3.0
Updating `~/.julia/environments/v1.3/Manifest.toml`
[4fba245c] + ArrayInterface v2.0.0
[b99e7846] + BinaryProvider v0.5.8
[324d7699] + CategoricalArrays v0.7.3
[34da2185] + Compat v2.2.0
[187b0558] + ConstructionBase v1.0.0
[9a962f9c] + DataAPI v1.1.0
[a93c6f00] + DataFrames v0.19.4
[864edb3b] + DataStructures v0.17.6
[e2d170a0] + DataValueInterfaces v1.0.0
[2b5f629d] + DiffEqBase v6.7.0
[01453d9d] + DiffEqDiffTools v1.5.0
[ffbed154] + DocStringExtensions v0.8.1
[069b7b12] + FunctionWrappers v1.0.0
[41ab1584] + InvertedIndices v1.0.0
[42fd0dbc] + IterativeSolvers v0.8.1
[82899510] + IteratorInterfaceExtensions v1.0.0
[682c06a0] + JSON v0.21.0
[1914dd2f] + MacroTools v0.5.2
[e1d29d7a] + Missings v0.4.3
[cb905087] + Modia v0.3.0
[67ccffd1] + ModiaMath v0.5.2
[46d2c3a1] + MuladdMacro v0.2.1
[bac558e1] + OrderedCollections v1.1.0
[d96e819e] + Parameters v0.12.0
[69de0a69] + Parsers v0.3.10
[2dfb63ee] + PooledArrays v0.5.2
[3cdcf5f2] + RecipesBase v0.7.0
[731186ca] + RecursiveArrayTools v1.2.0
[f2c3362d] + RecursiveFactorization v0.1.0
[189a3867] + Reexport v0.2.0
[ae029012] + Requires v0.5.2
[f2b01f46] + Roots v0.8.3
[a2af1166] + SortingAlgorithms v0.3.1
[90137ffa] + StaticArrays v0.12.1
[c3572dad] + Sundials v3.8.1
[3783bdb8] + TableTraits v1.0.0
[bd369af6] + Tables v0.2.11
[a2a6695c] + TreeViews v0.3.0
[1986cc42] + Unitful v0.18.0
[2a0f44e3] + Base64
[ade2ca70] + Dates
[8bb1440f] + DelimitedFiles
[8ba89e20] + Distributed
[9fa8497b] + Future
[b77e0a4c] + InteractiveUtils
[76f85450] + LibGit2
[8f399da3] + Libdl
[37e2e46d] + LinearAlgebra
[56ddb016] + Logging
[d6f4376e] + Markdown
[a63ad114] + Mmap
[44cfe95a] + Pkg
[de0858da] + Printf
[3fa0cd96] + REPL
[9a3f8284] + Random
[ea8e919c] + SHA
[9e88b42a] + Serialization
[1a1011a3] + SharedArrays
[6462fe0b] + Sockets
[2f01184e] + SparseArrays
[10745b16] + Statistics
[4607b0f0] + SuiteSparse
[8dfed614] + Test
[cf7118a7] + UUIDs
[4ec0a83e] + Unicode
Building Sundials → `~/.julia/packages/Sundials/MllUG/deps/build.log`
Testing Modia
Status `/tmp/jl_8HVf8l/Manifest.toml`
[4fba245c] ArrayInterface v2.0.0
[b99e7846] BinaryProvider v0.5.8
[324d7699] CategoricalArrays v0.7.3
[34da2185] Compat v2.2.0
[187b0558] ConstructionBase v1.0.0
[9a962f9c] DataAPI v1.1.0
[a93c6f00] DataFrames v0.19.4
[864edb3b] DataStructures v0.17.6
[e2d170a0] DataValueInterfaces v1.0.0
[2b5f629d] DiffEqBase v6.7.0
[01453d9d] DiffEqDiffTools v1.5.0
[ffbed154] DocStringExtensions v0.8.1
[069b7b12] FunctionWrappers v1.0.0
[41ab1584] InvertedIndices v1.0.0
[42fd0dbc] IterativeSolvers v0.8.1
[82899510] IteratorInterfaceExtensions v1.0.0
[682c06a0] JSON v0.21.0
[1914dd2f] MacroTools v0.5.2
[e1d29d7a] Missings v0.4.3
[cb905087] Modia v0.3.0
[67ccffd1] ModiaMath v0.5.2
[46d2c3a1] MuladdMacro v0.2.1
[bac558e1] OrderedCollections v1.1.0
[d96e819e] Parameters v0.12.0
[69de0a69] Parsers v0.3.10
[2dfb63ee] PooledArrays v0.5.2
[3cdcf5f2] RecipesBase v0.7.0
[731186ca] RecursiveArrayTools v1.2.0
[f2c3362d] RecursiveFactorization v0.1.0
[189a3867] Reexport v0.2.0
[ae029012] Requires v0.5.2
[f2b01f46] Roots v0.8.3
[a2af1166] SortingAlgorithms v0.3.1
[90137ffa] StaticArrays v0.12.1
[c3572dad] Sundials v3.8.1
[3783bdb8] TableTraits v1.0.0
[bd369af6] Tables v0.2.11
[a2a6695c] TreeViews v0.3.0
[1986cc42] Unitful v0.18.0
[2a0f44e3] Base64 [`@stdlib/Base64`]
[ade2ca70] Dates [`@stdlib/Dates`]
[8bb1440f] DelimitedFiles [`@stdlib/DelimitedFiles`]
[8ba89e20] Distributed [`@stdlib/Distributed`]
[9fa8497b] Future [`@stdlib/Future`]
[b77e0a4c] InteractiveUtils [`@stdlib/InteractiveUtils`]
[76f85450] LibGit2 [`@stdlib/LibGit2`]
[8f399da3] Libdl [`@stdlib/Libdl`]
[37e2e46d] LinearAlgebra [`@stdlib/LinearAlgebra`]
[56ddb016] Logging [`@stdlib/Logging`]
[d6f4376e] Markdown [`@stdlib/Markdown`]
[a63ad114] Mmap [`@stdlib/Mmap`]
[44cfe95a] Pkg [`@stdlib/Pkg`]
[de0858da] Printf [`@stdlib/Printf`]
[3fa0cd96] REPL [`@stdlib/REPL`]
[9a3f8284] Random [`@stdlib/Random`]
[ea8e919c] SHA [`@stdlib/SHA`]
[9e88b42a] Serialization [`@stdlib/Serialization`]
[1a1011a3] SharedArrays [`@stdlib/SharedArrays`]
[6462fe0b] Sockets [`@stdlib/Sockets`]
[2f01184e] SparseArrays [`@stdlib/SparseArrays`]
[10745b16] Statistics [`@stdlib/Statistics`]
[4607b0f0] SuiteSparse [`@stdlib/SuiteSparse`]
[8dfed614] Test [`@stdlib/Test`]
[cf7118a7] UUIDs [`@stdlib/UUIDs`]
[4ec0a83e] Unicode [`@stdlib/Unicode`]
Welcome to Modia - Dynamic Modeling and Simulation with Julia
Version 0.3.0 (2019-04-07)
Type "?Modia" for help.
Importing ModiaMath Version 0.5.2 (2019-07-10)
PyPlot not available (plot commands will be ignored).
Try to install PyPlot. See hints here:
https://github.com/ModiaSim/ModiaMath.jl/wiki/Installing-PyPlot-in-a-robust-way.
Test match
assign = [8, 1, 2, 7, 4, 5, 3, 0]
Singular system
assign = [0, 3, 1, 0]
(invAssign, unAssignedVariables) = ([3, 0, 2], [1, 4])
(ass, unAssignedEquations) = ([0, 3, 1, 0], [2])
Test Tarjans strong connect
components = Any[Any[6], Any[7, 5, 4, 3], Any[8, 2, 1]]
Fixed-length pendulum
assign = [5, 4, 1, 2, 0, 0, 3, 0, 0]
Assigned original equations:
Test diagnostics for too many equations
Gbig = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 2], [1, 10], [2, 10]]
EGbig = Any[[3, 5, 11], [4, 6, 11], [1, 7, 9, 11], [2, 8, 9, 11], [1, 2, 11], [1, 10, 11], [2, 10, 11], [1, 5, 11], [2, 6, 11], [3, 7, 11], [4, 8, 11]]
componentsBig = Any[Any[5, 10, 3, 4, 11, 2, 9, 7, 6, 8, 1]]
Test diagnostics for too many variables
Gbig = Any[[3, 5], [4, 6], [1, 7, 9], [10, 8, 9], [1, 10]]
EGbig = Any[[3, 5], [4, 6], [1, 7, 9], [10, 8, 9], [1, 10], [1, 5], [2, 6], [3, 7], [4, 8], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]
componentsBig = Any[Any[5, 4, 9, 2, 7, 10, 6, 3, 8, 1]]
Test diagnostics for too few equations
Gbig = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9]]
EGbig = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 5], [2, 6], [3, 7], [4, 8], [1, 2, 3, 4, 5, 6, 7, 8, 9]]
componentsBig = Any[Any[6, 2, 8, 4, 9, 7, 3, 5, 1]]
Check consistency of equations by matching extended equation set
EG = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 2], [1, 5], [2, 6], [3, 7], [4, 8]]
assign = [5, 7, 1, 9, 6, 2, 8, 4, 3]
Perform index reduction
G = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 2], [1, 2, 5, 6], [3, 5, 7, 10], [4, 6, 8, 11], [1, 2, 5, 6, 10, 11]]
assign = [0, 0, 0, 0, 1, 2, 7, 4, 3, 9, 8]
A = [5, 6, 7, 8, 10, 11, 0, 0, 0, 0, 0]
B = [7, 8, 0, 0, 6, 9, 0, 0, 0]
------------------------------------------------------
vActive = Bool[0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]
assign = [0, 5, 0, 2, 1, 6, 7, 4, 3, 9, 8]
components = Any[Any[1], Any[5], Any[6], Any[2], Any[4, 8, 9, 7, 3]]
------------------------------------------------------
All unknowns:
All equations:
Assigned equations:
Sorted equations:
Build augmented system.
AG = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 2], [1, 2, 5, 6], [3, 5, 7, 10], [4, 6, 8, 11], [1, 2, 5, 6, 10, 11], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]]
assignAG = [5, 4, 1, 2, 6, 8, 3, 10, 11, 7, 9]
componentsAG = Any[Any[11, 3, 7, 9, 8, 2, 10, 4, 5, 6, 1]]
Assigned augmented equations:
Sorted augmented equations:
Set initial conditions on x and y. Should fail.
IG1 = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 2], [1, 2, 5, 6], [3, 5, 7, 10], [4, 6, 8, 11], [1, 2, 5, 6, 10, 11], [1], [2]]
assignIG1 = [10, 5, 1, 2, 6, 8, 3, 4, 0, 7, 9]
componentsIG1 = Any[Any[10], Any[5], Any[4], Any[3], Any[7, 9, 2, 8, 6, 1], Any[11]]
Set initial conditions on x and w.
IG2 = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 2], [1, 2, 5, 6], [3, 5, 7, 10], [4, 6, 8, 11], [1, 2, 5, 6, 10, 11], [1], [3]]
assignIG2 = [10, 5, 11, 2, 1, 6, 3, 8, 4, 7, 9]
componentsIG2 = Any[Any[11], Any[1], Any[10], Any[5], Any[6], Any[2], Any[7, 9, 8, 4, 3]]
Sorted IG2 equations:
Set initial conditions on w and z.
IG3 = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 2], [1, 2, 5, 6], [3, 5, 7, 10], [4, 6, 8, 11], [1, 2, 5, 6, 10, 11], [3], [4]]
assignIG3 = [6, 5, 10, 11, 1, 2, 3, 8, 4, 7, 9]
componentsIG3 = Any[Any[10], Any[1], Any[11], Any[2], Any[5, 6], Any[7, 9, 8, 4, 3]]
Sorted IG3 equations:
Fixed-length pendulum
Perform index reduction
Set initial conditions on x and w.
IG = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 2], [1, 2, 5, 6], [3, 5, 7, 10], [4, 6, 8, 11], [1, 2, 5, 6, 10, 11], [1], [3]]
assignIG = [10, 5, 11, 2, 1, 6, 3, 8, 4, 7, 9]
componentsIG = Any[Any[11], Any[1], Any[10], Any[5], Any[6], Any[2], Any[7, 9, 8, 4, 3]]
Exothermic Reactor Model
assign = [0, 0, 1, 7, 3, 2, 8, 6]
A = [3, 4, 7, 0, 8, 0, 0, 0]
B = [6, 0, 7, 5, 8, 0, 0, 0]
components = Any[Any[3], Any[1], Any[8], Any[6], Any[7], Any[2], Any[4], Any[5]]
----------------------
----------------------
Big tests, n = 5000
Big test: diagonal
0.057786 seconds (76.44 k allocations: 51.765 MiB, 21.07% gc time)
Big test: band
0.046707 seconds (108.39 k allocations: 52.149 MiB, 13.83% gc time)
Big test: full, n=1000
0.940906 seconds (4.80 M allocations: 105.997 MiB, 3.41% gc time)
Test solve
Solve: x from: y = x
x = y
Solve: x from: y = x + z
x = y - z
Solve: x from: y = x + z + v + w
x = y - (z + v + w)
Solve: z from: y = x + z + v + w
z = (y - x) - (v + w)
Solve: v from: y = x + z + v + w
v = ((y - x) - z) - w
Solve: w from: y = x + z + v + w
w = ((y - x) - z) - v
Solve: x from: y = x - z
x = y + z
Solve: x from: y = (x - z) - w
x = (y + w) + z
Solve: x from: y = -(x, z, v, w)
x = y + (z + v + w)
Solve: v from: y = -(x, z, v, w)
v = ((x - y) - z) - w
Solve: x from: y = z - x
x = z - y
Solve: x from: y = x * z
x = y / z
Solve: x from: y = x * z * z * z
x = y / (z * z * z)
Solve: x from: y = /(x, z, w)
x = y * (z * w)
Solve: z from: y = /(x, z, w)
z = (x / y) / w
Solve: x from: y = x / z
x = y * z
Solve: z from: y = x / z
z = x / y
Solve: x from: y = x \ z
NOT SOLVED
x \ z = y
----------------------
Test differentiate
Equation: x + 5 + z = w
Differentiated: der(x) + der(z) = der(w)
Equation: der(x) + der(z) = der(w)
Differentiated: der(der(x)) + der(der(z)) = der(der(w))
Equation: +x = w
Differentiated: der(x) = der(w)
Equation: 2 + 3 = w
Differentiated: 0.0 = der(w)
Equation: -x = w
Differentiated: -(der(x)) = der(w)
Equation: (x - 5) - z = w
Differentiated: der(x) - der(z) = der(w)
Equation: 5x = w
Differentiated: 5 * der(x) = der(w)
Equation: x * 5 * z = w
Differentiated: der(x) * 5 * z + x * 5 * der(z) = der(w)
Equation: 4 * 5 * 6 = w
Differentiated: 0.0 = der(w)
Equation: y = x / y
Differentiated: der(y) = der(x) / y + (x / y ^ 2) * der(y)
Equation: y = x / 5
Differentiated: der(y) = der(x) / 5
Equation: y = 5 / y
Differentiated: der(y) = (5 / y ^ 2) * der(y)
Equation: y = [1, x]
Differentiated: der(y) = [0.0, der(x)]
Equation: y = [2x 3x; 4x 5x]
Differentiated: der(y) = [2 * der(x) 3 * der(x); 4 * der(x) 5 * der(x)]
Equation: y = [2x 3x; 4x 5x] * [1, x]
Differentiated: der(y) = [2 * der(x) 3 * der(x); 4 * der(x) 5 * der(x)] * [1, x] + [2x 3x; 4x 5x] * [0.0, der(x)]
Equation: y = transpose(B) + B´
Differentiated: der(y) = transpose(der(B)) + der(B´)
Equation: y = x[5, 6]
Differentiated: der(y) = (der(x))[5, 6]
Equation: y = x[5:7]
Differentiated: der(y) = (der(x))[5:7]
Equation: y = sin(x)
Differentiated: der(y) = cos(x) * der(x)
Equation: y = cos(x)
Differentiated: der(y) = -(sin(x)) * der(x)
Equation: y = tan(x)
Differentiated: der(y) = (1 / cos(x) ^ 2) * der(x)
Equation: y = exp(x)
Differentiated: der(y) = exp(x) * der(x)
Equation: y = x ^ y
Differentiated: der(y) = y * x ^ (y - 1) * der(x) + x ^ y * log(x) * der(y)
Equation: y = log(x)
Differentiated: der(y) = (1 / x) * der(x)
Equation: y = asin(x)
Differentiated: der(y) = (1 / sqrt(1 - x ^ 2)) * der(x)
Equation: y = acos(x)
Differentiated: der(y) = (-1 / sqrt(1 - x ^ 2)) * der(x)
Equation: y = atan(x)
Differentiated: der(y) = (1 / (1 + x ^ 2)) * der(x)
Equation: y = f(x, 5, z)
Derivative function f_der_1 not found.
Derivative function f_der_3 not found.
Differentiated: der(y) = f_der_1(x, 5, z) * der(x) + f_der_3(x, 5, z) * der(z)
Equation: y = f(x, 5, g(z))
Derivative function f_der_1 not found.
Derivative function g_der_1 not found.
Derivative function f_der_3 not found.
Differentiated: der(y) = f_der_1(x, 5, g(z)) * der(x) + f_der_3(x, 5, g(z)) * (g_der_1(z) * der(z))
Equation: y = if true
x
else
y
end
Differentiated: der(y) = if true
der(x)
else
der(y)
end
Equation: y = time
Differentiated: der(y) = 1.0
Equation: y = a * x
Differentiated: der(y) = a * der(x)
----------------------
... Test two coupled inertias (all unknowns can be solved for)
Variables of _x vector (length=2):
_x[1]: J1_phi
_x[2]: der(J1_phi) # = der(_x[1])
Variables of _der_x vector (length=2):
_der_x[1]: --- # = _x[2] = der(J1_phi)
_der_x[2]: der2(J1_phi)
Sorted equations (length(_r) = 2, nc = 0):
_r[1] = _der_x[1] - _x[2]
J2_phi = < solved from eq.5 >
der(J2_phi) = < solved from eq.7 = der(eq.5) >
J1_w = < solved from eq.1 >
J2_w = < solved from eq.3 >
der2(J2_phi) = < solved from eq.10 = der2(eq.5) >
der(J1_w) = < solved from eq.8 = der(eq.1) >
der(J2_w) = < solved from eq.9 = der(eq.3) >
J2_tau = < solved from eq.4 >
J1_tau = < solved from eq.6 >
_r[2] = < residue of eq.2 >
... Test two coupled inertias (only a subset of unknowns can be solved for)
Variables of _x vector (length=2):
_x[1]: J2_phi
_x[2]: der(J2_phi) # = der(_x[1])
Variables of _der_x vector (length=2):
_der_x[1]: --- # = _x[2] = der(J2_phi)
_der_x[2]: der2(J2_phi)
Sorted equations (length(_r) = 2, nc = 0):
_r[1] = _der_x[1] - _x[2]
J1_phi = < solved from eq.5 >
der(J1_phi) = < solved from eq.7 = der(eq.5) >
J1_w = < solved from eq.1 >
J2_w = < solved from eq.3 >
der2(J1_phi) = < solved from eq.10 = der2(eq.5) >
der(J1_w) = < solved from eq.8 = der(eq.1) >
der(J2_w) = < solved from eq.9 = der(eq.3) >
J2_tau = < solved from eq.4 >
J1_tau = < solved from eq.6 >
_r[2] = < residue of eq.2 >
... Test two coupled inertias (no unknowns can be solved for)
Variables of _x vector (length=9):
_x[1]: J1_phi
_x[2]: J2_phi
_x[3]: der(J1_phi) # = der(_x[1])
_x[4]: der(J2_phi) # = der(_x[2])
_x[5]: J2_w
_x[6]: J1_w
_x[7]: --- # integral of lambda variable
_x[8]: --- # integral of lambda variable
_x[9]: --- # integral of mue variable
Variables of _der_x vector (length=9):
_der_x[1]: --- # = _x[3] = der(J1_phi)
_der_x[2]: --- # = _x[4] = der(J2_phi)
_der_x[3]: der2(J1_phi)
_der_x[4]: der2(J2_phi)
_der_x[5]: der(J2_w)
_der_x[6]: der(J1_w)
_der_x[7]: J2_tau # lambda variable
_der_x[8]: J1_tau # lambda variable
_der_x[9]: --- # mue variable associated with equation eq.7 = der(eq.5)
Sorted equations (length(_r) = 9, nc = 4):
_r[1] = _der_x[1] - _x[3]
_r[2] = _der_x[2] - _x[4]
_r[6] = < residue of eq.5 >
_r[7] = < residue of eq.1 >
_r[9] = < residue of eq.7 = der(eq.5) >
_r[8] = < residue of eq.3 >
_r[3] = < residue of eq.4 >
_r[4] = < residue of eq.6 >
_r[5] = < residue of eq.2 >
... Test simple sliding mass model with Tearing
Variables of _x vector (length=3):
_x[1]: s
_x[2]: der(s) # = der(_x[1])
_x[3]: sf
Variables of _der_x vector (length=3):
_der_x[1]: --- # = _x[2] = der(s)
_der_x[2]: der2(s)
_der_x[3]: der(sf)
Sorted equations (length(_r) = 3, nc = 0):
_r[1] = _der_x[1] - _x[2]
r = < solved from eq.1 >
der(r) = < solved from eq.6 = der(eq.1) >
v = < solved from eq.2 >
der2(r) = < solved from eq.7 = der2(eq.1) >
der(v) = < solved from eq.8 = der(eq.2) >
u = < solved from eq.5 >
_r[2] = < residue of eq.9 >
f = < solved from eq.3 >
_r[3] = < residue of eq.4 >
... Test Multi-Index DAE without tearing
Variables of _x vector (length=21):
_x[1]: x7
_x[2]: x6
_x[3]: der(x7) # = der(_x[1])
_x[4]: der(x6) # = der(_x[2])
_x[5]: der2(x7) # = der(_x[3])
_x[6]: der2(x6) # = der(_x[4])
_x[7]: x1
_x[8]: x2
_x[9]: x3
_x[10]: der(x1) # = der(_x[7])
_x[11]: der(x2) # = der(_x[8])
_x[12]: der(x3) # = der(_x[9])
_x[13]: x4
_x[14]: x8 # algebraic variable
_x[15]: --- # integral of lambda variable
_x[16]: --- # integral of mue variable
_x[17]: --- # integral of mue variable
_x[18]: --- # integral of mue variable
_x[19]: --- # integral of mue variable
_x[20]: --- # integral of mue variable
_x[21]: --- # integral of mue variable
Variables of _der_x vector (length=21):
_der_x[1]: --- # = _x[3] = der(x7)
_der_x[2]: --- # = _x[4] = der(x6)
_der_x[3]: --- # = _x[5] = der2(x7)
_der_x[4]: --- # = _x[6] = der2(x6)
_der_x[5]: der3(x7)
_der_x[6]: der3(x6)
_der_x[7]: --- # = _x[10] = der(x1)
_der_x[8]: --- # = _x[11] = der(x2)
_der_x[9]: --- # = _x[12] = der(x3)
_der_x[10]: der2(x1)
_der_x[11]: der2(x2)
_der_x[12]: der2(x3)
_der_x[13]: der(x4)
_der_x[14]: --- # derivative of algebraic variable
_der_x[15]: x5 # lambda variable
_der_x[16]: --- # mue variable associated with equation eq.14 = der(eq.6)
_der_x[17]: --- # mue variable associated with equation eq.15 = der(eq.7)
_der_x[18]: --- # mue variable associated with equation eq.16 = der2(eq.6)
_der_x[19]: --- # mue variable associated with equation eq.17 = der2(eq.7)
_der_x[20]: --- # mue variable associated with equation eq.9 = der(eq.1)
_der_x[21]: --- # mue variable associated with equation eq.11 = der(eq.2)
Sorted equations (length(_r) = 21, nc = 12):
_r[1] = _der_x[1] - _x[3]
_r[2] = _der_x[2] - _x[4]
_r[3] = _der_x[3] - _x[5]
_r[4] = _der_x[4] - _x[6]
_r[5] = _der_x[7] - _x[10]
_r[6] = _der_x[8] - _x[11]
_r[7] = _der_x[9] - _x[12]
_r[11] = < residue of eq.6 >
_r[12] = < residue of eq.7 >
_r[16] = < residue of eq.14 = der(eq.6) >
_r[17] = < residue of eq.15 = der(eq.7) >
_r[18] = < residue of eq.16 = der2(eq.6) >
_r[19] = < residue of eq.17 = der2(eq.7) >
_r[13] = < residue of eq.1 >
_r[14] = < residue of eq.2 >
_r[20] = < residue of eq.9 = der(eq.1) >
_r[21] = < residue of eq.11 = der(eq.2) >
_r[15] = < residue of eq.3 >
_r[10] = < residue of eq.8 >
_r[8] = < residue of eq.4 >
_r[9] = < residue of eq.5 >
... Test Multi-Index DAE WITH tearing
Variables of _x vector (length=8):
_x[1]: x7
_x[2]: der(x7) # = der(_x[1])
_x[3]: der2(x7) # = der(_x[2])
_x[4]: x2
_x[5]: der(x2) # = der(_x[4])
_x[6]: x8 # algebraic variable
_x[7]: --- # integral of mue variable
_x[8]: --- # integral of mue variable
Variables of _der_x vector (length=8):
_der_x[1]: --- # = _x[2] = der(x7)
_der_x[2]: --- # = _x[3] = der2(x7)
_der_x[3]: der3(x7)
_der_x[4]: --- # = _x[5] = der(x2)
_der_x[5]: der2(x2)
_der_x[6]: --- # derivative of algebraic variable
_der_x[7]: --- # mue variable associated with equation eq.15 = der(eq.7)
_der_x[8]: --- # mue variable associated with equation eq.17 = der2(eq.7)
Sorted equations (length(_r) = 8, nc = 4):
_r[1] = _der_x[1] - _x[2]
_r[2] = _der_x[2] - _x[3]
_r[3] = _der_x[4] - _x[5]
x6 = < solved from eq.6 >
_r[6] = < residue of eq.7 >
der(x6) = < solved from eq.14 = der(eq.6) >
_r[7] = < residue of eq.15 = der(eq.7) >
der2(x6) = < solved from eq.16 = der2(eq.6) >
_r[8] = < residue of eq.17 = der2(eq.7) >
x1 = < solved from eq.1 >
x3 = < solved from eq.2 >
der(x1) = < solved from eq.9 = der(eq.1) >
der(x3) = < solved from eq.11 = der(eq.2) >
x4 = < solved from eq.3 >
der3(x6) = < solved from eq.18 = der3(eq.6) >
der2(x1) = < solved from eq.10 = der2(eq.1) >
der2(x3) = < solved from eq.12 = der2(eq.2) >
der(x4) = < solved from eq.13 = der(eq.3) >
x5 = < solved from eq.5 >
_r[5] = < residue of eq.8 >
_r[4] = < residue of eq.4 >
TestVariableTypes: Demonstrating the handling of various variable types
Simulating model: TestVariableTypes1
Number of equations: 9
Number of variables: 11
Number of continuous states: 2
final i = 1
Simulation OK
(result["f"])[end] = 1.0
(result["b"])[end] = true
(result["i"])[end] = 1
(result["s"])[end] = "asdf"
(result["c"])[end] = 2.0 + 3.0im
(result["re"])[end] = 2.0
(result["im"])[end] = 3.0
Simulating model: TestArrays1
Number of equations: 5
Number of variables: 7
Number of continuous states: 2
final i = [1, 2]
Simulation OK
keys(result) = AbstractString["f", "c1", "time", "der(f)", "der(c1)", "b", "s", "i"]
(result["f"])[end, :] = [2.999999999999999, 5.999999999999998, 8.999999999999996]
(result["der(f)"])[end, :] = [2.0, 4.0, 6.0]
(result["b"])[end] = Bool[0, 1]
(result["i"])[end] = [1, 2]
(result["s"])[end] = ["asdf", "qwerty"]
(result["c1"])[end, :] = [2.999999999999999, 5.999999999999998]
(result["der(c1)"])[end, :] = [2.0, 4.0]
storeEliminated = false
Simulating model: TestVariableTypes2
Number of equations: 10
Number of variables: 10
Number of continuous states: 0
Variable(T=Array{Float64,1}; args...) does not work with storeEliminated=true!
logTranslation = true
removeSingularities = false
storeEliminated = false
Log file: /root/ModiaResults/Test.txt
Simulating model: Test
Number of equations: 9
Number of variables: 10
Number of continuous states: 1
Time=0.0: size of u: (10, 10)
Time=0.0: size of u: (10, 10)
Time=0.0: size of u: (10, 10)
Time=0.0: size of u: (10, 10)
Time=0.0: size of u: (10, 10)
Time=0.0: size of u: (10, 10)
Time=5.005005005005005e-6: size of u: (10, 10)
Time=5.005005005005005e-6: size of u: (10, 10)
Time=1.991991991991992: size of u: ()
Time=1.996996996996997: size of u: ()
Time=2.002002002002002: size of u: ()
Time=2.007007007007007: size of u: ()
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Simulating model: Resistor
Number of equations: 6
Number of variables: 6
Number of continuous states: 0
Simulating model: ParallelResistors
Number of equations: 12
Number of variables: 12
Number of continuous states: 0
Simulating model: ParallelCapacitors
Number of equations: 15
Number of variables: 16
Number of continuous states: 1
Number of non states: 2
TestFilter: Tests various features of the symbolic handling.
Simulating model: LPfilter
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
storeEliminated = false
logSimulation = true
Log file: /root/ModiaResults/LPfilter.txt
Simulating model: LPfilter
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: LPfilter
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ C.v │ 0.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 2.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.53 s (init: 0.53 s, integration: 0.0023 s)
startTime = 0.0 s
stopTime = 2.0 s
interval = 0.002002002002002002 s
tolerance = 0.0001
nEquations = 1
nResults = 1000
nSteps = 58
nResidues = 92 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 22
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 1
h0 = 1.3e-07 s
hMin = 1.3e-07 s
hMax = 0.24 s
orderMax = 5
sparseSolver = false
final C.v = 9.996843043929996
Simulation OK
aliasElimination = true
Simulating model: LPfilter
Number of equations: 12
Number of variables: 13
Number of continuous states: 1
logName = "LPfilter aliasElimination"
aliasElimination = true
Simulating model: LPfilter
Number of equations: 12
Number of variables: 13
Number of continuous states: 1
final C.v = 9.996843043929996
Simulation OK
logName = "LPfilter aliasElimination removeSingularities"
aliasElimination = true
removeSingularities = true
Simulating model: LPfilter
Number of equations: 12
Number of variables: 13
Number of continuous states: 1
final C.v = 9.996843043929996
Simulation OK
logName = "LPfilter aliasElimination removeSingularities"
aliasElimination = true
removeSingularities = true
Simulating model: LPfilter
Number of equations: 12
Number of variables: 13
Number of continuous states: 1
removeSingularities = true
Simulating model: LPfilterWithoutGround
Number of equations: 18
Number of variables: 19
Number of continuous states: 1
final C.v = 9.996843043929996
Simulation OK
Simulating model: LPfilterAndSineSource
Number of equations: 20
Number of variables: 22
Number of continuous states: 2
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
removeSingularities = true
Simulating model: HPfilter
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
final C.v = 7.16540372163548
Simulation OK
removeSingularities = true
Simulating model: NewFilter
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
final C.v = 9.999596486913553
Simulation OK
removeSingularities = true
Simulating model: CondFilter
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
final C.v = 3.6787780967777772
Simulation OK
removeSingularities = true
Simulating model: CondFilter2
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
final C.v = 3.6787780967777772
Simulation OK
removeSingularities = true
Simulating model: FilterModels
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
final C.v = 3.6787780967777772
Simulation OK
Simulating model: FilterComponents
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
final C.v = 3.6787780967777772
Simulation OK
Simulating model: TenCoupledFilters
Number of equations: 188
Number of variables: 198
Number of continuous states: 10
final F10.C.v = 1.232726022885834e-5
Simulation OK
aliasElimination = true
Simulating model: TenCoupledFilters
Number of equations: 115
Number of variables: 125
Number of continuous states: 10
final F10.C.v = 1.232726022885834e-5
Simulation OK
TestArrayOfComponents: Demonstrating the handling of arrays of components
Simulating model: TwoFilters
Number of equations: 40
Number of variables: 42
Number of continuous states: 2
final F[2].C.v = 3.2967996078157973
Simulation OK
Simulating model: ManyFilters
Number of equations: 200
Number of variables: 210
Number of continuous states: 10
final F[1].C.v = 9.816758325302478
Simulation OK
Simulating model: ManyDifferentFilters
Number of equations: 200
Number of variables: 210
Number of continuous states: 10
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Simulating model: AdvancedLPfilter
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
Rectifier: Demonstrating conditional components
logTranslation = true
Log file: /root/ModiaResults/ConditionalLoad.txt
Simulating model: ConditionalLoad
Number of equations: 39
Number of variables: 41
Number of continuous states: 2
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Simulating model: NoExtraLoad
Number of equations: 33
Number of variables: 35
Number of continuous states: 2
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Demonstrating conditional equations
logTranslation = true
removeSingularities = false
Log file: /root/ModiaResults/Conditional.txt
Simulating model: Conditional
Conditional equation:
if !steadyState
var"der(x)" + 2x = u
else
0 + 2x = u
end
condition = true
Conditional equation:
if !cond
y = 1
else
y = 2
end
condition = true
Number of equations: 3
Number of variables: 4
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = false
Log file: /root/ModiaResults/ConditionalInstance1.txt
Simulating model: ConditionalInstance1
Conditional equation:
if !steadyState
var"der(x)" + 2x = u
else
0 + 2x = u
end
condition = false
Conditional equation:
if !cond
y = 1
else
y = 2
end
condition = false
Number of equations: 3
Number of variables: 3
Number of continuous states: 0
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = false
Log file: /root/ModiaResults/ConditionalInstance2.txt
Simulating model: ConditionalInstance2
Conditional equation:
if !steadyState
var"der(x)" + 2x = u
else
0 + 2x = u
end
condition = true
Conditional equation:
if !cond
y = 1
else
y = 2
end
condition = true
Number of equations: 3
Number of variables: 4
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = false
removeSingularities = false
Simulating model: Conditional2
Conditional equation:
if !steadyState
var"der(x)" + 2x = u
else
0 + 2x = u
end
condition = true
Conditional equation:
if cond
y = 1
end
condition = false
Number of equations: 2
Number of variables: 3
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Simulating model: SpatialDiscretization
Number of equations: 1
Number of variables: 2
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
storeEliminated = false
Log file: /root/ModiaResults/SpatialDiscretization2.txt
Simulating model: SpatialDiscretization2
Number of equations: 3
Number of variables: 4
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
WARNING: redefining constant n
logTranslation = true
storeEliminated = false
Log file: /root/ModiaResults/SpatialDiscretization4.txt
Simulating model: SpatialDiscretization4
Number of equations: 3
Number of variables: 4
Number of continuous states: 1
0.968839 seconds (611.02 k allocations: 49.264 MiB, 1.59% gc time)
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Demonstrating merging modifiers
logTranslation = true
removeSingularities = false
Log file: /root/ModiaResults/M.txt
Simulating model: M
Number of equations: 1
Number of variables: 2
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = false
Log file: /root/ModiaResults/MInstance.txt
Simulating model: MInstance
Number of equations: 1
Number of variables: 2
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = false
Log file: /root/ModiaResults/MInstance2.txt
Simulating model: MInstance2
Number of equations: 1
Number of variables: 2
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
WARNING: replacing module TestTearing.
TestTearing: Tests tearing algorithm of the symbolic handling.
logTranslation = true
removeSingularities = true
tearing = true
logSimulation = true
Log file: /root/ModiaResults/Tearing1.txt
Simulating model: Tearing1
Number of equations: 3
Number of variables: 4
Number of continuous states: 1
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Tearing1
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ x1 │ 0.0 │ 0 │ 1.0 │
│ 2 │ x2 │ 0.0 │ 0 │ 1.0 │
│ 3 │ x3 │ 1.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
x1 = 0.0 changed to -0.07042253512258778
x2 = 0.0 changed to 0.3802816900817252
Simulation started
Simulation is terminated at time = 1.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.032 s (init: 0.029 s, integration: 0.0025 s)
startTime = 0.0 s
stopTime = 1.0 s
interval = 0.001001001001001001 s
tolerance = 0.0001
nEquations = 3
nResults = 1000
nSteps = 52
nResidues = 168 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 35
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 9.7e-13 s
hMin = 9.7e-13 s
hMax = 0.11 s
orderMax = 3
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
logSimulation = true
Log file: /root/ModiaResults/Tearing1B.txt
Simulating model: Tearing1B
Number of equations: 3
Number of variables: 4
Number of continuous states: 1
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Tearing1B
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ x1 │ 0.0 │ 0 │ 1.0 │
│ 2 │ x3 │ 1.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
x1 = 0.0 changed to 0.2631578952872082
Simulation started
Simulation is terminated at time = 1.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.025 s (init: 0.023 s, integration: 0.0023 s)
startTime = 0.0 s
stopTime = 1.0 s
interval = 0.001001001001001001 s
tolerance = 0.0001
nEquations = 2
nResults = 1000
nSteps = 54
nResidues = 138 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 36
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 1
h0 = 9.8e-13 s
hMin = 9.8e-13 s
hMax = 0.095 s
orderMax = 4
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
logSimulation = true
Log file: /root/ModiaResults/Tearing2.txt
Simulating model: Tearing2
Number of equations: 3
Number of variables: 4
Number of continuous states: 1
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Tearing2
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ x1 │ 0.0 │ 0 │ 1.0 │
│ 2 │ x3 │ 1.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
x1 = 0.0 changed to -2.3941317512897893
Simulation started
Simulation is terminated at time = 0.3 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.028 s (init: 0.026 s, integration: 0.0027 s)
startTime = 0.0 s
stopTime = 0.3 s
interval = 0.0003003003003003003 s
tolerance = 0.0001
nEquations = 2
nResults = 1000
nSteps = 46
nResidues = 122 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 34
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 7.4e-13 s
hMin = 7.4e-13 s
hMax = 0.051 s
orderMax = 3
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
logSimulation = true
Log file: /root/ModiaResults/Tearing3.txt
Simulating model: Tearing3
Number of equations: 4
Number of variables: 5
Number of continuous states: 1
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Tearing3
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ x3 │ 0.0 │ 0 │ 1.0 │
│ 2 │ x4 │ 1.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
x3 = 0.0 changed to 0.2933845009458145
Simulation started
Simulation is terminated at time = 0.3 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.035 s (init: 0.031 s, integration: 0.0039 s)
startTime = 0.0 s
stopTime = 0.3 s
interval = 0.0003003003003003003 s
tolerance = 0.0001
nEquations = 2
nResults = 1000
nSteps = 45
nResidues = 129 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 36
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 9.5e-13 s
hMin = 9.5e-13 s
hMax = 0.065 s
orderMax = 2
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
logSimulation = true
Log file: /root/ModiaResults/Tearing4.txt
Simulating model: Tearing4
Number of equations: 5
Number of variables: 6
Number of continuous states: 1
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Tearing4
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ x3 │ 0.0 │ 0 │ 1.0 │
│ 2 │ x4 │ 0.0 │ 0 │ 1.0 │
│ 3 │ x5 │ 1.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
x3 = 0.0 changed to 0.20486182844879683
x4 = 0.0 changed to 1.4702013267955072
Simulation started
Simulation is terminated at time = 0.3 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.047 s (init: 0.042 s, integration: 0.005 s)
startTime = 0.0 s
stopTime = 0.3 s
interval = 0.0003003003003003003 s
tolerance = 0.0001
nEquations = 3
nResults = 1000
nSteps = 47
nResidues = 164 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 36
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 7.5e-13 s
hMin = 7.5e-13 s
hMax = 0.046 s
orderMax = 2
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
logSimulation = true
Log file: /root/ModiaResults/TearingCombined.txt
Simulating model: TearingCombined
Number of equations: 14
Number of variables: 18
Number of continuous states: 4
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: TearingCombined
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├────┼────────┼─────────┼───────┼─────────┤
│ 1 │ x1 │ 0.0 │ 0 │ 1.0 │
│ 2 │ x2 │ 0.0 │ 0 │ 1.0 │
│ 3 │ x3 │ 1.0 │ 1 │ 1.0 │
│ 4 │ x11 │ 0.0 │ 0 │ 1.0 │
│ 5 │ x13 │ 1.0 │ 1 │ 1.0 │
│ 6 │ x21 │ 0.0 │ 0 │ 1.0 │
│ 7 │ x23 │ 1.0 │ 1 │ 1.0 │
│ 8 │ x31 │ 0.0 │ 0 │ 1.0 │
│ 9 │ x33 │ 1.0 │ 1 │ 1.0 │
│ 10 │ x35 │ 0.0 │ 0 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
x1 = 0.0 changed to -0.0704225352112676
x2 = 0.0 changed to 0.38028169014084506
x11 = 0.0 changed to 0.26315789473684215
x21 = 0.0 changed to 0.523777476412269
x31 = 0.0 changed to 0.523777476412269
x35 = 0.0 changed to 0.523777476412269
Simulation started
Simulation is terminated at time = 1.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.09 s (init: 0.081 s, integration: 0.0089 s)
startTime = 0.0 s
stopTime = 1.0 s
interval = 0.001001001001001001 s
tolerance = 0.0001
nEquations = 10
nResults = 1000
nSteps = 53
nResidues = 415 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 35
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 8.5e-13 s
hMin = 8.5e-13 s
hMax = 0.11 s
orderMax = 3
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
logSimulation = true
Log file: /root/ModiaResults/Tearing5.txt
Simulating model: Tearing5
Number of equations: 4
Number of variables: 5
Number of continuous states: 1
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Tearing5
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ x2 │ 0.0 │ 0 │ 1.0 │
│ 2 │ x3 │ 0.0 │ 0 │ 1.0 │
│ 3 │ x4 │ 1.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
x2 = 0.0 changed to -0.014925372682878572
x3 = 0.0 changed to -1.044776118048636
Simulation started
Simulation is terminated at time = 0.3 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.039 s (init: 0.035 s, integration: 0.0036 s)
startTime = 0.0 s
stopTime = 0.3 s
interval = 0.0003003003003003003 s
tolerance = 0.0001
nEquations = 3
nResults = 1000
nSteps = 45
nResidues = 162 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 36
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 9.4e-13 s
hMin = 9.4e-13 s
hMax = 0.065 s
orderMax = 2
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
logSimulation = true
Log file: /root/ModiaResults/Tearing6.txt
Simulating model: Tearing6
Number of equations: 6
Number of variables: 7
Number of continuous states: 1
Number of non states: 2
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Tearing6
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ u1 │ 1.0 │ 1 │ 1.0 │
│ 2 │ der_u2 │ 0.0 │ 0 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 1.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.028 s (init: 0.026 s, integration: 0.0024 s)
startTime = 0.0 s
stopTime = 1.0 s
interval = 0.001001001001001001 s
tolerance = 0.0001
nEquations = 2
nResults = 1000
nSteps = 21
nResidues = 59 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 19
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 1e-06 s
hMin = 1e-06 s
hMax = 0.48 s
orderMax = 2
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = false
tearing = true
automaticStateSelection = true
logSimulation = true
Log file: /root/ModiaResults/TwoConnectedInertias.txt
Simulating model: TwoConnectedInertias
startValues = Any[1.0, 1.0, 0.0, 0.0]
fixedFlags = Any[false, false, false, false]
names = Any[:w1, :w2, :tau, :t, "der(t)", "der(w1)", "der(w2)"]
Avar = [6, 7, 0, 5, 0, 0, 0]
stateIndices = [2, 4]
Gsolvable = Any[Any[5], Any[3], Any[3], Any[1, 2], Any[]]
stateIndices = [2, 4]
stateNames = ["w2", "t"]
realStates = Any[this.t, this.w1, this.w2]
Number of equations: 5
Number of variables: 7
Number of continuous states: 2
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: TwoConnectedInertias
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ w2 │ 1.0 │ 1 │ 1.0 │
│ 2 │ t │ 0.0 │ 1 │ 1.0 │
│ 3 │ der_w1 │ 0.0 │ 0 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 3.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.028 s (init: 0.026 s, integration: 0.0018 s)
startTime = 0.0 s
stopTime = 3.0 s
interval = 0.003003003003003003 s
tolerance = 0.0001
nEquations = 3
nResults = 1000
nSteps = 37
nResidues = 96 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 16
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 2
h0 = 3e-06 s
hMin = 3e-06 s
hMax = 0.27 s
orderMax = 4
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
automaticStateSelection = true
logSimulation = true
Log file: /root/ModiaResults/TwoInertiasConnectedViaIdealGear.txt
Simulating model: TwoInertiasConnectedViaIdealGear
startValues = Any[1.0, 1.0, 0.0, 0.0]
fixedFlags = Any[false, false, false, false]
names = Any[:w1, :w2, :tau, :t, "der(t)", "der(w1)", "der(w2)"]
Avar = [6, 7, 0, 5, 0, 0, 0]
stateIndices = [2, 4]
Gsolvable = Array{Int64,1}[[5], [], [3], [1], []]
stateIndices = [2, 4]
stateNames = ["w2", "t"]
realStates = Any[this.t, this.w1, this.w2]
Number of equations: 5
Number of variables: 7
Number of continuous states: 2
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: TwoInertiasConnectedViaIdealGear
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ w2 │ 1.0 │ 1 │ 1.0 │
│ 2 │ t │ 0.0 │ 1 │ 1.0 │
│ 3 │ der_w1 │ 0.0 │ 0 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 3.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.032 s (init: 0.03 s, integration: 0.0022 s)
startTime = 0.0 s
stopTime = 3.0 s
interval = 0.003003003003003003 s
tolerance = 0.0001
nEquations = 3
nResults = 1000
nSteps = 36
nResidues = 101 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 17
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 3
h0 = 3e-06 s
hMin = 3e-06 s
hMax = 0.39 s
orderMax = 4
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
automaticStateSelection = false
logSimulation = true
Log file: /root/ModiaResults/ParallelCapacitors1.txt
Simulating model: ParallelCapacitors1
Number of equations: 15
Number of variables: 16
Number of continuous states: 1
Number of non states: 2
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: ParallelCapacitors1
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼──────────┼─────────┼───────┼─────────┤
│ 1 │ C1.v │ 1.0 │ 1 │ 1.0 │
│ 2 │ C2.p.i │ 0.0 │ 0 │ 1.0 │
│ 3 │ der_C2.v │ 0.0 │ 0 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 1.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.038 s (init: 0.035 s, integration: 0.0031 s)
startTime = 0.0 s
stopTime = 1.0 s
interval = 0.001001001001001001 s
tolerance = 0.0001
nEquations = 3
nResults = 1000
nSteps = 21
nResidues = 78 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 19
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 1e-06 s
hMin = 1e-06 s
hMax = 0.48 s
orderMax = 2
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = false
tearing = true
automaticStateSelection = true
logSimulation = true
Log file: /root/ModiaResults/ParallelCapacitors2.txt
Simulating model: ParallelCapacitors2
startValues = Any[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
fixedFlags = Any[false, false, false, false, false, false, false, false, false, false, false, false, false, false]
names = Any[Symbol("C1.v"), Symbol("C1.i"), Symbol("C1.p.v"), Symbol("C1.p.i"), Symbol("C1.n.v"), Symbol("C1.n.i"), Symbol("C2.v"), Symbol("C2.i"), Symbol("C2.p.v"), Symbol("C2.p.i"), Symbol("C2.n.v"), Symbol("C2.n.i"), Symbol("ground.p.v"), Symbol("ground.p.i"), "der(C1.v)", "der(C2.v)"]
Avar = [15, 0, 17, 0, 18, 0, 16, 0, 19, 0, 20, 0, 21, 0, 0, 0, 0, 0, 0, 0, 0]
stateIndices = [9]
Gsolvable = Any[Any[1, 3, 5], Any[4, 6], Any[2, 4], Any[2], Any[7, 9, 11], Any[10, 12], Any[8, 10], Any[8], Any[13], Any[5, 13], Any[5, 11], Any[6, 12, 14], Any[3, 9], Any[4, 10], Any[], Any[], Any[], Any[], Any[], Any[]]
stateIndices = [9]
stateNames = ["C2.p.v"]
realStates = Any[this.C1.v, this.C2.v]
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: ParallelCapacitors2
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────────┼─────────┼───────┼─────────┤
│ 1 │ C2.p.v │ 0.0 │ 1 │ 1.0 │
│ 2 │ der_C1.v │ 0.0 │ 0 │ 1.0 │
│ 3 │ der_C1.p.v │ 0.0 │ 0 │ 1.0 │
│ 4 │ der_C2.v │ 0.0 │ 0 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 1.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.04 s (init: 0.037 s, integration: 0.003 s)
startTime = 0.0 s
stopTime = 1.0 s
interval = 0.001001001001001001 s
tolerance = 0.0001
nEquations = 4
nResults = 1000
nSteps = 21
nResidues = 97 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 19
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 1e-06 s
hMin = 1e-06 s
hMax = 0.48 s
orderMax = 2
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
automaticStateSelection = false
logSimulation = true
Log file: /root/ModiaResults/ParallelCapacitors2b.txt
Simulating model: ParallelCapacitors2b
Number of equations: 6
Number of variables: 7
Number of continuous states: 1
Number of non states: 2
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: ParallelCapacitors2b
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ u1 │ 1.0 │ 1 │ 1.0 │
│ 2 │ der_u2 │ 0.0 │ 0 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 1.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.028 s (init: 0.026 s, integration: 0.0022 s)
startTime = 0.0 s
stopTime = 1.0 s
interval = 0.001001001001001001 s
tolerance = 0.0001
nEquations = 2
nResults = 1000
nSteps = 21
nResidues = 59 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 19
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 1e-06 s
hMin = 1e-06 s
hMax = 0.48 s
orderMax = 2
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = false
tearing = true
automaticStateSelection = true
logSimulation = true
Log file: /root/ModiaResults/TwoInertiasConnectedViaIdealGearWithPositionConstraints.txt
Simulating model: TwoInertiasConnectedViaIdealGearWithPositionConstraints
startValues = Any[1.0, 1.0, 1.0, 1.0, 0.0, 0.0]
fixedFlags = Any[true, false, true, true, false, false]
names = Any[:phi1, :phi2, :w1, :w2, :tau, :t, "der(t)", "der(phi1)", "der(phi2)", "der(w1)", "der(w2)"]
Avar = [8, 9, 10, 11, 0, 7, 0, 12, 13, 0, 0, 0, 0]
stateIndices = [2, 9, 6]
Gsolvable = Any[Any[7], Any[3, 8], Any[4, 9], Any[], Any[5], Any[1], Any[], Any[], Any[], Any[]]
alias = 4
i = 2
stateIndices = [2, 4, 6]
stateNames = ["phi2", "w2", "t"]
realStates = Any[this.t, this.phi1, this.phi2, this.w1, this.w2]
Number of equations: 10
Number of variables: 13
Number of continuous states: 3
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: TwoInertiasConnectedViaIdealGearWithPositionConstraints
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼──────────────┼─────────┼───────┼─────────┤
│ 1 │ phi2 │ 1.0 │ 1 │ 1.0 │
│ 2 │ w2 │ 1.0 │ 1 │ 1.0 │
│ 3 │ t │ 0.0 │ 1 │ 1.0 │
│ 4 │ der_der_phi2 │ 0.0 │ 0 │ 1.0 │
│ 5 │ der_der_phi1 │ 0.0 │ 0 │ 1.0 │
│ 6 │ der_w1 │ 0.0 │ 0 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 3.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.043 s (init: 0.04 s, integration: 0.003 s)
startTime = 0.0 s
stopTime = 3.0 s
interval = 0.003003003003003003 s
tolerance = 0.0001
nEquations = 6
nResults = 1000
nSteps = 37
nResidues = 145 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 16
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 2
h0 = 3e-06 s
hMin = 3e-06 s
hMax = 0.28 s
orderMax = 4
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
after plot
----------------------
Number of simulations OK : 16
Number of simulations NOT OK: 0
Log category statistics:
StaticModel: 3
DynamicModel: 50
CalculatedResult: 16
----------------------
CurrentController: Demonstrating the ability to simulate mixed domain models
removeSingularities = false
tearing = true
Simulating model: CurrentController
Number of equations: 83
Number of variables: 91
Number of continuous states: 8
Number of non states: 2
final load.w = 0.07929150274932795
Simulation OK
Simulating model: CurrentController
Number of equations: 81
Number of variables: 89
Number of continuous states: 8
Number of non states: 3
final load.w = 0.07927277295604353
Simulation OK
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Rectifier: Demonstrating the ability to simulate models with state events
logTranslation = true
Log file: /root/ModiaResults/Rectifier.txt
Simulating model: Rectifier
Number of equations: 33
Number of variables: 35
Number of continuous states: 2
final C.v = 0.47739155081386353
Simulation OK
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
TestCauerLowPassFilter: Demonstrating the ability to simulate an electrical model translated from Modelica Standar Library
logTranslation = false
removeSingularities = false
logTiming = true
Simulating model: CauerLowPassOPV
Instantiate: 15.090897 seconds (14.40 M allocations: 693.483 MiB, 2.29% gc time)
Flatten: 0.011144 seconds (9.43 k allocations: 513.828 KiB)
Consistency check: 0.000158 seconds (436 allocations: 131.125 KiB)
Pantelides: 0.001479 seconds (2.00 k allocations: 244.141 KiB)
Matching: 0.000088 seconds (469 allocations: 155.547 KiB)
Number of equations: 234
Number of variables: 240
Number of continuous states: 6
Number of non states: 4
BLT: 0.000125 seconds (1.13 k allocations: 69.391 KiB)
Symbolic processing: 0.057118 seconds (153.18 k allocations: 8.812 MiB)
Code generation and simulation:
ModiaMath: 0.331737 seconds (710.35 k allocations: 16.400 MiB)
ModiaMath: 0.052076 seconds (606.78 k allocations: 9.957 MiB)
2.382531 seconds (2.10 M allocations: 66.753 MiB, 1.33% gc time)
Total time: 17.740 seconds
final C9.v = -0.5003269853778406
Simulation OK
LinearSystems: Demonstrates type and size deduction.
logTranslation = true
Log file: /root/ModiaResults/MySISOABCD.txt
Simulating model: MySISOABCD
Number of equations: 2
Number of variables: 3
Number of continuous states: 1
final x = 0.5000001198147007
Simulation OK
storeEliminated = false
Simulating model: MyMIMOABCD
Number of equations: 5
Number of variables: 6
Number of continuous states: 1
final x = 0.004778441608750007
Simulation OK
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
SynchronousExamples: Demonstrating the ability to simulate models with synchronous semantics
storeEliminated = false
logSimulation = true
Simulating model: SpeedControl
Number of equations: 6
Number of variables: 9
Number of continuous states: 3
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: SpeedControl
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ x │ 0.0 │ 1 │ 1.0 │
│ 2 │ v │ 0.0 │ 1 │ 1.0 │
│ 3 │ fobs │ 0.0 │ 1 │ 1.0 │
in Clock, nr = 1 (isInitial)
nextEventTime = 0 s, integrateToEvent = true
in sample, nr = 1 (initialize sample store)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Time event at time = 0.0 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.1 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 0.0 changed to 4.9504950495049505
restart = Restart
Time event at time = 0.1 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.2 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 499.99999999999994 changed to 497.53708727326847
restart = Restart
Time event at time = 0.2 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.3 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 251.24581460012237 changed to 250.0327282685336
restart = Restart
Time event at time = 0.30000000000000004 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.4 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 128.7240951096561 changed to 128.1266042418583
restart = Restart
Time event at time = 0.4 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.5 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 68.37751746207857 changed to 68.08325834556372
restart = Restart
Time event at time = 0.5 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.6 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 38.657346694078356 changed to 38.512391609218206
restart = Restart
Time event at time = 0.6 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.7 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 24.016883123203456 changed to 23.945525114776522
restart = Restart
Time event at time = 0.7 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.8 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 16.809724272083102 changed to 16.774558695741685
restart = Restart
Time event at time = 0.7999999999999999 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.9 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 13.258001061599884 changed to 13.240670823454916
restart = Restart
Time event at time = 0.8999999999999999 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 11.507647008958202 changed to 11.499100919972314
restart = Restart
Time event at time = 0.9999999999999999 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.1 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 10.644492021383485 changed to 10.64027421898659
restart = Restart
Time event at time = 1.0999999999999999 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.2 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 10.218493979297065 changed to 10.216413738546947
restart = Restart
Time event at time = 1.2 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.3 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 10.008389663535127 changed to 10.00736376269226
restart = Restart
Time event at time = 1.3 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.4 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 1.4000000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.5 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 1.5000000000000002 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.6 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 1.6000000000000003 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.7 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 1.7000000000000004 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.8 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 1.8000000000000005 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.9 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 1.9000000000000006 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.0000000000000004 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.1 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.1000000000000005 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.2 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.2000000000000006 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.3 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.3000000000000007 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.4 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.400000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.5 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.500000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.6 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.600000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.7 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.700000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.8 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.800000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.9 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.9000000000000012 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.0000000000000013 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.1 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.1000000000000014 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.2 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.2000000000000015 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.3 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.3000000000000016 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.4 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.4000000000000017 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.5 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.5000000000000018 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.6 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.600000000000002 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.7 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.700000000000002 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.8 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.800000000000002 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.9 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.900000000000002 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.000000000000002 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.1 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.100000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.2 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.200000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.3 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.300000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.4 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.4 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.5 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.5 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.6 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.6 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.7 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.699999999999999 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.8 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.799999999999999 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.9 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.899999999999999 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 5 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 5.0 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 5.1 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Simulation is terminated at time = 5.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.19 s (init: 0.085 s, integration: 0.1 s)
startTime = 0.0 s
stopTime = 5.0 s
interval = 0.005005005005005005 s
tolerance = 0.0001
nEquations = 3
nResults = 1100
nSteps = 1408
nResidues = 4419 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 904
nTimeEvents = 51
nStateEvents = 0
nRestartEvents = 51
nErrTestFails = 20
h0 = 8.8e-13 s
hMin = 8.8e-13 s
hMax = 0.049 s
orderMax = 5
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
storeEliminated = false
logSimulation = false
Simulating model: SpeedControlPI
Number of equations: 8
Number of variables: 11
Number of continuous states: 3
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
(result["v"])[end] = 100.28482401529581
ElectricalVehicleAndCharger: Demonstrates the ability to change models from Julia.
Simulating model: Charger
Number of equations: 17
Number of variables: 17
Number of continuous states: 0
Simulating model: ElectricVehicle
Number of equations: 17
Number of variables: 18
Number of continuous states: 1
Simulating model: ElectricalVehicleWithCharger
Standard Charger
Number of equations: 34
Number of variables: 35
Number of continuous states: 1
Connecting Electric Vehicle to Charger
logStatistics = false
Standard Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Disconnect
logStatistics = false
Super Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Connecting Electric Vehicle to Charger
logStatistics = false
Super Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Disconnect
logStatistics = false
Super Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Connecting Electric Vehicle to Charger
logStatistics = false
Super Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Disconnect
logStatistics = false
Standard Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Connecting Electric Vehicle to Charger
logStatistics = false
Standard Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Disconnect
logStatistics = false
Super Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Connecting Electric Vehicle to Charger
logStatistics = false
Standard Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Disconnect
logStatistics = false
Super Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
CollidingBalls: Demonstrating the use of allInstances to set up contact force between any number of balls
expandArrayIncidence = true
storeEliminated = false
Simulating model: Balls3
Number of equations: 24
Number of variables: 36
Number of continuous states: 12
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Simulating model: HeatTransfer
Number of equations: 1
Number of variables: 2
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
----------------------
Number of simulations OK : 6
Number of simulations NOT OK: 0
Log category statistics:
StaticModel: 1
DynamicModel: 22
CalculatedResult: 6
----------------------
Test Summary: | Pass Total
RunTests | 320 320
Testing Modia tests passed
Results with Julia v1.3.1-pre-7704df0a5a
Testing was successful .
Last evaluation was ago and took 8 minutes, 43 seconds.
Click here to download the log file.
Click here to show the log contents.
Resolving package versions...
Installed FunctionWrappers ──────────── v1.0.0
Installed Tables ────────────────────── v0.2.11
Installed ConstructionBase ──────────── v1.0.0
Installed Unitful ───────────────────── v0.18.0
Installed DataStructures ────────────── v0.17.6
Installed IterativeSolvers ──────────── v0.8.1
Installed DataFrames ────────────────── v0.19.4
Installed Modia ─────────────────────── v0.3.0
Installed MacroTools ────────────────── v0.5.2
Installed Compat ────────────────────── v2.2.0
Installed Missings ──────────────────── v0.4.3
Installed PooledArrays ──────────────── v0.5.2
Installed TableTraits ───────────────── v1.0.0
Installed Roots ─────────────────────── v0.8.3
Installed StaticArrays ──────────────── v0.12.1
Installed Sundials ──────────────────── v3.8.1
Installed BinaryProvider ────────────── v0.5.8
Installed MuladdMacro ───────────────── v0.2.1
Installed DocStringExtensions ───────── v0.8.1
Installed InvertedIndices ───────────── v1.0.0
Installed Parameters ────────────────── v0.12.0
Installed Requires ──────────────────── v0.5.2
Installed ArrayInterface ────────────── v2.0.0
Installed RecursiveArrayTools ───────── v1.2.0
Installed DataValueInterfaces ───────── v1.0.0
Installed RecursiveFactorization ────── v0.1.0
Installed Reexport ──────────────────── v0.2.0
Installed DiffEqBase ────────────────── v6.7.0
Installed ModiaMath ─────────────────── v0.5.2
Installed CategoricalArrays ─────────── v0.7.3
Installed IteratorInterfaceExtensions ─ v1.0.0
Installed RecipesBase ───────────────── v0.7.0
Installed DataAPI ───────────────────── v1.1.0
Installed JSON ──────────────────────── v0.21.0
Installed OrderedCollections ────────── v1.1.0
Installed Parsers ───────────────────── v0.3.10
Installed TreeViews ─────────────────── v0.3.0
Installed DiffEqDiffTools ───────────── v1.5.0
Installed SortingAlgorithms ─────────── v0.3.1
Updating `~/.julia/environments/v1.3/Project.toml`
[cb905087] + Modia v0.3.0
Updating `~/.julia/environments/v1.3/Manifest.toml`
[4fba245c] + ArrayInterface v2.0.0
[b99e7846] + BinaryProvider v0.5.8
[324d7699] + CategoricalArrays v0.7.3
[34da2185] + Compat v2.2.0
[187b0558] + ConstructionBase v1.0.0
[9a962f9c] + DataAPI v1.1.0
[a93c6f00] + DataFrames v0.19.4
[864edb3b] + DataStructures v0.17.6
[e2d170a0] + DataValueInterfaces v1.0.0
[2b5f629d] + DiffEqBase v6.7.0
[01453d9d] + DiffEqDiffTools v1.5.0
[ffbed154] + DocStringExtensions v0.8.1
[069b7b12] + FunctionWrappers v1.0.0
[41ab1584] + InvertedIndices v1.0.0
[42fd0dbc] + IterativeSolvers v0.8.1
[82899510] + IteratorInterfaceExtensions v1.0.0
[682c06a0] + JSON v0.21.0
[1914dd2f] + MacroTools v0.5.2
[e1d29d7a] + Missings v0.4.3
[cb905087] + Modia v0.3.0
[67ccffd1] + ModiaMath v0.5.2
[46d2c3a1] + MuladdMacro v0.2.1
[bac558e1] + OrderedCollections v1.1.0
[d96e819e] + Parameters v0.12.0
[69de0a69] + Parsers v0.3.10
[2dfb63ee] + PooledArrays v0.5.2
[3cdcf5f2] + RecipesBase v0.7.0
[731186ca] + RecursiveArrayTools v1.2.0
[f2c3362d] + RecursiveFactorization v0.1.0
[189a3867] + Reexport v0.2.0
[ae029012] + Requires v0.5.2
[f2b01f46] + Roots v0.8.3
[a2af1166] + SortingAlgorithms v0.3.1
[90137ffa] + StaticArrays v0.12.1
[c3572dad] + Sundials v3.8.1
[3783bdb8] + TableTraits v1.0.0
[bd369af6] + Tables v0.2.11
[a2a6695c] + TreeViews v0.3.0
[1986cc42] + Unitful v0.18.0
[2a0f44e3] + Base64
[ade2ca70] + Dates
[8bb1440f] + DelimitedFiles
[8ba89e20] + Distributed
[9fa8497b] + Future
[b77e0a4c] + InteractiveUtils
[76f85450] + LibGit2
[8f399da3] + Libdl
[37e2e46d] + LinearAlgebra
[56ddb016] + Logging
[d6f4376e] + Markdown
[a63ad114] + Mmap
[44cfe95a] + Pkg
[de0858da] + Printf
[3fa0cd96] + REPL
[9a3f8284] + Random
[ea8e919c] + SHA
[9e88b42a] + Serialization
[1a1011a3] + SharedArrays
[6462fe0b] + Sockets
[2f01184e] + SparseArrays
[10745b16] + Statistics
[4607b0f0] + SuiteSparse
[8dfed614] + Test
[cf7118a7] + UUIDs
[4ec0a83e] + Unicode
Building Sundials → `~/.julia/packages/Sundials/MllUG/deps/build.log`
Testing Modia
Status `/tmp/jl_Oc0FLE/Manifest.toml`
[4fba245c] ArrayInterface v2.0.0
[b99e7846] BinaryProvider v0.5.8
[324d7699] CategoricalArrays v0.7.3
[34da2185] Compat v2.2.0
[187b0558] ConstructionBase v1.0.0
[9a962f9c] DataAPI v1.1.0
[a93c6f00] DataFrames v0.19.4
[864edb3b] DataStructures v0.17.6
[e2d170a0] DataValueInterfaces v1.0.0
[2b5f629d] DiffEqBase v6.7.0
[01453d9d] DiffEqDiffTools v1.5.0
[ffbed154] DocStringExtensions v0.8.1
[069b7b12] FunctionWrappers v1.0.0
[41ab1584] InvertedIndices v1.0.0
[42fd0dbc] IterativeSolvers v0.8.1
[82899510] IteratorInterfaceExtensions v1.0.0
[682c06a0] JSON v0.21.0
[1914dd2f] MacroTools v0.5.2
[e1d29d7a] Missings v0.4.3
[cb905087] Modia v0.3.0
[67ccffd1] ModiaMath v0.5.2
[46d2c3a1] MuladdMacro v0.2.1
[bac558e1] OrderedCollections v1.1.0
[d96e819e] Parameters v0.12.0
[69de0a69] Parsers v0.3.10
[2dfb63ee] PooledArrays v0.5.2
[3cdcf5f2] RecipesBase v0.7.0
[731186ca] RecursiveArrayTools v1.2.0
[f2c3362d] RecursiveFactorization v0.1.0
[189a3867] Reexport v0.2.0
[ae029012] Requires v0.5.2
[f2b01f46] Roots v0.8.3
[a2af1166] SortingAlgorithms v0.3.1
[90137ffa] StaticArrays v0.12.1
[c3572dad] Sundials v3.8.1
[3783bdb8] TableTraits v1.0.0
[bd369af6] Tables v0.2.11
[a2a6695c] TreeViews v0.3.0
[1986cc42] Unitful v0.18.0
[2a0f44e3] Base64 [`@stdlib/Base64`]
[ade2ca70] Dates [`@stdlib/Dates`]
[8bb1440f] DelimitedFiles [`@stdlib/DelimitedFiles`]
[8ba89e20] Distributed [`@stdlib/Distributed`]
[9fa8497b] Future [`@stdlib/Future`]
[b77e0a4c] InteractiveUtils [`@stdlib/InteractiveUtils`]
[76f85450] LibGit2 [`@stdlib/LibGit2`]
[8f399da3] Libdl [`@stdlib/Libdl`]
[37e2e46d] LinearAlgebra [`@stdlib/LinearAlgebra`]
[56ddb016] Logging [`@stdlib/Logging`]
[d6f4376e] Markdown [`@stdlib/Markdown`]
[a63ad114] Mmap [`@stdlib/Mmap`]
[44cfe95a] Pkg [`@stdlib/Pkg`]
[de0858da] Printf [`@stdlib/Printf`]
[3fa0cd96] REPL [`@stdlib/REPL`]
[9a3f8284] Random [`@stdlib/Random`]
[ea8e919c] SHA [`@stdlib/SHA`]
[9e88b42a] Serialization [`@stdlib/Serialization`]
[1a1011a3] SharedArrays [`@stdlib/SharedArrays`]
[6462fe0b] Sockets [`@stdlib/Sockets`]
[2f01184e] SparseArrays [`@stdlib/SparseArrays`]
[10745b16] Statistics [`@stdlib/Statistics`]
[4607b0f0] SuiteSparse [`@stdlib/SuiteSparse`]
[8dfed614] Test [`@stdlib/Test`]
[cf7118a7] UUIDs [`@stdlib/UUIDs`]
[4ec0a83e] Unicode [`@stdlib/Unicode`]
Welcome to Modia - Dynamic Modeling and Simulation with Julia
Version 0.3.0 (2019-04-07)
Type "?Modia" for help.
Importing ModiaMath Version 0.5.2 (2019-07-10)
PyPlot not available (plot commands will be ignored).
Try to install PyPlot. See hints here:
https://github.com/ModiaSim/ModiaMath.jl/wiki/Installing-PyPlot-in-a-robust-way.
Test match
assign = [8, 1, 2, 7, 4, 5, 3, 0]
Singular system
assign = [0, 3, 1, 0]
(invAssign, unAssignedVariables) = ([3, 0, 2], [1, 4])
(ass, unAssignedEquations) = ([0, 3, 1, 0], [2])
Test Tarjans strong connect
components = Any[Any[6], Any[7, 5, 4, 3], Any[8, 2, 1]]
Fixed-length pendulum
assign = [5, 4, 1, 2, 0, 0, 3, 0, 0]
Assigned original equations:
Test diagnostics for too many equations
Gbig = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 2], [1, 10], [2, 10]]
EGbig = Any[[3, 5, 11], [4, 6, 11], [1, 7, 9, 11], [2, 8, 9, 11], [1, 2, 11], [1, 10, 11], [2, 10, 11], [1, 5, 11], [2, 6, 11], [3, 7, 11], [4, 8, 11]]
componentsBig = Any[Any[5, 10, 3, 4, 11, 2, 9, 7, 6, 8, 1]]
Test diagnostics for too many variables
Gbig = Any[[3, 5], [4, 6], [1, 7, 9], [10, 8, 9], [1, 10]]
EGbig = Any[[3, 5], [4, 6], [1, 7, 9], [10, 8, 9], [1, 10], [1, 5], [2, 6], [3, 7], [4, 8], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]
componentsBig = Any[Any[5, 4, 9, 2, 7, 10, 6, 3, 8, 1]]
Test diagnostics for too few equations
Gbig = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9]]
EGbig = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 5], [2, 6], [3, 7], [4, 8], [1, 2, 3, 4, 5, 6, 7, 8, 9]]
componentsBig = Any[Any[6, 2, 8, 4, 9, 7, 3, 5, 1]]
Check consistency of equations by matching extended equation set
EG = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 2], [1, 5], [2, 6], [3, 7], [4, 8]]
assign = [5, 7, 1, 9, 6, 2, 8, 4, 3]
Perform index reduction
G = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 2], [1, 2, 5, 6], [3, 5, 7, 10], [4, 6, 8, 11], [1, 2, 5, 6, 10, 11]]
assign = [0, 0, 0, 0, 1, 2, 7, 4, 3, 9, 8]
A = [5, 6, 7, 8, 10, 11, 0, 0, 0, 0, 0]
B = [7, 8, 0, 0, 6, 9, 0, 0, 0]
------------------------------------------------------
vActive = Bool[0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]
assign = [0, 5, 0, 2, 1, 6, 7, 4, 3, 9, 8]
components = Any[Any[1], Any[5], Any[6], Any[2], Any[4, 8, 9, 7, 3]]
------------------------------------------------------
All unknowns:
All equations:
Assigned equations:
Sorted equations:
Build augmented system.
AG = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 2], [1, 2, 5, 6], [3, 5, 7, 10], [4, 6, 8, 11], [1, 2, 5, 6, 10, 11], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]]
assignAG = [5, 4, 1, 2, 6, 8, 3, 10, 11, 7, 9]
componentsAG = Any[Any[11, 3, 7, 9, 8, 2, 10, 4, 5, 6, 1]]
Assigned augmented equations:
Sorted augmented equations:
Set initial conditions on x and y. Should fail.
IG1 = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 2], [1, 2, 5, 6], [3, 5, 7, 10], [4, 6, 8, 11], [1, 2, 5, 6, 10, 11], [1], [2]]
assignIG1 = [10, 5, 1, 2, 6, 8, 3, 4, 0, 7, 9]
componentsIG1 = Any[Any[10], Any[5], Any[4], Any[3], Any[7, 9, 2, 8, 6, 1], Any[11]]
Set initial conditions on x and w.
IG2 = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 2], [1, 2, 5, 6], [3, 5, 7, 10], [4, 6, 8, 11], [1, 2, 5, 6, 10, 11], [1], [3]]
assignIG2 = [10, 5, 11, 2, 1, 6, 3, 8, 4, 7, 9]
componentsIG2 = Any[Any[11], Any[1], Any[10], Any[5], Any[6], Any[2], Any[7, 9, 8, 4, 3]]
Sorted IG2 equations:
Set initial conditions on w and z.
IG3 = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 2], [1, 2, 5, 6], [3, 5, 7, 10], [4, 6, 8, 11], [1, 2, 5, 6, 10, 11], [3], [4]]
assignIG3 = [6, 5, 10, 11, 1, 2, 3, 8, 4, 7, 9]
componentsIG3 = Any[Any[10], Any[1], Any[11], Any[2], Any[5, 6], Any[7, 9, 8, 4, 3]]
Sorted IG3 equations:
Fixed-length pendulum
Perform index reduction
Set initial conditions on x and w.
IG = Any[[3, 5], [4, 6], [1, 7, 9], [2, 8, 9], [1, 2], [1, 2, 5, 6], [3, 5, 7, 10], [4, 6, 8, 11], [1, 2, 5, 6, 10, 11], [1], [3]]
assignIG = [10, 5, 11, 2, 1, 6, 3, 8, 4, 7, 9]
componentsIG = Any[Any[11], Any[1], Any[10], Any[5], Any[6], Any[2], Any[7, 9, 8, 4, 3]]
Exothermic Reactor Model
assign = [0, 0, 1, 7, 3, 2, 8, 6]
A = [3, 4, 7, 0, 8, 0, 0, 0]
B = [6, 0, 7, 5, 8, 0, 0, 0]
components = Any[Any[3], Any[1], Any[8], Any[6], Any[7], Any[2], Any[4], Any[5]]
----------------------
----------------------
Big tests, n = 5000
Big test: diagonal
0.073476 seconds (76.44 k allocations: 51.765 MiB, 27.44% gc time)
Big test: band
0.044194 seconds (108.39 k allocations: 52.149 MiB, 13.24% gc time)
Big test: full, n=1000
0.989069 seconds (4.80 M allocations: 105.997 MiB, 1.76% gc time)
Test solve
Solve: x from: y = x
x = y
Solve: x from: y = x + z
x = y - z
Solve: x from: y = x + z + v + w
x = y - (z + v + w)
Solve: z from: y = x + z + v + w
z = (y - x) - (v + w)
Solve: v from: y = x + z + v + w
v = ((y - x) - z) - w
Solve: w from: y = x + z + v + w
w = ((y - x) - z) - v
Solve: x from: y = x - z
x = y + z
Solve: x from: y = (x - z) - w
x = (y + w) + z
Solve: x from: y = -(x, z, v, w)
x = y + (z + v + w)
Solve: v from: y = -(x, z, v, w)
v = ((x - y) - z) - w
Solve: x from: y = z - x
x = z - y
Solve: x from: y = x * z
x = y / z
Solve: x from: y = x * z * z * z
x = y / (z * z * z)
Solve: x from: y = /(x, z, w)
x = y * (z * w)
Solve: z from: y = /(x, z, w)
z = (x / y) / w
Solve: x from: y = x / z
x = y * z
Solve: z from: y = x / z
z = x / y
Solve: x from: y = x \ z
NOT SOLVED
x \ z = y
----------------------
Test differentiate
Equation: x + 5 + z = w
Differentiated: der(x) + der(z) = der(w)
Equation: der(x) + der(z) = der(w)
Differentiated: der(der(x)) + der(der(z)) = der(der(w))
Equation: +x = w
Differentiated: der(x) = der(w)
Equation: 2 + 3 = w
Differentiated: 0.0 = der(w)
Equation: -x = w
Differentiated: -(der(x)) = der(w)
Equation: (x - 5) - z = w
Differentiated: der(x) - der(z) = der(w)
Equation: 5x = w
Differentiated: 5 * der(x) = der(w)
Equation: x * 5 * z = w
Differentiated: der(x) * 5 * z + x * 5 * der(z) = der(w)
Equation: 4 * 5 * 6 = w
Differentiated: 0.0 = der(w)
Equation: y = x / y
Differentiated: der(y) = der(x) / y + (x / y ^ 2) * der(y)
Equation: y = x / 5
Differentiated: der(y) = der(x) / 5
Equation: y = 5 / y
Differentiated: der(y) = (5 / y ^ 2) * der(y)
Equation: y = [1, x]
Differentiated: der(y) = [0.0, der(x)]
Equation: y = [2x 3x; 4x 5x]
Differentiated: der(y) = [2 * der(x) 3 * der(x); 4 * der(x) 5 * der(x)]
Equation: y = [2x 3x; 4x 5x] * [1, x]
Differentiated: der(y) = [2 * der(x) 3 * der(x); 4 * der(x) 5 * der(x)] * [1, x] + [2x 3x; 4x 5x] * [0.0, der(x)]
Equation: y = transpose(B) + B´
Differentiated: der(y) = transpose(der(B)) + der(B´)
Equation: y = x[5, 6]
Differentiated: der(y) = (der(x))[5, 6]
Equation: y = x[5:7]
Differentiated: der(y) = (der(x))[5:7]
Equation: y = sin(x)
Differentiated: der(y) = cos(x) * der(x)
Equation: y = cos(x)
Differentiated: der(y) = -(sin(x)) * der(x)
Equation: y = tan(x)
Differentiated: der(y) = (1 / cos(x) ^ 2) * der(x)
Equation: y = exp(x)
Differentiated: der(y) = exp(x) * der(x)
Equation: y = x ^ y
Differentiated: der(y) = y * x ^ (y - 1) * der(x) + x ^ y * log(x) * der(y)
Equation: y = log(x)
Differentiated: der(y) = (1 / x) * der(x)
Equation: y = asin(x)
Differentiated: der(y) = (1 / sqrt(1 - x ^ 2)) * der(x)
Equation: y = acos(x)
Differentiated: der(y) = (-1 / sqrt(1 - x ^ 2)) * der(x)
Equation: y = atan(x)
Differentiated: der(y) = (1 / (1 + x ^ 2)) * der(x)
Equation: y = f(x, 5, z)
Derivative function f_der_1 not found.
Derivative function f_der_3 not found.
Differentiated: der(y) = f_der_1(x, 5, z) * der(x) + f_der_3(x, 5, z) * der(z)
Equation: y = f(x, 5, g(z))
Derivative function f_der_1 not found.
Derivative function g_der_1 not found.
Derivative function f_der_3 not found.
Differentiated: der(y) = f_der_1(x, 5, g(z)) * der(x) + f_der_3(x, 5, g(z)) * (g_der_1(z) * der(z))
Equation: y = if true
x
else
y
end
Differentiated: der(y) = if true
der(x)
else
der(y)
end
Equation: y = time
Differentiated: der(y) = 1.0
Equation: y = a * x
Differentiated: der(y) = a * der(x)
----------------------
... Test two coupled inertias (all unknowns can be solved for)
Variables of _x vector (length=2):
_x[1]: J1_phi
_x[2]: der(J1_phi) # = der(_x[1])
Variables of _der_x vector (length=2):
_der_x[1]: --- # = _x[2] = der(J1_phi)
_der_x[2]: der2(J1_phi)
Sorted equations (length(_r) = 2, nc = 0):
_r[1] = _der_x[1] - _x[2]
J2_phi = < solved from eq.5 >
der(J2_phi) = < solved from eq.7 = der(eq.5) >
J1_w = < solved from eq.1 >
J2_w = < solved from eq.3 >
der2(J2_phi) = < solved from eq.10 = der2(eq.5) >
der(J1_w) = < solved from eq.8 = der(eq.1) >
der(J2_w) = < solved from eq.9 = der(eq.3) >
J2_tau = < solved from eq.4 >
J1_tau = < solved from eq.6 >
_r[2] = < residue of eq.2 >
... Test two coupled inertias (only a subset of unknowns can be solved for)
Variables of _x vector (length=2):
_x[1]: J2_phi
_x[2]: der(J2_phi) # = der(_x[1])
Variables of _der_x vector (length=2):
_der_x[1]: --- # = _x[2] = der(J2_phi)
_der_x[2]: der2(J2_phi)
Sorted equations (length(_r) = 2, nc = 0):
_r[1] = _der_x[1] - _x[2]
J1_phi = < solved from eq.5 >
der(J1_phi) = < solved from eq.7 = der(eq.5) >
J1_w = < solved from eq.1 >
J2_w = < solved from eq.3 >
der2(J1_phi) = < solved from eq.10 = der2(eq.5) >
der(J1_w) = < solved from eq.8 = der(eq.1) >
der(J2_w) = < solved from eq.9 = der(eq.3) >
J2_tau = < solved from eq.4 >
J1_tau = < solved from eq.6 >
_r[2] = < residue of eq.2 >
... Test two coupled inertias (no unknowns can be solved for)
Variables of _x vector (length=9):
_x[1]: J1_phi
_x[2]: J2_phi
_x[3]: der(J1_phi) # = der(_x[1])
_x[4]: der(J2_phi) # = der(_x[2])
_x[5]: J2_w
_x[6]: J1_w
_x[7]: --- # integral of lambda variable
_x[8]: --- # integral of lambda variable
_x[9]: --- # integral of mue variable
Variables of _der_x vector (length=9):
_der_x[1]: --- # = _x[3] = der(J1_phi)
_der_x[2]: --- # = _x[4] = der(J2_phi)
_der_x[3]: der2(J1_phi)
_der_x[4]: der2(J2_phi)
_der_x[5]: der(J2_w)
_der_x[6]: der(J1_w)
_der_x[7]: J2_tau # lambda variable
_der_x[8]: J1_tau # lambda variable
_der_x[9]: --- # mue variable associated with equation eq.7 = der(eq.5)
Sorted equations (length(_r) = 9, nc = 4):
_r[1] = _der_x[1] - _x[3]
_r[2] = _der_x[2] - _x[4]
_r[6] = < residue of eq.5 >
_r[7] = < residue of eq.1 >
_r[9] = < residue of eq.7 = der(eq.5) >
_r[8] = < residue of eq.3 >
_r[3] = < residue of eq.4 >
_r[4] = < residue of eq.6 >
_r[5] = < residue of eq.2 >
... Test simple sliding mass model with Tearing
Variables of _x vector (length=3):
_x[1]: s
_x[2]: der(s) # = der(_x[1])
_x[3]: sf
Variables of _der_x vector (length=3):
_der_x[1]: --- # = _x[2] = der(s)
_der_x[2]: der2(s)
_der_x[3]: der(sf)
Sorted equations (length(_r) = 3, nc = 0):
_r[1] = _der_x[1] - _x[2]
r = < solved from eq.1 >
der(r) = < solved from eq.6 = der(eq.1) >
v = < solved from eq.2 >
der2(r) = < solved from eq.7 = der2(eq.1) >
der(v) = < solved from eq.8 = der(eq.2) >
u = < solved from eq.5 >
_r[2] = < residue of eq.9 >
f = < solved from eq.3 >
_r[3] = < residue of eq.4 >
... Test Multi-Index DAE without tearing
Variables of _x vector (length=21):
_x[1]: x7
_x[2]: x6
_x[3]: der(x7) # = der(_x[1])
_x[4]: der(x6) # = der(_x[2])
_x[5]: der2(x7) # = der(_x[3])
_x[6]: der2(x6) # = der(_x[4])
_x[7]: x1
_x[8]: x2
_x[9]: x3
_x[10]: der(x1) # = der(_x[7])
_x[11]: der(x2) # = der(_x[8])
_x[12]: der(x3) # = der(_x[9])
_x[13]: x4
_x[14]: x8 # algebraic variable
_x[15]: --- # integral of lambda variable
_x[16]: --- # integral of mue variable
_x[17]: --- # integral of mue variable
_x[18]: --- # integral of mue variable
_x[19]: --- # integral of mue variable
_x[20]: --- # integral of mue variable
_x[21]: --- # integral of mue variable
Variables of _der_x vector (length=21):
_der_x[1]: --- # = _x[3] = der(x7)
_der_x[2]: --- # = _x[4] = der(x6)
_der_x[3]: --- # = _x[5] = der2(x7)
_der_x[4]: --- # = _x[6] = der2(x6)
_der_x[5]: der3(x7)
_der_x[6]: der3(x6)
_der_x[7]: --- # = _x[10] = der(x1)
_der_x[8]: --- # = _x[11] = der(x2)
_der_x[9]: --- # = _x[12] = der(x3)
_der_x[10]: der2(x1)
_der_x[11]: der2(x2)
_der_x[12]: der2(x3)
_der_x[13]: der(x4)
_der_x[14]: --- # derivative of algebraic variable
_der_x[15]: x5 # lambda variable
_der_x[16]: --- # mue variable associated with equation eq.14 = der(eq.6)
_der_x[17]: --- # mue variable associated with equation eq.15 = der(eq.7)
_der_x[18]: --- # mue variable associated with equation eq.16 = der2(eq.6)
_der_x[19]: --- # mue variable associated with equation eq.17 = der2(eq.7)
_der_x[20]: --- # mue variable associated with equation eq.9 = der(eq.1)
_der_x[21]: --- # mue variable associated with equation eq.11 = der(eq.2)
Sorted equations (length(_r) = 21, nc = 12):
_r[1] = _der_x[1] - _x[3]
_r[2] = _der_x[2] - _x[4]
_r[3] = _der_x[3] - _x[5]
_r[4] = _der_x[4] - _x[6]
_r[5] = _der_x[7] - _x[10]
_r[6] = _der_x[8] - _x[11]
_r[7] = _der_x[9] - _x[12]
_r[11] = < residue of eq.6 >
_r[12] = < residue of eq.7 >
_r[16] = < residue of eq.14 = der(eq.6) >
_r[17] = < residue of eq.15 = der(eq.7) >
_r[18] = < residue of eq.16 = der2(eq.6) >
_r[19] = < residue of eq.17 = der2(eq.7) >
_r[13] = < residue of eq.1 >
_r[14] = < residue of eq.2 >
_r[20] = < residue of eq.9 = der(eq.1) >
_r[21] = < residue of eq.11 = der(eq.2) >
_r[15] = < residue of eq.3 >
_r[10] = < residue of eq.8 >
_r[8] = < residue of eq.4 >
_r[9] = < residue of eq.5 >
... Test Multi-Index DAE WITH tearing
Variables of _x vector (length=8):
_x[1]: x7
_x[2]: der(x7) # = der(_x[1])
_x[3]: der2(x7) # = der(_x[2])
_x[4]: x2
_x[5]: der(x2) # = der(_x[4])
_x[6]: x8 # algebraic variable
_x[7]: --- # integral of mue variable
_x[8]: --- # integral of mue variable
Variables of _der_x vector (length=8):
_der_x[1]: --- # = _x[2] = der(x7)
_der_x[2]: --- # = _x[3] = der2(x7)
_der_x[3]: der3(x7)
_der_x[4]: --- # = _x[5] = der(x2)
_der_x[5]: der2(x2)
_der_x[6]: --- # derivative of algebraic variable
_der_x[7]: --- # mue variable associated with equation eq.15 = der(eq.7)
_der_x[8]: --- # mue variable associated with equation eq.17 = der2(eq.7)
Sorted equations (length(_r) = 8, nc = 4):
_r[1] = _der_x[1] - _x[2]
_r[2] = _der_x[2] - _x[3]
_r[3] = _der_x[4] - _x[5]
x6 = < solved from eq.6 >
_r[6] = < residue of eq.7 >
der(x6) = < solved from eq.14 = der(eq.6) >
_r[7] = < residue of eq.15 = der(eq.7) >
der2(x6) = < solved from eq.16 = der2(eq.6) >
_r[8] = < residue of eq.17 = der2(eq.7) >
x1 = < solved from eq.1 >
x3 = < solved from eq.2 >
der(x1) = < solved from eq.9 = der(eq.1) >
der(x3) = < solved from eq.11 = der(eq.2) >
x4 = < solved from eq.3 >
der3(x6) = < solved from eq.18 = der3(eq.6) >
der2(x1) = < solved from eq.10 = der2(eq.1) >
der2(x3) = < solved from eq.12 = der2(eq.2) >
der(x4) = < solved from eq.13 = der(eq.3) >
x5 = < solved from eq.5 >
_r[5] = < residue of eq.8 >
_r[4] = < residue of eq.4 >
TestVariableTypes: Demonstrating the handling of various variable types
Simulating model: TestVariableTypes1
Number of equations: 9
Number of variables: 11
Number of continuous states: 2
final i = 1
Simulation OK
(result["f"])[end] = 1.0
(result["b"])[end] = true
(result["i"])[end] = 1
(result["s"])[end] = "asdf"
(result["c"])[end] = 2.0 + 3.0im
(result["re"])[end] = 2.0
(result["im"])[end] = 3.0
Simulating model: TestArrays1
Number of equations: 5
Number of variables: 7
Number of continuous states: 2
final i = [1, 2]
Simulation OK
keys(result) = AbstractString["f", "c1", "time", "der(f)", "der(c1)", "b", "s", "i"]
(result["f"])[end, :] = [2.999999999999999, 5.999999999999998, 8.999999999999996]
(result["der(f)"])[end, :] = [2.0, 4.0, 6.0]
(result["b"])[end] = Bool[0, 1]
(result["i"])[end] = [1, 2]
(result["s"])[end] = ["asdf", "qwerty"]
(result["c1"])[end, :] = [2.999999999999999, 5.999999999999998]
(result["der(c1)"])[end, :] = [2.0, 4.0]
storeEliminated = false
Simulating model: TestVariableTypes2
Number of equations: 10
Number of variables: 10
Number of continuous states: 0
Variable(T=Array{Float64,1}; args...) does not work with storeEliminated=true!
logTranslation = true
removeSingularities = false
storeEliminated = false
Log file: /root/ModiaResults/Test.txt
Simulating model: Test
Number of equations: 9
Number of variables: 10
Number of continuous states: 1
Time=0.0: size of u: (10, 10)
Time=0.0: size of u: (10, 10)
Time=0.0: size of u: (10, 10)
Time=0.0: size of u: (10, 10)
Time=0.0: size of u: (10, 10)
Time=0.0: size of u: (10, 10)
Time=5.005005005005005e-6: size of u: (10, 10)
Time=5.005005005005005e-6: size of u: (10, 10)
Time=1.991991991991992: size of u: ()
Time=1.996996996996997: size of u: ()
Time=2.002002002002002: size of u: ()
Time=2.007007007007007: size of u: ()
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Simulating model: Resistor
Number of equations: 6
Number of variables: 6
Number of continuous states: 0
Simulating model: ParallelResistors
Number of equations: 12
Number of variables: 12
Number of continuous states: 0
Simulating model: ParallelCapacitors
Number of equations: 15
Number of variables: 16
Number of continuous states: 1
Number of non states: 2
TestFilter: Tests various features of the symbolic handling.
Simulating model: LPfilter
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
storeEliminated = false
logSimulation = true
Log file: /root/ModiaResults/LPfilter.txt
Simulating model: LPfilter
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: LPfilter
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ C.v │ 0.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 2.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.55 s (init: 0.54 s, integration: 0.0033 s)
startTime = 0.0 s
stopTime = 2.0 s
interval = 0.002002002002002002 s
tolerance = 0.0001
nEquations = 1
nResults = 1000
nSteps = 58
nResidues = 92 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 22
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 1
h0 = 1.3e-07 s
hMin = 1.3e-07 s
hMax = 0.24 s
orderMax = 5
sparseSolver = false
final C.v = 9.996843043929996
Simulation OK
aliasElimination = true
Simulating model: LPfilter
Number of equations: 12
Number of variables: 13
Number of continuous states: 1
logName = "LPfilter aliasElimination"
aliasElimination = true
Simulating model: LPfilter
Number of equations: 12
Number of variables: 13
Number of continuous states: 1
final C.v = 9.996843043929996
Simulation OK
logName = "LPfilter aliasElimination removeSingularities"
aliasElimination = true
removeSingularities = true
Simulating model: LPfilter
Number of equations: 12
Number of variables: 13
Number of continuous states: 1
final C.v = 9.996843043929996
Simulation OK
logName = "LPfilter aliasElimination removeSingularities"
aliasElimination = true
removeSingularities = true
Simulating model: LPfilter
Number of equations: 12
Number of variables: 13
Number of continuous states: 1
removeSingularities = true
Simulating model: LPfilterWithoutGround
Number of equations: 18
Number of variables: 19
Number of continuous states: 1
final C.v = 9.996843043929996
Simulation OK
Simulating model: LPfilterAndSineSource
Number of equations: 20
Number of variables: 22
Number of continuous states: 2
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
removeSingularities = true
Simulating model: HPfilter
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
final C.v = 7.16540372163548
Simulation OK
removeSingularities = true
Simulating model: NewFilter
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
final C.v = 9.999596486913553
Simulation OK
removeSingularities = true
Simulating model: CondFilter
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
final C.v = 3.6787780967777772
Simulation OK
removeSingularities = true
Simulating model: CondFilter2
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
final C.v = 3.6787780967777772
Simulation OK
removeSingularities = true
Simulating model: FilterModels
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
final C.v = 3.6787780967777772
Simulation OK
Simulating model: FilterComponents
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
final C.v = 3.6787780967777772
Simulation OK
Simulating model: TenCoupledFilters
Number of equations: 188
Number of variables: 198
Number of continuous states: 10
final F10.C.v = 1.232726022885834e-5
Simulation OK
aliasElimination = true
Simulating model: TenCoupledFilters
Number of equations: 115
Number of variables: 125
Number of continuous states: 10
final F10.C.v = 1.232726022885834e-5
Simulation OK
TestArrayOfComponents: Demonstrating the handling of arrays of components
Simulating model: TwoFilters
Number of equations: 40
Number of variables: 42
Number of continuous states: 2
final F[2].C.v = 3.2967996078157973
Simulation OK
Simulating model: ManyFilters
Number of equations: 200
Number of variables: 210
Number of continuous states: 10
final F[1].C.v = 9.816758325302478
Simulation OK
Simulating model: ManyDifferentFilters
Number of equations: 200
Number of variables: 210
Number of continuous states: 10
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Simulating model: AdvancedLPfilter
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
Rectifier: Demonstrating conditional components
logTranslation = true
Log file: /root/ModiaResults/ConditionalLoad.txt
Simulating model: ConditionalLoad
Number of equations: 39
Number of variables: 41
Number of continuous states: 2
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Simulating model: NoExtraLoad
Number of equations: 33
Number of variables: 35
Number of continuous states: 2
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Demonstrating conditional equations
logTranslation = true
removeSingularities = false
Log file: /root/ModiaResults/Conditional.txt
Simulating model: Conditional
Conditional equation:
if !steadyState
var"der(x)" + 2x = u
else
0 + 2x = u
end
condition = true
Conditional equation:
if !cond
y = 1
else
y = 2
end
condition = true
Number of equations: 3
Number of variables: 4
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = false
Log file: /root/ModiaResults/ConditionalInstance1.txt
Simulating model: ConditionalInstance1
Conditional equation:
if !steadyState
var"der(x)" + 2x = u
else
0 + 2x = u
end
condition = false
Conditional equation:
if !cond
y = 1
else
y = 2
end
condition = false
Number of equations: 3
Number of variables: 3
Number of continuous states: 0
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = false
Log file: /root/ModiaResults/ConditionalInstance2.txt
Simulating model: ConditionalInstance2
Conditional equation:
if !steadyState
var"der(x)" + 2x = u
else
0 + 2x = u
end
condition = true
Conditional equation:
if !cond
y = 1
else
y = 2
end
condition = true
Number of equations: 3
Number of variables: 4
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = false
removeSingularities = false
Simulating model: Conditional2
Conditional equation:
if !steadyState
var"der(x)" + 2x = u
else
0 + 2x = u
end
condition = true
Conditional equation:
if cond
y = 1
end
condition = false
Number of equations: 2
Number of variables: 3
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Simulating model: SpatialDiscretization
Number of equations: 1
Number of variables: 2
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
storeEliminated = false
Log file: /root/ModiaResults/SpatialDiscretization2.txt
Simulating model: SpatialDiscretization2
Number of equations: 3
Number of variables: 4
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
WARNING: redefining constant n
logTranslation = true
storeEliminated = false
Log file: /root/ModiaResults/SpatialDiscretization4.txt
Simulating model: SpatialDiscretization4
Number of equations: 3
Number of variables: 4
Number of continuous states: 1
0.978080 seconds (611.00 k allocations: 49.265 MiB, 3.63% gc time)
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Demonstrating merging modifiers
logTranslation = true
removeSingularities = false
Log file: /root/ModiaResults/M.txt
Simulating model: M
Number of equations: 1
Number of variables: 2
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = false
Log file: /root/ModiaResults/MInstance.txt
Simulating model: MInstance
Number of equations: 1
Number of variables: 2
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = false
Log file: /root/ModiaResults/MInstance2.txt
Simulating model: MInstance2
Number of equations: 1
Number of variables: 2
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
WARNING: replacing module TestTearing.
TestTearing: Tests tearing algorithm of the symbolic handling.
logTranslation = true
removeSingularities = true
tearing = true
logSimulation = true
Log file: /root/ModiaResults/Tearing1.txt
Simulating model: Tearing1
Number of equations: 3
Number of variables: 4
Number of continuous states: 1
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Tearing1
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ x1 │ 0.0 │ 0 │ 1.0 │
│ 2 │ x2 │ 0.0 │ 0 │ 1.0 │
│ 3 │ x3 │ 1.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
x1 = 0.0 changed to -0.07042253512258778
x2 = 0.0 changed to 0.3802816900817252
Simulation started
Simulation is terminated at time = 1.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.035 s (init: 0.032 s, integration: 0.0028 s)
startTime = 0.0 s
stopTime = 1.0 s
interval = 0.001001001001001001 s
tolerance = 0.0001
nEquations = 3
nResults = 1000
nSteps = 52
nResidues = 168 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 35
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 9.7e-13 s
hMin = 9.7e-13 s
hMax = 0.11 s
orderMax = 3
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
logSimulation = true
Log file: /root/ModiaResults/Tearing1B.txt
Simulating model: Tearing1B
Number of equations: 3
Number of variables: 4
Number of continuous states: 1
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Tearing1B
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ x1 │ 0.0 │ 0 │ 1.0 │
│ 2 │ x3 │ 1.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
x1 = 0.0 changed to 0.2631578952872082
Simulation started
Simulation is terminated at time = 1.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.025 s (init: 0.023 s, integration: 0.0022 s)
startTime = 0.0 s
stopTime = 1.0 s
interval = 0.001001001001001001 s
tolerance = 0.0001
nEquations = 2
nResults = 1000
nSteps = 54
nResidues = 138 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 36
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 1
h0 = 9.8e-13 s
hMin = 9.8e-13 s
hMax = 0.095 s
orderMax = 4
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
logSimulation = true
Log file: /root/ModiaResults/Tearing2.txt
Simulating model: Tearing2
Number of equations: 3
Number of variables: 4
Number of continuous states: 1
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Tearing2
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ x1 │ 0.0 │ 0 │ 1.0 │
│ 2 │ x3 │ 1.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
x1 = 0.0 changed to -2.3941317512897893
Simulation started
Simulation is terminated at time = 0.3 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.026 s (init: 0.024 s, integration: 0.0025 s)
startTime = 0.0 s
stopTime = 0.3 s
interval = 0.0003003003003003003 s
tolerance = 0.0001
nEquations = 2
nResults = 1000
nSteps = 46
nResidues = 122 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 34
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 7.4e-13 s
hMin = 7.4e-13 s
hMax = 0.051 s
orderMax = 3
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
logSimulation = true
Log file: /root/ModiaResults/Tearing3.txt
Simulating model: Tearing3
Number of equations: 4
Number of variables: 5
Number of continuous states: 1
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Tearing3
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ x3 │ 0.0 │ 0 │ 1.0 │
│ 2 │ x4 │ 1.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
x3 = 0.0 changed to 0.2933845009458145
Simulation started
Simulation is terminated at time = 0.3 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.029 s (init: 0.026 s, integration: 0.0032 s)
startTime = 0.0 s
stopTime = 0.3 s
interval = 0.0003003003003003003 s
tolerance = 0.0001
nEquations = 2
nResults = 1000
nSteps = 45
nResidues = 129 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 36
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 9.5e-13 s
hMin = 9.5e-13 s
hMax = 0.065 s
orderMax = 2
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
logSimulation = true
Log file: /root/ModiaResults/Tearing4.txt
Simulating model: Tearing4
Number of equations: 5
Number of variables: 6
Number of continuous states: 1
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Tearing4
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ x3 │ 0.0 │ 0 │ 1.0 │
│ 2 │ x4 │ 0.0 │ 0 │ 1.0 │
│ 3 │ x5 │ 1.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
x3 = 0.0 changed to 0.20486182844879683
x4 = 0.0 changed to 1.4702013267955072
Simulation started
Simulation is terminated at time = 0.3 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.053 s (init: 0.046 s, integration: 0.0065 s)
startTime = 0.0 s
stopTime = 0.3 s
interval = 0.0003003003003003003 s
tolerance = 0.0001
nEquations = 3
nResults = 1000
nSteps = 47
nResidues = 164 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 36
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 7.5e-13 s
hMin = 7.5e-13 s
hMax = 0.046 s
orderMax = 2
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
logSimulation = true
Log file: /root/ModiaResults/TearingCombined.txt
Simulating model: TearingCombined
Number of equations: 14
Number of variables: 18
Number of continuous states: 4
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: TearingCombined
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├────┼────────┼─────────┼───────┼─────────┤
│ 1 │ x1 │ 0.0 │ 0 │ 1.0 │
│ 2 │ x2 │ 0.0 │ 0 │ 1.0 │
│ 3 │ x3 │ 1.0 │ 1 │ 1.0 │
│ 4 │ x11 │ 0.0 │ 0 │ 1.0 │
│ 5 │ x13 │ 1.0 │ 1 │ 1.0 │
│ 6 │ x21 │ 0.0 │ 0 │ 1.0 │
│ 7 │ x23 │ 1.0 │ 1 │ 1.0 │
│ 8 │ x31 │ 0.0 │ 0 │ 1.0 │
│ 9 │ x33 │ 1.0 │ 1 │ 1.0 │
│ 10 │ x35 │ 0.0 │ 0 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
x1 = 0.0 changed to -0.0704225352112676
x2 = 0.0 changed to 0.38028169014084506
x11 = 0.0 changed to 0.26315789473684215
x21 = 0.0 changed to 0.523777476412269
x31 = 0.0 changed to 0.523777476412269
x35 = 0.0 changed to 0.523777476412269
Simulation started
Simulation is terminated at time = 1.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.1 s (init: 0.091 s, integration: 0.0089 s)
startTime = 0.0 s
stopTime = 1.0 s
interval = 0.001001001001001001 s
tolerance = 0.0001
nEquations = 10
nResults = 1000
nSteps = 53
nResidues = 415 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 35
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 8.5e-13 s
hMin = 8.5e-13 s
hMax = 0.11 s
orderMax = 3
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
logSimulation = true
Log file: /root/ModiaResults/Tearing5.txt
Simulating model: Tearing5
Number of equations: 4
Number of variables: 5
Number of continuous states: 1
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Tearing5
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ x2 │ 0.0 │ 0 │ 1.0 │
│ 2 │ x3 │ 0.0 │ 0 │ 1.0 │
│ 3 │ x4 │ 1.0 │ 1 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
x2 = 0.0 changed to -0.014925372682878572
x3 = 0.0 changed to -1.044776118048636
Simulation started
Simulation is terminated at time = 0.3 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.041 s (init: 0.037 s, integration: 0.0034 s)
startTime = 0.0 s
stopTime = 0.3 s
interval = 0.0003003003003003003 s
tolerance = 0.0001
nEquations = 3
nResults = 1000
nSteps = 45
nResidues = 162 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 36
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 9.4e-13 s
hMin = 9.4e-13 s
hMax = 0.065 s
orderMax = 2
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
logSimulation = true
Log file: /root/ModiaResults/Tearing6.txt
Simulating model: Tearing6
Number of equations: 6
Number of variables: 7
Number of continuous states: 1
Number of non states: 2
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: Tearing6
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ u1 │ 1.0 │ 1 │ 1.0 │
│ 2 │ der_u2 │ 0.0 │ 0 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 1.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.029 s (init: 0.027 s, integration: 0.002 s)
startTime = 0.0 s
stopTime = 1.0 s
interval = 0.001001001001001001 s
tolerance = 0.0001
nEquations = 2
nResults = 1000
nSteps = 21
nResidues = 59 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 19
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 1e-06 s
hMin = 1e-06 s
hMax = 0.48 s
orderMax = 2
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = false
tearing = true
automaticStateSelection = true
logSimulation = true
Log file: /root/ModiaResults/TwoConnectedInertias.txt
Simulating model: TwoConnectedInertias
startValues = Any[1.0, 1.0, 0.0, 0.0]
fixedFlags = Any[false, false, false, false]
names = Any[:w1, :w2, :tau, :t, "der(t)", "der(w1)", "der(w2)"]
Avar = [6, 7, 0, 5, 0, 0, 0]
stateIndices = [2, 4]
Gsolvable = Any[Any[5], Any[3], Any[3], Any[1, 2], Any[]]
stateIndices = [2, 4]
stateNames = ["w2", "t"]
realStates = Any[this.t, this.w1, this.w2]
Number of equations: 5
Number of variables: 7
Number of continuous states: 2
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: TwoConnectedInertias
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ w2 │ 1.0 │ 1 │ 1.0 │
│ 2 │ t │ 0.0 │ 1 │ 1.0 │
│ 3 │ der_w1 │ 0.0 │ 0 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 3.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.03 s (init: 0.028 s, integration: 0.0019 s)
startTime = 0.0 s
stopTime = 3.0 s
interval = 0.003003003003003003 s
tolerance = 0.0001
nEquations = 3
nResults = 1000
nSteps = 37
nResidues = 96 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 16
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 2
h0 = 3e-06 s
hMin = 3e-06 s
hMax = 0.27 s
orderMax = 4
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
automaticStateSelection = true
logSimulation = true
Log file: /root/ModiaResults/TwoInertiasConnectedViaIdealGear.txt
Simulating model: TwoInertiasConnectedViaIdealGear
startValues = Any[1.0, 1.0, 0.0, 0.0]
fixedFlags = Any[false, false, false, false]
names = Any[:w1, :w2, :tau, :t, "der(t)", "der(w1)", "der(w2)"]
Avar = [6, 7, 0, 5, 0, 0, 0]
stateIndices = [2, 4]
Gsolvable = Array{Int64,1}[[5], [], [3], [1], []]
stateIndices = [2, 4]
stateNames = ["w2", "t"]
realStates = Any[this.t, this.w1, this.w2]
Number of equations: 5
Number of variables: 7
Number of continuous states: 2
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: TwoInertiasConnectedViaIdealGear
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ w2 │ 1.0 │ 1 │ 1.0 │
│ 2 │ t │ 0.0 │ 1 │ 1.0 │
│ 3 │ der_w1 │ 0.0 │ 0 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 3.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.079 s (init: 0.073 s, integration: 0.0061 s)
startTime = 0.0 s
stopTime = 3.0 s
interval = 0.003003003003003003 s
tolerance = 0.0001
nEquations = 3
nResults = 1000
nSteps = 36
nResidues = 101 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 17
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 3
h0 = 3e-06 s
hMin = 3e-06 s
hMax = 0.39 s
orderMax = 4
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
automaticStateSelection = false
logSimulation = true
Log file: /root/ModiaResults/ParallelCapacitors1.txt
Simulating model: ParallelCapacitors1
Number of equations: 15
Number of variables: 16
Number of continuous states: 1
Number of non states: 2
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: ParallelCapacitors1
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼──────────┼─────────┼───────┼─────────┤
│ 1 │ C1.v │ 1.0 │ 1 │ 1.0 │
│ 2 │ C2.p.i │ 0.0 │ 0 │ 1.0 │
│ 3 │ der_C2.v │ 0.0 │ 0 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 1.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.032 s (init: 0.029 s, integration: 0.0024 s)
startTime = 0.0 s
stopTime = 1.0 s
interval = 0.001001001001001001 s
tolerance = 0.0001
nEquations = 3
nResults = 1000
nSteps = 21
nResidues = 78 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 19
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 1e-06 s
hMin = 1e-06 s
hMax = 0.48 s
orderMax = 2
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = false
tearing = true
automaticStateSelection = true
logSimulation = true
Log file: /root/ModiaResults/ParallelCapacitors2.txt
Simulating model: ParallelCapacitors2
startValues = Any[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
fixedFlags = Any[false, false, false, false, false, false, false, false, false, false, false, false, false, false]
names = Any[Symbol("C1.v"), Symbol("C1.i"), Symbol("C1.p.v"), Symbol("C1.p.i"), Symbol("C1.n.v"), Symbol("C1.n.i"), Symbol("C2.v"), Symbol("C2.i"), Symbol("C2.p.v"), Symbol("C2.p.i"), Symbol("C2.n.v"), Symbol("C2.n.i"), Symbol("ground.p.v"), Symbol("ground.p.i"), "der(C1.v)", "der(C2.v)"]
Avar = [15, 0, 17, 0, 18, 0, 16, 0, 19, 0, 20, 0, 21, 0, 0, 0, 0, 0, 0, 0, 0]
stateIndices = [9]
Gsolvable = Any[Any[1, 3, 5], Any[4, 6], Any[2, 4], Any[2], Any[7, 9, 11], Any[10, 12], Any[8, 10], Any[8], Any[13], Any[5, 13], Any[5, 11], Any[6, 12, 14], Any[3, 9], Any[4, 10], Any[], Any[], Any[], Any[], Any[], Any[]]
stateIndices = [9]
stateNames = ["C2.p.v"]
realStates = Any[this.C1.v, this.C2.v]
Number of equations: 20
Number of variables: 21
Number of continuous states: 1
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: ParallelCapacitors2
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────────┼─────────┼───────┼─────────┤
│ 1 │ C2.p.v │ 0.0 │ 1 │ 1.0 │
│ 2 │ der_C1.v │ 0.0 │ 0 │ 1.0 │
│ 3 │ der_C1.p.v │ 0.0 │ 0 │ 1.0 │
│ 4 │ der_C2.v │ 0.0 │ 0 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 1.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.046 s (init: 0.043 s, integration: 0.0035 s)
startTime = 0.0 s
stopTime = 1.0 s
interval = 0.001001001001001001 s
tolerance = 0.0001
nEquations = 4
nResults = 1000
nSteps = 21
nResidues = 97 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 19
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 1e-06 s
hMin = 1e-06 s
hMax = 0.48 s
orderMax = 2
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = true
tearing = true
automaticStateSelection = false
logSimulation = true
Log file: /root/ModiaResults/ParallelCapacitors2b.txt
Simulating model: ParallelCapacitors2b
Number of equations: 6
Number of variables: 7
Number of continuous states: 1
Number of non states: 2
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: ParallelCapacitors2b
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ u1 │ 1.0 │ 1 │ 1.0 │
│ 2 │ der_u2 │ 0.0 │ 0 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 1.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.056 s (init: 0.054 s, integration: 0.0024 s)
startTime = 0.0 s
stopTime = 1.0 s
interval = 0.001001001001001001 s
tolerance = 0.0001
nEquations = 2
nResults = 1000
nSteps = 21
nResidues = 59 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 19
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 0
h0 = 1e-06 s
hMin = 1e-06 s
hMax = 0.48 s
orderMax = 2
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
logTranslation = true
removeSingularities = false
tearing = true
automaticStateSelection = true
logSimulation = true
Log file: /root/ModiaResults/TwoInertiasConnectedViaIdealGearWithPositionConstraints.txt
Simulating model: TwoInertiasConnectedViaIdealGearWithPositionConstraints
startValues = Any[1.0, 1.0, 1.0, 1.0, 0.0, 0.0]
fixedFlags = Any[true, false, true, true, false, false]
names = Any[:phi1, :phi2, :w1, :w2, :tau, :t, "der(t)", "der(phi1)", "der(phi2)", "der(w1)", "der(w2)"]
Avar = [8, 9, 10, 11, 0, 7, 0, 12, 13, 0, 0, 0, 0]
stateIndices = [2, 9, 6]
Gsolvable = Any[Any[7], Any[3, 8], Any[4, 9], Any[], Any[5], Any[1], Any[], Any[], Any[], Any[]]
alias = 4
i = 2
stateIndices = [2, 4, 6]
stateNames = ["phi2", "w2", "t"]
realStates = Any[this.t, this.phi1, this.phi2, this.w1, this.w2]
Number of equations: 10
Number of variables: 13
Number of continuous states: 3
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: TwoInertiasConnectedViaIdealGearWithPositionConstraints
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼──────────────┼─────────┼───────┼─────────┤
│ 1 │ phi2 │ 1.0 │ 1 │ 1.0 │
│ 2 │ w2 │ 1.0 │ 1 │ 1.0 │
│ 3 │ t │ 0.0 │ 1 │ 1.0 │
│ 4 │ der_der_phi2 │ 0.0 │ 0 │ 1.0 │
│ 5 │ der_der_phi1 │ 0.0 │ 0 │ 1.0 │
│ 6 │ der_w1 │ 0.0 │ 0 │ 1.0 │
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Simulation is terminated at time = 3.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.046 s (init: 0.043 s, integration: 0.0028 s)
startTime = 0.0 s
stopTime = 3.0 s
interval = 0.003003003003003003 s
tolerance = 0.0001
nEquations = 6
nResults = 1000
nSteps = 37
nResidues = 145 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 16
nTimeEvents = 0
nStateEvents = 0
nRestartEvents = 0
nErrTestFails = 2
h0 = 3e-06 s
hMin = 3e-06 s
hMax = 0.28 s
orderMax = 4
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
after plot
----------------------
Number of simulations OK : 16
Number of simulations NOT OK: 0
Log category statistics:
StaticModel: 3
DynamicModel: 50
CalculatedResult: 16
----------------------
CurrentController: Demonstrating the ability to simulate mixed domain models
removeSingularities = false
tearing = true
Simulating model: CurrentController
Number of equations: 83
Number of variables: 91
Number of continuous states: 8
Number of non states: 2
final load.w = 0.07929150274932795
Simulation OK
Simulating model: CurrentController
Number of equations: 81
Number of variables: 89
Number of continuous states: 8
Number of non states: 3
final load.w = 0.07927277295604353
Simulation OK
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Rectifier: Demonstrating the ability to simulate models with state events
logTranslation = true
Log file: /root/ModiaResults/Rectifier.txt
Simulating model: Rectifier
Number of equations: 33
Number of variables: 35
Number of continuous states: 2
final C.v = 0.47739155081386353
Simulation OK
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
TestCauerLowPassFilter: Demonstrating the ability to simulate an electrical model translated from Modelica Standar Library
logTranslation = false
removeSingularities = false
logTiming = true
Simulating model: CauerLowPassOPV
Instantiate: 16.057485 seconds (14.40 M allocations: 693.489 MiB, 1.79% gc time)
Flatten: 0.005158 seconds (9.43 k allocations: 513.828 KiB)
Consistency check: 0.000115 seconds (436 allocations: 131.125 KiB)
Pantelides: 0.001445 seconds (2.00 k allocations: 244.141 KiB)
Matching: 0.000084 seconds (469 allocations: 155.547 KiB)
Number of equations: 234
Number of variables: 240
Number of continuous states: 6
Number of non states: 4
BLT: 0.000155 seconds (1.13 k allocations: 69.391 KiB)
Symbolic processing: 0.058451 seconds (153.18 k allocations: 8.812 MiB)
Code generation and simulation:
ModiaMath: 0.443221 seconds (710.35 k allocations: 16.400 MiB)
ModiaMath: 0.061923 seconds (606.78 k allocations: 9.957 MiB)
2.782764 seconds (2.10 M allocations: 66.753 MiB, 1.36% gc time)
Total time: 19.152 seconds
final C9.v = -0.5003269853778406
Simulation OK
LinearSystems: Demonstrates type and size deduction.
logTranslation = true
Log file: /root/ModiaResults/MySISOABCD.txt
Simulating model: MySISOABCD
Number of equations: 2
Number of variables: 3
Number of continuous states: 1
final x = 0.5000001198147007
Simulation OK
storeEliminated = false
Simulating model: MyMIMOABCD
Number of equations: 5
Number of variables: 6
Number of continuous states: 1
final x = 0.004778441608750007
Simulation OK
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
SynchronousExamples: Demonstrating the ability to simulate models with synchronous semantics
storeEliminated = false
logSimulation = true
Simulating model: SpeedControl
Number of equations: 6
Number of variables: 9
Number of continuous states: 3
... ModiaMath.simulate! (version 0.5.2 2019-07-10) to simulate model: SpeedControl
Initialization at time = 0.0 s
initial values:
│ x │ name │ start │ fixed │ nominal │
├───┼────────┼─────────┼───────┼─────────┤
│ 1 │ x │ 0.0 │ 1 │ 1.0 │
│ 2 │ v │ 0.0 │ 1 │ 1.0 │
│ 3 │ fobs │ 0.0 │ 1 │ 1.0 │
in Clock, nr = 1 (isInitial)
nextEventTime = 0 s, integrateToEvent = true
in sample, nr = 1 (initialize sample store)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
Simulation started
Time event at time = 0.0 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.1 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 0.0 changed to 4.9504950495049505
restart = Restart
Time event at time = 0.1 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.2 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 499.99999999999994 changed to 497.53708727326847
restart = Restart
Time event at time = 0.2 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.3 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 251.24581460012237 changed to 250.0327282685336
restart = Restart
Time event at time = 0.30000000000000004 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.4 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 128.7240951096561 changed to 128.1266042418583
restart = Restart
Time event at time = 0.4 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.5 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 68.37751746207857 changed to 68.08325834556372
restart = Restart
Time event at time = 0.5 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.6 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 38.657346694078356 changed to 38.512391609218206
restart = Restart
Time event at time = 0.6 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.7 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 24.016883123203456 changed to 23.945525114776522
restart = Restart
Time event at time = 0.7 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.8 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 16.809724272083102 changed to 16.774558695741685
restart = Restart
Time event at time = 0.7999999999999999 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 0.9 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 13.258001061599884 changed to 13.240670823454916
restart = Restart
Time event at time = 0.8999999999999999 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 11.507647008958202 changed to 11.499100919972314
restart = Restart
Time event at time = 0.9999999999999999 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.1 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 10.644492021383485 changed to 10.64027421898659
restart = Restart
Time event at time = 1.0999999999999999 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.2 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 10.218493979297065 changed to 10.216413738546947
restart = Restart
Time event at time = 1.2 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.3 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
fobs = 10.008389663535127 changed to 10.00736376269226
restart = Restart
Time event at time = 1.3 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.4 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 1.4000000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.5 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 1.5000000000000002 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.6 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 1.6000000000000003 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.7 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 1.7000000000000004 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.8 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 1.8000000000000005 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 1.9 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 1.9000000000000006 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.0000000000000004 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.1 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.1000000000000005 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.2 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.2000000000000006 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.3 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.3000000000000007 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.4 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.400000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.5 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.500000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.6 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.600000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.7 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.700000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.8 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.800000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 2.9 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 2.9000000000000012 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.0000000000000013 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.1 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.1000000000000014 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.2 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.2000000000000015 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.3 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.3000000000000016 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.4 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.4000000000000017 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.5 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.5000000000000018 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.6 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.600000000000002 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.7 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.700000000000002 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.8 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.800000000000002 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 3.9 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 3.900000000000002 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.000000000000002 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.1 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.100000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.2 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.200000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.3 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.300000000000001 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.4 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.4 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.5 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.5 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.6 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.6 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.7 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.699999999999999 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.8 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.799999999999999 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 4.9 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 4.899999999999999 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 5 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Time event at time = 5.0 s
in Clock, nr = 1 (isEvent; clock is active)
nextEventTime = 5.1 s, integrateToEvent = true
in sample, nr = 1 (clock is active)
determine consistent DAE variables x,der(x) (with implicit Euler step; step size = 1.0e-8)
restart = Restart
Simulation is terminated at time = 5.0 s
Statistics (get help with ?ModiaMath.SimulationStatistics):
structureOfDAE = DAE_NoSpecialStructure
cpuTime = 0.22 s (init: 0.079 s, integration: 0.14 s)
startTime = 0.0 s
stopTime = 5.0 s
interval = 0.005005005005005005 s
tolerance = 0.0001
nEquations = 3
nResults = 1100
nSteps = 1408
nResidues = 4419 (includes residue calls for Jacobian)
nZeroCrossings = 0
nJac = 904
nTimeEvents = 51
nStateEvents = 0
nRestartEvents = 51
nErrTestFails = 20
h0 = 8.8e-13 s
hMin = 8.8e-13 s
hMax = 0.049 s
orderMax = 5
sparseSolver = false
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
storeEliminated = false
logSimulation = false
Simulating model: SpeedControlPI
Number of equations: 8
Number of variables: 11
Number of continuous states: 3
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
(result["v"])[end] = 100.28482401529581
ElectricalVehicleAndCharger: Demonstrates the ability to change models from Julia.
Simulating model: Charger
Number of equations: 17
Number of variables: 17
Number of continuous states: 0
Simulating model: ElectricVehicle
Number of equations: 17
Number of variables: 18
Number of continuous states: 1
Simulating model: ElectricalVehicleWithCharger
Standard Charger
Number of equations: 34
Number of variables: 35
Number of continuous states: 1
Connecting Electric Vehicle to Charger
logStatistics = false
Standard Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Disconnect
logStatistics = false
Standard Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Connecting Electric Vehicle to Charger
logStatistics = false
Super Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Disconnect
logStatistics = false
Standard Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Connecting Electric Vehicle to Charger
logStatistics = false
Standard Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Disconnect
logStatistics = false
Super Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Connecting Electric Vehicle to Charger
logStatistics = false
Super Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Disconnect
logStatistics = false
Standard Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Connecting Electric Vehicle to Charger
logStatistics = false
Standard Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Disconnect
logStatistics = false
Standard Charger
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
CollidingBalls: Demonstrating the use of allInstances to set up contact force between any number of balls
expandArrayIncidence = true
storeEliminated = false
Simulating model: Balls3
Number of equations: 24
Number of variables: 36
Number of continuous states: 12
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
Simulating model: HeatTransfer
Number of equations: 1
Number of variables: 2
Number of continuous states: 1
... ModiaMath.plot(..): Call is ignored, since PyPlot not available.
----------------------
Number of simulations OK : 6
Number of simulations NOT OK: 0
Log category statistics:
StaticModel: 1
DynamicModel: 22
CalculatedResult: 6
----------------------
Test Summary: | Pass Total
RunTests | 320 320
Testing Modia tests passed