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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### FMU の生成とシミュレーションの実行\n",
"[Functional Mock-up Interface](https://fmi-standard.org/) は、様々なダイナミックシステムのシミュレーションツール間でモデル交換やCo-Simulationを行うための規格(satandard)です。規格書は、[https://fmi-standard.org/downloads/](https://fmi-standard.org/downloads/) からダウンロードできます。\n",
"\n",
"JModelica.org を使用すると、\n",
"* FMI Version 1.0 Model Exchange (ME10)\n",
"* FMI Version 1.0 Co-Simulation (CS10)\n",
"* FMI Version 2.0 Model Exchange (ME20)\n",
"* FMI Version 2.0 Co-Simulation (CS20)\n",
"\n",
"に従った Functional Mock-up Unit (FMU) を生成することと、FMUを読み込んでシミュレーションをの実行することができます。デフォルトは ME20 です。ここでは、これらの FMU の生成とシミュレーションを行います。\n",
"\n",
"モデルは、[FMI1.0 FMI for Co-Simulationについて](https://www.amane.to/archives/73) P.22 で例題として作成したもので、元は [Modelica by Example](https://mbe.modelica.university/)の跳ね返るボールのモデル [Bouncing Ball](https://mbe.modelica.university/behavior/discrete/bouncing/) を参考にして、反発係数 $e$ を入力信号とし、$y=h$ を出力信号としたものです。これに対して上記の4種類の FMU を生成し、各FMUについて、入力信号が無い場合($e=0.8$)と、入力信号 $e$ が1秒で1.0から0.7に変化する場合の2ケースのシミュレーションを実行します。\n",
"\n",
"モデルのソースコードを作成します。"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting BouncingBall.mo\n"
]
}
],
"source": [
"%%writefile BouncingBall.mo\n",
"model BouncingBall\n",
" import SI = Modelica.SIunits;\n",
" Modelica.Blocks.Interfaces.RealInput e(start=0.8);\n",
" Modelica.Blocks.Interfaces.RealOutput y;\n",
" SI.Height h;\n",
" SI.Velocity v;\n",
" parameter SI.Acceleration g = Modelica.Constants.g_n;\n",
" parameter SI.Height h0 = 1.0;\n",
"initial equation\n",
" h = h0;\n",
"equation\n",
" v = der(h);\n",
" der(v) = -g;\n",
" y = h;\n",
" when h < 0 then\n",
" reinit(v, -e * pre(v));\n",
" end when;\n",
"end BouncingBall;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ソースコードをコンパイルして、FMIの各規格に従った4種類の FMU を作成します。"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from pymodelica import compile_fmu\n",
"fmu = compile_fmu(\"BouncingBall\",\"BouncingBall.mo\", version='1.0', target='me',compile_to = \"BouncingBallME10.fmu\")\n",
"fmu = compile_fmu(\"BouncingBall\",\"BouncingBall.mo\", version='2.0', target='me',compile_to = \"BouncingBallME20.fmu\")\n",
"fmu = compile_fmu(\"BouncingBall\",\"BouncingBall.mo\", version='1.0', target='cs',compile_to = \"BouncingBallCS10.fmu\")\n",
"fmu = compile_fmu(\"BouncingBall\",\"BouncingBall.mo\", version='2.0', target='cs',compile_to = \"BouncingBallCS20.fmu\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"入力信号を表す関数を作成します。 "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def stepf(t):\n",
" startTime = 1.0\n",
" if t < startTime:\n",
" return 1.0\n",
" else:\n",
" return 0.7"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"図化のために Matplotlib をインポートします。"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"from pyfmi import load_fmu"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"4種類のFMUについて、入力信号がない場合とある場合の2ケースのシミュレーションを実行し、結果の入力信号$e$と出力信号$y$をプロットします。"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"1 BouncingBallME10.fmu\n",
"Final Run Statistics: BouncingBallME10 \n",
"\n",
" Number of steps : 106\n",
" Number of function evaluations : 164\n",
" Number of Jacobian evaluations : 13\n",
" Number of function eval. due to Jacobian eval. : 26\n",
" Number of error test failures : 0\n",
" Number of nonlinear iterations : 112\n",
" Number of nonlinear convergence failures : 0\n",
" Number of state function evaluations : 231\n",
" Number of state events : 12\n",
"\n",
"Solver options:\n",
"\n",
" Solver : CVode\n",
" Linear multistep method : BDF\n",
" Nonlinear solver : Newton\n",
" Linear solver type : DENSE\n",
" Maximal order : 5\n",
" Tolerances (absolute) : 1e-06\n",
" Tolerances (relative) : 0.0001\n",
"\n",
"Simulation interval : 0.0 - 3.0 seconds.\n",
"Elapsed simulation time: 0.0217440128326 seconds.\n",
"Final Run Statistics: BouncingBallME10 \n",
"\n",
" Number of steps : 106\n",
" Number of function evaluations : 166\n",
" Number of Jacobian evaluations : 13\n",
" Number of function eval. due to Jacobian eval. : 26\n",
" Number of error test failures : 1\n",
" Number of nonlinear iterations : 114\n",
" Number of nonlinear convergence failures : 0\n",
" Number of state function evaluations : 239\n",
" Number of state events : 12\n",
"\n",
"Solver options:\n",
"\n",
" Solver : CVode\n",
" Linear multistep method : BDF\n",
" Nonlinear solver : Newton\n",
" Linear solver type : DENSE\n",
" Maximal order : 5\n",
" Tolerances (absolute) : 1e-06\n",
" Tolerances (relative) : 0.0001\n",
"\n",
"Simulation interval : 0.0 - 3.0 seconds.\n",
"Elapsed simulation time: 0.0366840362549 seconds.\n",
"\n",
"\n",
"2 BouncingBallME20.fmu\n",
"Final Run Statistics: --- \n",
"\n",
" Number of steps : 106\n",
" Number of function evaluations : 164\n",
" Number of Jacobian evaluations : 13\n",
" Number of function eval. due to Jacobian eval. : 26\n",
" Number of error test failures : 0\n",
" Number of nonlinear iterations : 112\n",
" Number of nonlinear convergence failures : 0\n",
" Number of state function evaluations : 231\n",
" Number of state events : 12\n",
"\n",
"Solver options:\n",
"\n",
" Solver : CVode\n",
" Linear multistep method : BDF\n",
" Nonlinear solver : Newton\n",
" Linear solver type : DENSE\n",
" Maximal order : 5\n",
" Tolerances (absolute) : 1e-06\n",
" Tolerances (relative) : 0.0001\n",
"\n",
"Simulation interval : 0.0 - 3.0 seconds.\n",
"Elapsed simulation time: 0.0213289260864 seconds.\n",
"Final Run Statistics: --- \n",
"\n",
" Number of steps : 106\n",
" Number of function evaluations : 166\n",
" Number of Jacobian evaluations : 13\n",
" Number of function eval. due to Jacobian eval. : 26\n",
" Number of error test failures : 1\n",
" Number of nonlinear iterations : 114\n",
" Number of nonlinear convergence failures : 0\n",
" Number of state function evaluations : 239\n",
" Number of state events : 12\n",
"\n",
"Solver options:\n",
"\n",
" Solver : CVode\n",
" Linear multistep method : BDF\n",
" Nonlinear solver : Newton\n",
" Linear solver type : DENSE\n",
" Maximal order : 5\n",
" Tolerances (absolute) : 1e-06\n",
" Tolerances (relative) : 0.0001\n",
"\n",
"Simulation interval : 0.0 - 3.0 seconds.\n",
"Elapsed simulation time: 0.0345189571381 seconds.\n",
"\n",
"\n",
"3 BouncingBallCS10.fmu\n",
"Simulation interval : 0.0 - 3.0 seconds.\n",
"Elapsed simulation time: 0.0159890651703 seconds.\n",
"Simulation interval : 0.0 - 3.0 seconds.\n",
"Elapsed simulation time: 0.0230710506439 seconds.\n",
"\n",
"\n",
"4 BouncingBallCS20.fmu\n",
"Simulation interval : 0.0 - 3.0 seconds.\n",
"Elapsed simulation time: 0.0138819217682 seconds.\n",
"Simulation interval : 0.0 - 3.0 seconds.\n",
"Elapsed simulation time: 0.0238161087036 seconds.\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f148fd75550>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f148fd69f50>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f148f77dc50>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f148f7688d0>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"names = [\"BouncingBallME10.fmu\",\"BouncingBallME20.fmu\",\"BouncingBallCS10.fmu\",\"BouncingBallCS20.fmu\"]\n",
"i = 0\n",
"for name in names:\n",
" i += 1\n",
" print('\\n')\n",
" print('%d %s' % (i, name))\n",
" # Simulate without input signal\n",
" model = load_fmu(name)\n",
" opts = model.simulate_options()\n",
" opts[\"ncp\"]=500\n",
" res1 = model.simulate(final_time = 3, options=opts)\n",
" # Simulate with input signal\n",
" model = load_fmu(name)\n",
" opts = model.simulate_options()\n",
" opts[\"ncp\"]=500\n",
" res2 = model.simulate(final_time = 3, options=opts, input=(\"e\",stepf)) \n",
" # Plot simulation results\n",
" plt.figure(i)\n",
" plt.subplot(2,1,1)\n",
" plt.title(name)\n",
" plt.plot(res1[\"time\"], res1[\"e\"],res1[\"time\"],res1[\"y\"])\n",
" plt.legend([\"e\",\"y\"])\n",
" plt.subplot(2,1,2) \n",
" plt.plot(res2[\"time\"], res2[\"e\"],res2[\"time\"],res2[\"y\"])\n",
" plt.legend([\"e\",\"y\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.17"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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finback-at commented Mar 26, 2020

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