Files
Python-DT_Slot_3/e-RK4-Test.ipynb
2022-06-14 14:25:51 +02:00

94 lines
13 KiB
Plaintext

{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from Ausgleichsbecken_class_file import Ausgleichsbecken_class\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.707892426750419\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"t_max = 0.8\n",
"V = Ausgleichsbecken_class(1.,0.5,0.,10.,timestep=0.001)\n",
"t_vec = np.arange(0,t_max,V.timestep)\n",
"\n",
"V.set_initial_level(5.) \n",
"V.set_influx(0.)\n",
"V.set_outflux(0.)\n",
"\n",
"V.p0 = 0.\n",
"\n",
"outflux_vec = np.zeros_like(t_vec)\n",
"level_vec = np.zeros_like(t_vec)\n",
"\n",
"for i in range(np.size(t_vec)):\n",
" t = t_vec[i]\n",
" V.e_RK_4()\n",
" V.level = V.update_level(V.timestep)\n",
" V.set_volume()\n",
" outflux_vec[i] = V.outflux\n",
" level_vec[i] = V.level\n",
" \n",
"plt.plot(level_vec)\n",
"print(level_vec[-1])\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.8.13 ('Georg_DT_Slot3')",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.13"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}