224 lines
34 KiB
Plaintext
224 lines
34 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from Ausgleichsbecken_class_file import Ausgleichsbecken_class\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"# define constants\n",
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"initial_level = 5.\n",
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"initial_influx = 3. \n",
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"initial_outflux = 0. \n",
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"initial_pipeline_pressure = 0. \n",
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"\n",
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"total_min_level = 0.01\n",
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"total_max_time = 100\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x15d7c3b7970>]"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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sb8j3Gcrg7njJlk284cIz4cJBVyJJp49H7yQpMwa3JGXG4JakzBjckpQZg1uSMmNwS1JmDG5JyozBLUmZ6csl7xExDZzqzbF3AaP2HDLXPPxGbb3gmk/WK1JKPT2Fpi/B/WJExFSv1+sPC9c8/EZtveCa+8lRiSRlxuCWpMyUMbhvHHQBA+Cah9+orRdcc9+UbsYtSTqxMnbckqQTMLglKTOlCe6IuDoiHoyIhyPiukHXczIi4pyIuD0i9kbE/RHxvmL7zoj4dkQ8VPy+o+tzri/W+mBE/GHX9tdGxI+Lj30siuepRcSmiPhSsf3OiNiz4QtdQ0RUI+KeiLileD/Ua46I7RFxc0Q8UPx9XzYCa/5A8e/6voj4QkQ0h23NEfHpiNgfEfd1bduQNUbENcX3eCgirump4JTSwH8BVeBnwPlAA/ghcNGg6zqJ+ncDlxSvtwI/BS4C/hm4rth+HfBPxeuLijVuAs4r1l4tPnYXcBntRzV+A/ijYvtfAf9WvH4H8KVBr7uo5e+AzwO3FO+Hes3AZ4G/KF43gO3DvGbgLOBRYKx4/2XgPcO2ZuAK4BLgvq5tfV8jsBN4pPh9R/F6x7r1Dvo/hKL4y4Bbu95fD1w/6LpexHr+G/gD4EFgd7FtN/DgWusDbi3+DHYDD3RtfydwQ/c+xesa7auzYsDrPBu4DbiS5eAe2jUD22iHWKzaPsxrPgt4vAiWGnALcNUwrhnYw8rg7vsau/cpPnYD8M71ai3LqKTzj6NjX7EtO8WPQBcDdwIvTSk9CVD8fmax2wut96zi9ertKz4npbQAPAe8pC+L6N1HgQ8Cra5tw7zm84Fp4DPFeOiTEbGZIV5zSumXwL8AvwCeBJ5LKX2LIV5zl41Y4yllX1mCO9bYlt15ihGxBfgK8P6U0vMn2nWNbekE20/0OQMREW8F9qeU7u71U9bYltWaaXdKlwCfSCldDMzQ/hH6hWS/5mKu+8e0RwIvBzZHxLtO9ClrbMtqzT04nWs8pbWXJbj3Aed0vT8beGJAtZySiKjTDu2bUkpfLTb/KiJ2Fx/fDewvtr/QevcVr1dvX/E5EVEDzgCePf0r6dnlwNsi4jHgi8CVEfE5hnvN+4B9KaU7i/c30w7yYV7zm4BHU0rTKaV54KvA7zLca+7YiDWeUvaVJbh/AFwQEedFRIP28P7rA66pZ8WR408Be1NKH+n60NeBzlHia2jPvjvb31EcaT4PuAC4q/hx7FBEXFp8zT9d9Tmdr/V24DupGIoNQkrp+pTS2SmlPbT/vr6TUnoXw73mp4DHI+LCYtMbgZ8wxGumPSK5NCLGi1rfCOxluNfcsRFrvBW4KiJ2FD/dXFVsO7GNPgBwggMDb6Z9NsbPgA8Nup6TrP31tH+8+RFwb/HrzbRnWLcBDxW/7+z6nA8Va32Q4shzsX0SuK/42MdZvrq1Cfwn8DDtI9fnD3rdXTW/geWDk0O9ZuA1wFTxd/1ftM8EGPY1fxh4oKj3P2ifTTFUawa+QHuGP0+7C/7zjVoj8GfF9oeB9/ZSr5e8S1JmyjIqkST1yOCWpMwY3JKUGYNbkjJjcEtSZgxuScqMwS1Jmfl/L1mIquoTiucAAAAASUVORK5CYII=",
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"V = Ausgleichsbecken_class(1.,0.5,0.,10.,timestep=0.001)\n",
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"V.set_initial_level(initial_level) \n",
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"V.set_influx(initial_influx)\n",
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"V.set_outflux(initial_outflux)\n",
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"\n",
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"V.p0 = initial_pipeline_pressure\n",
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"\n",
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"outflux_vec = []\n",
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"level_vec = []\n",
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"\n",
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"t = 0\n",
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"while V.level > total_min_level:\n",
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" t = t+V.timestep\n",
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" V.e_RK_4()\n",
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" V.level = V.update_level(V.timestep)\n",
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" V.set_volume()\n",
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" outflux_vec.append(V.outflux)\n",
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" level_vec.append(V.level)\n",
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" if t > total_max_time:\n",
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" break\n",
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"\n",
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"plt.plot(level_vec)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x15d7c7a4220>]"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"V = Ausgleichsbecken_class(1.,0.1,0.,10.,timestep=0.001)\n",
|
|
"\n",
|
|
"V.set_initial_level(initial_level) \n",
|
|
"V.set_influx(initial_influx)\n",
|
|
"V.set_outflux(initial_outflux)\n",
|
|
"\n",
|
|
"V.p0 = initial_pipeline_pressure\n",
|
|
"\n",
|
|
"outflux_vec = []\n",
|
|
"level_vec = []\n",
|
|
"\n",
|
|
"t = 0\n",
|
|
"while V.level > total_min_level:\n",
|
|
" t = t+V.timestep\n",
|
|
" V.e_RK_4()\n",
|
|
" V.level = V.update_level(V.timestep)\n",
|
|
" V.set_volume()\n",
|
|
" outflux_vec.append(V.outflux)\n",
|
|
" level_vec.append(V.level)\n",
|
|
" if t > total_max_time:\n",
|
|
" break\n",
|
|
"\n",
|
|
"plt.plot(level_vec)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[<matplotlib.lines.Line2D at 0x15d7c7ec1f0>]"
|
|
]
|
|
},
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"V = Ausgleichsbecken_class(1.,0.9,0.,10.,timestep=0.001)\n",
|
|
"\n",
|
|
"V.set_initial_level(initial_level) \n",
|
|
"V.set_influx(initial_influx)\n",
|
|
"V.set_outflux(initial_outflux)\n",
|
|
"\n",
|
|
"V.p0 = initial_pipeline_pressure\n",
|
|
"\n",
|
|
"outflux_vec = []\n",
|
|
"level_vec = []\n",
|
|
"\n",
|
|
"t = 0\n",
|
|
"while V.level > total_min_level:\n",
|
|
" t = t+V.timestep\n",
|
|
" V.e_RK_4()\n",
|
|
" V.level = V.update_level(V.timestep)\n",
|
|
" V.set_volume()\n",
|
|
" outflux_vec.append(V.outflux)\n",
|
|
" level_vec.append(V.level)\n",
|
|
" if t > total_max_time:\n",
|
|
" break\n",
|
|
"\n",
|
|
"plt.plot(level_vec)\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"interpreter": {
|
|
"hash": "4a28055eb8a3160fa4c7e4fca69770c4e0a1add985300856aa3fcf4ce32a2c48"
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python 3.8.13 ('DT_Slot_3')",
|
|
"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
|
|
}
|