From 5bb223fedb8e97003daa1131d4741b809d8ebdbc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Georg=20=C2=B4Brantegger?= Date: Wed, 15 Jun 2022 10:15:18 +0200 Subject: [PATCH] looks like e-RK4 is working :D --- Ausgleichsbecken_class_file.py | 2 +- e-RK4-Test.ipynb | 139 ++++++++++++++++++++++++++++----- 2 files changed, 120 insertions(+), 21 deletions(-) diff --git a/Ausgleichsbecken_class_file.py b/Ausgleichsbecken_class_file.py index 8f0b24f..781cb51 100644 --- a/Ausgleichsbecken_class_file.py +++ b/Ausgleichsbecken_class_file.py @@ -72,7 +72,7 @@ class Ausgleichsbecken_class: dt = self.timestep p = self.p0 p_hs = self.p0 - alpha = (self.area/self.area_outflux-1) + alpha = (self.area_outflux/self.area-1) h_hs = self.update_level(dt/2) Y1 = yn Y2 = yn + dt/2*FODE_function(Y1, h, alpha, self.p0) diff --git a/e-RK4-Test.ipynb b/e-RK4-Test.ipynb index bf6414c..147f48f 100644 --- a/e-RK4-Test.ipynb +++ b/e-RK4-Test.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -13,19 +13,63 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 7, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.707892426750419\n" + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32mv:\\georg\\Documents\\Persönliche Dokumente\\Arbeit\\Kelag\\Coding\\Python\\DT_Slot_3\\Kelag_DT_Slot_3\\e-RK4-Test.ipynb Cell 2'\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[39mwhile\u001b[39;00m V\u001b[39m.\u001b[39mlevel \u001b[39m>\u001b[39m \u001b[39m0.005\u001b[39m:\n\u001b[0;32m 13\u001b[0m t \u001b[39m=\u001b[39m t\u001b[39m+\u001b[39mV\u001b[39m.\u001b[39mtimestep\n\u001b[1;32m---> 14\u001b[0m V\u001b[39m.\u001b[39;49me_RK_4()\n\u001b[0;32m 15\u001b[0m V\u001b[39m.\u001b[39mlevel \u001b[39m=\u001b[39m V\u001b[39m.\u001b[39mupdate_level(V\u001b[39m.\u001b[39mtimestep)\n\u001b[0;32m 16\u001b[0m V\u001b[39m.\u001b[39mset_volume()\n", + "File \u001b[1;32mv:\\georg\\Documents\\Persönliche Dokumente\\Arbeit\\Kelag\\Coding\\Python\\DT_Slot_3\\Kelag_DT_Slot_3\\Ausgleichsbecken_class_file.py:81\u001b[0m, in \u001b[0;36mAusgleichsbecken_class.e_RK_4\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 79\u001b[0m Y3 \u001b[39m=\u001b[39m yn \u001b[39m+\u001b[39m dt\u001b[39m/\u001b[39m\u001b[39m2\u001b[39m\u001b[39m*\u001b[39mFODE_function(Y2, h_hs, alpha, p_hs)\n\u001b[0;32m 80\u001b[0m Y4 \u001b[39m=\u001b[39m yn \u001b[39m+\u001b[39m dt\u001b[39m*\u001b[39mFODE_function(Y3, h_hs, alpha, p_hs)\n\u001b[1;32m---> 81\u001b[0m ynp1 \u001b[39m=\u001b[39m yn \u001b[39m+\u001b[39m dt\u001b[39m/\u001b[39m\u001b[39m6\u001b[39m\u001b[39m*\u001b[39m(FODE_function(Y1, h, alpha, p)\u001b[39m+\u001b[39m\u001b[39m2\u001b[39;49m\u001b[39m*\u001b[39;49mFODE_function(Y2, h_hs, alpha, p_hs)\u001b[39m+\u001b[39m \\\n\u001b[0;32m 82\u001b[0m \u001b[39m2\u001b[39m\u001b[39m*\u001b[39mFODE_function(Y3, h_hs, alpha, p_hs)\u001b[39m+\u001b[39m FODE_function(Y4, h, alpha, p))\n\u001b[0;32m 84\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moutflux \u001b[39m=\u001b[39m ynp1\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39marea_outflux\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] + } + ], + "source": [ + "V = Ausgleichsbecken_class(1.,0.5,0.,10.,timestep=0.001)\n", + "V.set_initial_level(5.) \n", + "V.set_influx(1.)\n", + "V.set_outflux(0.)\n", + "\n", + "V.p0 = 0.\n", + "\n", + "outflux_vec = []\n", + "level_vec = []\n", + "\n", + "t = 0\n", + "while V.level > 0.005:\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", + " \n", + "plt.plot(level_vec)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -37,35 +81,90 @@ } ], "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", + "V = Ausgleichsbecken_class(1.,0.1,0.,10.,timestep=0.001)\n", "\n", "V.set_initial_level(5.) \n", - "V.set_influx(0.)\n", + "V.set_influx(0.75)\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", + "outflux_vec = []\n", + "level_vec = []\n", "\n", - "for i in range(np.size(t_vec)):\n", - " t = t_vec[i]\n", + "t = 0\n", + "while V.level > 0.01:\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[i] = V.outflux\n", - " level_vec[i] = V.level\n", + " outflux_vec.append(V.outflux)\n", + " level_vec.append(V.level)\n", + " if t > 100:\n", + " break\n", " \n", - "plt.plot(level_vec)\n", - "print(level_vec[-1])\n" + "plt.plot(level_vec)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "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(10.) \n", + "V.set_influx(3.2)\n", + "V.set_outflux(0.)\n", + "\n", + "V.p0 = 0.\n", + "\n", + "outflux_vec = []\n", + "level_vec = []\n", + "\n", + "t = 0\n", + "while V.level > 0.005:\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", + " \n", + "plt.plot(level_vec)\n" ] } ], "metadata": { + "interpreter": { + "hash": "4a28055eb8a3160fa4c7e4fca69770c4e0a1add985300856aa3fcf4ce32a2c48" + }, "kernelspec": { - "display_name": "Python 3.8.13 ('Georg_DT_Slot3')", + "display_name": "Python 3.8.13 ('DT_Slot_3')", "language": "python", "name": "python3" },