EX10-jupyter-notebook-c++2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--- ![tu-dresden-blue.png](attachment:tu-dresden-blue.png) --->\n",
    "<img src=\"tu-dresden-blue.png\" alt=\"drawing\" width=\"200\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Professur für Angewandte Umweltsystemanalyse an der TU Dresden <br/>\n",
    "Prof. Dr.-Ing. habil. Olaf Kolditz <br/>\n",
    "***Hydroinformatik II (HyBHW-2-01)*** <br/>\n",
    "*[Lehre-Webseite](https://www.ufz.de/index.php?de=40425)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Exercise EX10 - Open channel flow*** <br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello Jupyter!\n",
      "This is Hydroinformatics II 2021: EX10 (open channel flow) on Jupter\n"
     ]
    }
   ],
   "source": [
    "#include <iostream>\n",
    "using namespace std;\n",
    "cout << \"Hello Jupyter!\" << endl;\n",
    "std::cout << \"This is Hydroinformatics II 2021: EX10 (open channel flow) on Jupter\" << std::endl;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1"
     ]
    }
   ],
   "source": [
    "#include <vector>\n",
    "#include <iostream>\n",
    "#include <fstream>\n",
    "using namespace std;\n",
    "int n=11;\n",
    "vector<double> u_new, u_old;\n",
    "ofstream out_file;\n",
    "out_file.open(\"out.txt\");\n",
    "ofstream out_python;\n",
    "out_python.open(\"out.csv\");\n",
    "//out_file.precision(3);\n",
    "u_new.resize(n);\n",
    "u_old.resize(n);\n",
    "double x[11];\n",
    "for(int i=0;i<n;i++)\n",
    "  x[i] = -100. + i*10.;\n",
    "double bottom_elevation[11];\n",
    "for(int i=0;i<n;i++)\n",
    "  bottom_elevation[i] = 0.04 - i*0.004;\n",
    "for(int i=0;i<n;i++)\n",
    "{\n",
    "  u_old[i] = 0.25;\n",
    "}\n",
    "// Randbedingungen\n",
    "u_old[10] = 0.1; // Wasserstand flußabwärts [m]\n",
    "u_new[10] = 0.1; // Wasserstand flußabwärts [m]\n",
    "cout << u_old[10];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "// Parameter (konstant)\n",
    "double discharge = 0.05; // Volumenfließrate [m3/s]\n",
    "double gravity = 9.81; // [m/s2]\n",
    "double friction_law_exponent = 0.5; // Chezy, Manning-Strickler [-]\n",
    "double error_tolerance = 1e-3; // [m]\n",
    "double bed_slope = 0.0004; // [m/m]\n",
    "double bottom_width = 1.; // [m]\n",
    "double m = 1.; //\n",
    "double friction_coefficient = 10.; //\n",
    "// Parameter (variabel)\n",
    "double wetted_cross_section[11];\n",
    "double water_level_elevation[11];\n",
    "double flow_velocity[11];\n",
    "double Froude_number[11];\n",
    "double wetted_perimeter[11];\n",
    "double hydraulic_radius[11];\n",
    "double friction_slope[11];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Newton error: 0.0759657"
     ]
    }
   ],
   "source": [
    "for(int i=0;i<n;i++)\n",
    "{\n",
    "  wetted_perimeter[i] = bottom_width + 2.*sqrt(1.+m*m)*u_old[i];\n",
    "  wetted_cross_section[i] = (bottom_width + m*u_old[i])*u_old[i];\n",
    "  hydraulic_radius[i] = wetted_cross_section[i] / wetted_perimeter[i];\n",
    "  water_level_elevation[i] = bottom_elevation[i] + u_old[i];\n",
    "  flow_velocity[i] = discharge/wetted_cross_section[i];\n",
    "  Froude_number[i] = flow_velocity[i]/(sqrt(gravity*wetted_cross_section[i]\\\n",
    "                   /sqrt(bottom_width*bottom_width+4.*m*wetted_cross_section[i])));\n",
    "  friction_slope[i] = pow(flow_velocity[i]/(friction_coefficient*pow(hydraulic_radius[i],friction_law_exponent)),2);\n",
    "}\n",
    "// Berechnungsgrößen\n",
    "double N,N1,N2,N3,D,D1,D2,D21,D22;\n",
    "double error;\n",
    "for(int i=0;i<n-1;i++)\n",
    "{\n",
    "  N1 = pow(discharge,2)/pow(wetted_cross_section[i+1],2) + gravity*u_old[i+1];\n",
    "  N2 = pow(discharge,2)/pow(wetted_cross_section[i],2) + gravity*u_old[i];\n",
    "  N3 = gravity*(bed_slope - (friction_slope[i+1]+friction_slope[i])/2.)*(x[i+1]-x[i]);\n",
    "  N = N1 - N2 - N3;\n",
    "  D1 = pow(discharge,2)/pow(wetted_cross_section[i],3) * (bottom_width+2.*m*u_old[i]) - gravity;\n",
    "  D21 = friction_law_exponent*2.*(sqrt(1+m*m))/wetted_perimeter[i];\n",
    "  D22 = (1.+friction_law_exponent)/wetted_cross_section[i] * (bottom_width+2.*m*u_old[i]);\n",
    "  D2 = gravity*friction_slope[i]*(D21-D22)*(x[i+1]-x[i]);\n",
    "  D = D1 + D2;\n",
    "  u_new[i] = u_old[i] - N/D;\n",
    "}\n",
    "//calc Newton error\n",
    "for(int i=0;i<n-1;i++)\n",
    "{\n",
    "  error += u_old[i] -u_new[i];\n",
    "}\n",
    "error = sqrt(error*error);\n",
    "cout << \"Newton error: \" << error;\n",
    "//save Newton step\n",
    "for(int i=0;i<n-1;i++)\n",
    "{\n",
    "  u_old[i] = u_new[i];\n",
    "}\n",
    "// Berechnung (1. Iteration des Newton-Verfahrens)\n",
    "// Ausgabe der Ergebnisse\n",
    "// File \n",
    "out_file << \"Water depth (new):\\t\";\n",
    "for(int i=0;i<n;i++)\n",
    "{\n",
    "  out_file << \"\\t\" << u_new[i] << \" \";\n",
    "}\n",
    "out_file << endl;\n",
    "// für Python plot\n",
    "for(int i=0;i<n;i++)\n",
    "{\n",
    "  out_python << x[i] << \",\" << u_new[i] << endl;\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Newton error: 0.0186149"
     ]
    }
   ],
   "source": [
    "for(int i=0;i<n;i++)\n",
    "{\n",
    "  wetted_perimeter[i] = bottom_width + 2.*sqrt(1.+m*m)*u_old[i];\n",
    "  wetted_cross_section[i] = (bottom_width + m*u_old[i])*u_old[i];\n",
    "  hydraulic_radius[i] = wetted_cross_section[i] / wetted_perimeter[i];\n",
    "  water_level_elevation[i] = bottom_elevation[i] + u_old[i];\n",
    "  flow_velocity[i] = discharge/wetted_cross_section[i];\n",
    "  Froude_number[i] = flow_velocity[i]/(sqrt(gravity*wetted_cross_section[i]\\\n",
    "                   /sqrt(bottom_width*bottom_width+4.*m*wetted_cross_section[i])));\n",
    "  friction_slope[i] = pow(flow_velocity[i]/(friction_coefficient*pow(hydraulic_radius[i],friction_law_exponent)),2);\n",
    "}\n",
    "// Berechnungsgrößen\n",
    "double N,N1,N2,N3,D,D1,D2,D21,D22;\n",
    "for(int i=0;i<n-1;i++)\n",
    "{\n",
    "  N1 = pow(discharge,2)/pow(wetted_cross_section[i+1],2) + gravity*u_old[i+1];\n",
    "  N2 = pow(discharge,2)/pow(wetted_cross_section[i],2) + gravity*u_old[i];\n",
    "  N3 = gravity*(bed_slope - (friction_slope[i+1]+friction_slope[i])/2.)*(x[i+1]-x[i]);\n",
    "  N = N1 - N2 - N3;\n",
    "  D1 = pow(discharge,2)/pow(wetted_cross_section[i],3) * (bottom_width+2.*m*u_old[i]) - gravity;\n",
    "  D21 = friction_law_exponent*2.*(sqrt(1+m*m))/wetted_perimeter[i];\n",
    "  D22 = (1.+friction_law_exponent)/wetted_cross_section[i] * (bottom_width+2.*m*u_old[i]);\n",
    "  D2 = gravity*friction_slope[i]*(D21-D22)*(x[i+1]-x[i]);\n",
    "  D = D1 + D2;\n",
    "  u_new[i] = u_old[i] - N/D;\n",
    "}\n",
    "//calc Newton error\n",
    "for(int i=0;i<n-1;i++)\n",
    "{\n",
    "  error += u_old[i] -u_new[i];\n",
    "}\n",
    "error = sqrt(error*error);\n",
    "cout << \"Newton error: \" << error;\n",
    "//save Newton step\n",
    "for(int i=0;i<n-1;i++)\n",
    "{\n",
    "  u_old[i] = u_new[i];\n",
    "}\n",
    "// Berechnung (2. Iteration des Newton-Verfahrens)\n",
    "// Ausgabe der Ergebnisse\n",
    "// File \n",
    "out_file << \"Water depth (new):\\t\";\n",
    "for(int i=0;i<n;i++)\n",
    "{\n",
    "  out_file << \"\\t\" << u_new[i] << \" \";\n",
    "}\n",
    "out_file << endl;\n",
    "// für Python plot\n",
    "for(int i=0;i<n;i++)\n",
    "{\n",
    "  out_python << x[i] << \",\" << u_new[i] << endl;\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_file.close();\n",
    "out_python.close();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import csv\n",
    "x = []\n",
    "y = []\n",
    "with open('out.csv','r') as csvfile:\n",
    "    plots = csv.reader(csvfile, delimiter=',')\n",
    "    for row in plots:\n",
    "        x.append(float(row[0]))\n",
    "        y.append(float(row[1]))\n",
    "plt.plot(x,y, label='Data loaded from file: out.cvs')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('Hydroinformatics II (Olaf Kolditz)\\nExercise BHYWI-08-11-for-python\\nNewton-Verfahren Gerinnehydraulik')\n",
    "plt.legend()\n",
    "plt.savefig(\"test1.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}