{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Beispiel 18.5: Gas-Flüssig-Reaktion 2. Ordnung in einem Rührkesselreaktor\n",
    "Bearbeitet von Franz Braun\n",
    "\n",
    "Dieses Beispiel befindet sich im Lehrbuch auf den Seiten 287 - 289. Die Nummerierung\n",
    "der verwendeten Gleichungen entspricht der Nummerierung im Lehrbuch. Das hier angewendete\n",
    "Vorgehen entspricht dem im Lehrbuch vorgestellten Lösungsweg.\n",
    "\n",
    "Zunächst werden die benötigten Pakete importiert."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Import\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.integrate import solve_bvp\n",
    "from scipy.optimize import root\n",
    "from tabulate import tabulate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die Reaktionskinetik für die betrachtete Reaktion ($\\mathrm{A_1} +\\mathrm{A_2} \\rightarrow \\mathrm{A_3}$) ist geben als:\n",
    "\n",
    "\\begin{align*}\n",
    "r_\\mathrm{L} = k\\, c_{c_1,\\mathrm{L}} \\, c_{c_1,\\mathrm{L}}.\n",
    "\\end{align*}\n",
    "\n",
    "Für die Komponenten $\\mathrm{A_1}$, $\\mathrm{A_2}$ und $\\mathrm{A_3}$ ergeben sich die stationären, dimensionslosen Materialbilanzen für eine Reaktion 2. Ordnung im flüssigkeitsseitigen Grenzfilm (Mesoskala) und für $D_\\mathrm{1,L} = D_\\mathrm{2,L} = D_\\mathrm{3,L}$ aus Gleichung 18.15a zu:\n",
    "\n",
    "\\begin{align*}\n",
    "\\frac{\\mathrm{d} f_\\mathrm{1,L}^2}{\\mathrm{d}^2\\chi} &=  Ha^2 f_\\mathrm{1,L}\\, f_\\mathrm{1,L},\\\\\n",
    "\\frac{\\mathrm{d} f_\\mathrm{2,L}^2}{\\mathrm{d}^2\\chi} &=  Ha^2 f_\\mathrm{1,L}\\, f_\\mathrm{1,L},\\\\\n",
    "\\frac{\\mathrm{d} f_\\mathrm{3,L}^2}{\\mathrm{d}^2\\chi} &= -Ha^2 f_\\mathrm{1,L}\\, f_\\mathrm{1,L}.\n",
    "\\end{align*}\n",
    "\n",
    "Die Materialbilanzen werden in folgender Funktion implementiert."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def material(X, y):\n",
    "    '''\n",
    "    Dimensionslose Materialbilanz (Mesoskala) für die Reaktion 2.Ordnung (Gleichung 18.15a)\n",
    "    y[0]       : Restanteil von A1\n",
    "    y[1]       : Restanteil von A2\n",
    "    y[2]       : Restanteil von A3\n",
    "    y[3]       : erste Ableitung des Restanteils von A1 nach der dimensionslosen Ortskoordinate\n",
    "    y[4]       : erste Ableitung des Restanteils von A2 nach der dimensionslosen Ortskoordinate   \n",
    "    y[5]       : erste Ableitung des Restanteils von A3 nach der dimensionslosen Ortskoordinate\n",
    "    dy2dX2     : zweite Ableitung der Restanteile von nach der dimensionslosen Ortskoordinate (Vektor der Größe 2)\n",
    "    X          : Ortskoordinate\n",
    "    '''\n",
    "    f = y[:3]  # Vektor für die Restanteile von A1, A2 und A2 \n",
    "\n",
    "    dy2dX2 = np.empty_like(f)\n",
    "\n",
    "    dy2dX2[0] =   Ha**2 * f[0] * f[1]\n",
    "    dy2dX2[1] =   Ha**2 * f[0] * f[1]\n",
    "    dy2dX2[2] = - Ha**2 * f[0] * f[1]\n",
    "    \n",
    "\n",
    "    return np.vstack((y[3:],dy2dX2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die dazugehörigen Randbedingungen an der Grenzfläche zwischen flüssigkeitsseitigem Grenzfilm und Kernströmung, also für $\\chi = 1$,\n",
    "lauten unter der Annahme $D_\\mathrm{1,L} = D_\\mathrm{2,L} = D_\\mathrm{3,L}$ (s. Gleichung 18.17 und Tabelle 18.3):\n",
    "\n",
    "\\begin{align*}\n",
    "f_\\mathrm{1,L}(\\chi = 1) & = f_\\mathrm{1,L,b},\\\\\n",
    "f_\\mathrm{2,L}(\\chi = 1) & = f_\\mathrm{2,L,b},\\\\\n",
    "f_\\mathrm{3,L}(\\chi = 1) & = f_\\mathrm{3,L,b}.\n",
    "\\end{align*}\n",
    "\n",
    "An der Stelle $\\chi = 0$ (Phasengrenzfläche) werden gem. Gleichung 18.16 folgende Randbedingungen angesetzt (s. Tabelle 18.3 und Erläuterung Lehrbuchtext zu Beispiel 18.5):\n",
    "\n",
    "\\begin{align*}\n",
    "f_\\mathrm{1,L}(\\chi = 0) & = f_\\mathrm{1,G,b} = 1,\\\\\n",
    "\\frac{\\mathrm{d} f_\\mathrm{2,L}}{\\mathrm{d}\\chi} \\Big \\vert_{\\chi = 0}\\normalsize & = 0,\\\\\n",
    "\\frac{\\mathrm{d} f_\\mathrm{3,L}}{\\mathrm{d}\\chi} \\Big \\vert_{\\chi = 0}\\normalsize & = 0.\n",
    "\\end{align*}\n",
    "\n",
    "Für die Berechnung der erforderlichen Restanteile von $A_1$, $A_2$  und $A_3$ in der Kernströmung der Flüssigkeit $f_{i,\\mathrm{L,b}}$ werden die stationären Bilanzgleichungen auf der Reaktorskala (Mesoskala) auf Basis von Gleichung 18.38b formuliert. Es wird angenommen, dass $c_\\mathrm{1,L,e} = c_\\mathrm{3,L,e} = 0$. Das Einsatzverhältnis ist $\\kappa_\\mathrm{2,L} = 2$.  Es ergibt sich:\n",
    "\n",
    "\\begin{align*}\n",
    "0 &= - f_\\mathrm{1,L,b} - \\frac{Da_\\mathrm{I}}{Hi \\, Ha^2} \\frac{\\mathrm{d} f_\\mathrm{1,L}}{\\mathrm{d}\\chi} \\Big \\vert_{\\chi = 1} \\normalsize - Da_\\mathrm{I} \\frac{Hi - 1 }{Hi} f_\\mathrm{1,L,b}\\,f_\\mathrm{2,L,b},\\\\\n",
    "0 &= \\kappa_\\mathrm{2,L} - f_\\mathrm{2,L,b} - \\frac{Da_\\mathrm{I}}{Hi \\, Ha^2} \\frac{\\mathrm{d} f_\\mathrm{2,L}}{\\mathrm{d}\\chi} \\Big \\vert_{\\chi = 1} \\normalsize - Da_\\mathrm{I} \\frac{Hi - 1 }{Hi} f_\\mathrm{1,L,b}\\,f_\\mathrm{2,L,b},\\\\\n",
    "0 &= - f_\\mathrm{3,L,b} - \\frac{Da_\\mathrm{I}}{Hi \\, Ha^2} \\frac{\\mathrm{d} f_\\mathrm{3,L}}{\\mathrm{d}\\chi} \\Big \\vert_{\\chi = 1} \\normalsize + Da_\\mathrm{I} \\frac{Hi - 1 }{Hi} f_\\mathrm{1,L,b}\\,f_\\mathrm{2,L,b},\n",
    "\\end{align*}\n",
    "\n",
    "Die Randbedingungen werden in der Funktion _bc_material_ implementiert, die auch die Berechung der Restanteile in der Kernströmung enthält."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bc_material(y0, y1):\n",
    "    '''\n",
    "    Randbedingungen der Materialbilanz\n",
    "    y0    : Randbedingungen bei X = 0; \n",
    "                Vektor der Größe 4 : (Restanteil A1, Restanteil A2, Restanteil A3, Ableitung des Restanteils A1 nach der Ortskoordinate, Ableitung des Restanteils A2 nach der Ortskoordinate, \n",
    "                Ableitung des Restanteils A3 nach der Ortskoordinate)\n",
    "    y1    : Randbedingungen bei X = 1;\n",
    "                Vektor der Größe 4 : (Restanteil A1, Restanteil A2, Restanteil A3, Ableitung des Restanteils A1 nach der Ortskoordinate, Ableitung des Restanteils A2 nach der Ortskoordinate, \n",
    "                Ableitung des Restanteils A3 nach der Ortskoordinate)\n",
    "    f_L_b : Restanteil von A1, A2 und A3 in der Kernströmung\n",
    "    f_G_b : Restanteil von A1, A2 und A3 im Bulk des Gases\n",
    "    '''\n",
    "\n",
    "    # Berechnung der Restanteile in der Kernströmung der Flüssigkeit f_L_b (Bilanz Makroskala)\n",
    "    f_L_b = np.empty(3)\n",
    "    f_L_b[0] = - Da_I / (Hi * Ha**2) * y1[3] - (1 + Da_I * (Hi - 1) / Hi) * y1[0]*  y1[1]\n",
    "    f_L_b[1] = kappa_2 - Da_I / (Hi * Ha**2) * y1[4] - (1 + Da_I * (Hi - 1) / Hi) * y1[0]*  y1[1]     \n",
    "    f_L_b[2] = - Da_I / (Hi * Ha**2) * y1[5] + (1 + Da_I * (Hi - 1) / Hi) * y1[0]*  y1[1]\n",
    "    \n",
    "    BC = np.empty(nu.size *2)  # 2 Randbedingungen (RB) pro Komponente\n",
    "    # A1\n",
    "    BC[0] = y0[0] - f_1_G_b    # RB an der Stelle X = 0 für Komponente A1 : 0 = f_1,L(X = 0) - f_1,G,b\n",
    "    BC[1] = y1[0] - f_L_b[0]   # RB an der Stelle X = 1 für Komponente A1 : 0 = f_1,L(X = 1) - f_1,L,b\n",
    "    # A2\n",
    "    BC[2] = y0[4]              # RB an der Stelle X = 0 für Komponente A2 : 0 = df_2dX(X = 0)\n",
    "    BC[3] = y1[1] - f_L_b[1]   # RB an der Stelle X = 1 für Komponente A2 : 0 = f_2,L(X = 1) - f_2,L,b\n",
    "    # A3\n",
    "    BC[4] = y0[5]              # RB an der Stelle X = 0 für Komponente A3 : 0 = df_3dX(X = 0)\n",
    "    BC[5] = y1[2] - f_L_b[2]   # RB an der Stelle X = 1 für Komponente A3 : 0 = f_3,L(X = 1) - f_3,L,b\n",
    "    \n",
    "    return BC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Danach werden die stöchiometrischen Koeffizienten gemäß der Reaktionsgleichung, die Hatta-Zahl und der Restanteil von $A_1$ in der Kernströmung des Gases parametriert. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "Hi          = 100                    # Hinterland-Verhältnis\n",
    "kappa_2     = 2                      # Einsatzverhältnis\n",
    "nu          = np.array((-1,-1,1))    # Stöchiometrische Koeffizienten für A1, A3 \n",
    "f_1_G_b     = np.array((1))          # Restanteil von A1 in der Kernströmung des Gases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Da der Einfluss verschiedener  Dammköhler-Zahlen $(Da_I)$ und Hinterland-Verhältnisse $(Hi)$ betrachtet werden soll, erfolgt die Variation von $Da_I$ und $Hi$ jeweils in einer For-Schleife. Die Profile der Restanteile werden durch den Aufruf eines Solvers für Randwertprobleme (solve_bvp) auf Grundlage der Materialbilanzen und der Randbedingungen berechnet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "Da_I_vec      = np.array((1, 3, 10))             # Vektor mit Dammköhler-Zahlen für Beispiel 18.5\n",
    "Ha_vec        = np.array((0.3, 1, 10))           # Vektor mit Hatta-Zahlen für Beispiel 18.5\n",
    "\n",
    "Lösungen_Ha_1 = []                               # Leere Liste zum Speichern der Lösungen für Ha = 0,3\n",
    "Lösungen_Ha_2 = []                               # Leere Liste zum Speichern der Lösungen für Ha = 1\n",
    "Lösungen_Ha_3 = []                               # Leere Liste zum Speichern der Lösungen für Ha = 10\n",
    "N             = 101                              # Diskretisierung\n",
    "X             = np.linspace(0, 1, N)             # Diskretisierung der Ortskoordinate\n",
    "init_f        = np.ones((3, N))                  # Startwerte für Restanteile\n",
    "init_df       = np.ones((3, N)) *1e-6            # Startwerte für die Ableitungen der Restanteile\n",
    "init          = np.vstack((init_f, init_df))     # Zusammengefügte Startwerte für die Iterationen des Solvers\n",
    "\n",
    "\n",
    "for DaDa in Da_I_vec:\n",
    "    Ha     = Ha_vec[0]\n",
    "    Da_I   = DaDa\n",
    "    sol    = solve_bvp(material, bc_material, X, init , max_nodes = 1e10, tol = 1e-10)\n",
    "    if sol.success == False:\n",
    "        print(sol)\n",
    "    Lösungen_Ha_1.append(sol)\n",
    "\n",
    "for DaDa in Da_I_vec:\n",
    "    Ha     = Ha_vec[1]\n",
    "    Da_I   = DaDa\n",
    "    sol    = solve_bvp(material, bc_material, X, init , max_nodes = 1e10, tol = 1e-10)\n",
    "    if sol.success == False:\n",
    "        print(sol)\n",
    "    Lösungen_Ha_2.append(sol)\n",
    "\n",
    "for DaDa in Da_I_vec:\n",
    "    Ha     = Ha_vec[2]\n",
    "    Da_I   = DaDa\n",
    "    sol    = solve_bvp(material, bc_material, X, init , max_nodes = 1e10, tol = 1e-10)\n",
    "    if sol.success == False:\n",
    "        print(sol)\n",
    "    Lösungen_Ha_3.append(sol)\n",
    "\n",
    "\n",
    "Lösungen_Ha_1 = np.array((Lösungen_Ha_1))\n",
    "Lösungen_Ha_2 = np.array((Lösungen_Ha_2))\n",
    "Lösungen_Ha_3 = np.array((Lösungen_Ha_3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Abschließend werden die Ergebnisse grafisch veranschaulicht."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors_Da = ['black','blue','red']\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize = (10, 5))\n",
    "for Lösung, Da, color in zip(Lösungen_Ha_1,Da_I_vec,colors_Da):\n",
    "    ax[0].plot(Lösung.x, Lösung.y[0], label = str(Da), color = color)\n",
    "    ax[0].hlines(y=Lösung.y[0][-1], xmin=1, xmax=1.1, colors = color)\n",
    "    ax[0].plot(Lösung.x, Lösung.y[1], color = color, linestyle = '--')\n",
    "    ax[0].hlines(y=Lösung.y[1][-1], xmin=1, xmax=1.1, colors = color, linestyle = '--')\n",
    "    ax[0].plot(Lösung.x, Lösung.y[2], color = color, linestyle = ':')\n",
    "    ax[0].hlines(y=Lösung.y[2][-1], xmin=1, xmax=1.1, colors = color, linestyle = ':')\n",
    "\n",
    "for Lösung, Da, color in zip(Lösungen_Ha_2,Da_I_vec,colors_Da):\n",
    "    ax[1].plot(Lösung.x, Lösung.y[0], label = str(Da), color = color)\n",
    "    ax[1].hlines(y=Lösung.y[0][-1], xmin=1, xmax=1.1, colors = color)\n",
    "    ax[1].plot(Lösung.x, Lösung.y[1], color = color, linestyle = '--')\n",
    "    ax[1].hlines(y=Lösung.y[1][-1], xmin=1, xmax=1.1, colors = color, linestyle = '--')\n",
    "    ax[1].plot(Lösung.x, Lösung.y[2], color = color, linestyle = ':')\n",
    "    ax[1].hlines(y=Lösung.y[2][-1], xmin=1, xmax=1.1, colors = color, linestyle = ':')\n",
    "\n",
    "ax[0].vlines(x=1, ymin=0, ymax=3, colors='black')\n",
    "ax[0].set_xlim(0,1.1)\n",
    "ax[0].set_ylim(0,2.05)\n",
    "ax[0].tick_params(axis=\"y\",direction=\"in\", right = True)\n",
    "ax[0].tick_params(axis=\"x\",direction=\"in\", top = True)\n",
    "ax[0].set_ylabel(r'$f_{i,\\mathrm{L}}\\,/\\,1$')\n",
    "ax[0].set_xlabel(r'$\\chi \\,/\\,1$')\n",
    "\n",
    "ax[1].vlines(x=1, ymin=0, ymax=3, colors='black')\n",
    "ax[1].set_ylabel(r'$f_{i,\\mathrm{L}}\\,/\\,1$')\n",
    "ax[1].set_xlabel(r'$\\chi \\,/\\,1$')\n",
    "ax[1].set_xlim(0,1.1)\n",
    "ax[1].set_ylim(0,2.05)\n",
    "ax[1].tick_params(axis=\"y\",direction=\"in\", right = True)\n",
    "ax[1].tick_params(axis=\"x\",direction=\"in\", top = True)\n",
    "ax[1].legend(title = '$Da_\\mathrm{I}$ =',frameon = False, alignment = 'left',ncol=1,loc=(0.7,0.5))\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Für die Betrachtung des Einflusses von $Ha$ und $Da_\\mathrm{I}$ auf den Nutzungsgrad des Flüssigkeitsfilms $\\eta_\\mathrm{L,Film}$, den Nutzungsgrad der Kernströmung $\\eta_\\mathrm{L,b}$, den Nutzungsgradverlust $\\Delta\\eta_\\mathrm{L}$ und den Verstärkungsfaktor $E$ werden folgende Gleichungen ausgewertet (s. Beispiel 18.4 im Lehrbuch):\n",
    "\n",
    "\\begin{align*}\n",
    "E &= \\frac{\\frac{\\mathrm{d} f_\\mathrm{1,L}}{\\mathrm{d}\\chi} \\Big \\vert_{\\chi = 0}}{f_{1,\\chi = 1}-f_{1,\\chi = 0}} \\\\\n",
    "\\eta_\\mathrm{L,Film} &= 1 - \\frac{\\dot{n}_{1,\\chi = 1}}{\\dot{n}_{1,\\chi = 0}} = 1 - \\frac{J_{1,\\chi = 1}}{J_{1,\\chi = 0}} = 1 - \\frac{\\frac{\\mathrm{d} f_\\mathrm{1,L}}{\\mathrm{d}\\chi} \\Big \\vert_{\\chi = 1}}{\\frac{\\mathrm{d} f_\\mathrm{1,L}}{\\mathrm{d}\\chi} \\Big \\vert_{\\chi = 0}}\\\\\n",
    "\\Delta\\eta_\\mathrm{L} &= \\frac{\\dot{n}_\\mathrm{1,L,a} - \\dot{n}_\\mathrm{1,L,e}}{\\dot{n}_{1,\\chi = 0}} = - \\frac{Hi\\,Ha^2}{Da_\\mathrm{I}} \\frac{f_\\mathrm{1,L,b}}{\\frac{\\mathrm{d} f_\\mathrm{1,L}}{\\mathrm{d}\\chi} \\Big \\vert_{\\chi = 0}}\\\\\n",
    "\\eta_\\mathrm{L,b} &= 1 - \\eta_\\mathrm{L,Film} - \\Delta\\eta_\\mathrm{L}\n",
    "\\end{align*}\n",
    "\n",
    "Diese Berechnungsvorschriften werden jeweils als Funktionen wie folgt implementiert."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cal_E(dfdx_0, f_0, f_1):\n",
    "    '''\n",
    "    Berechnung des Verstärkungsfaktors\n",
    "    dfdx_0 : Ableitung des Restanteils nach der Ortskoordinate an der Stelle X = 0\n",
    "    f_0    : Restanteil an der Stelle X = 0\n",
    "    f_1    : Restanteil an der Stelle X = 1\n",
    "    '''\n",
    "    return dfdx_0 / (f_1 - f_0)\n",
    "\n",
    "def cal_eta_L_Film(dfdx_1, dfdx_0):\n",
    "    '''\n",
    "    Berechnung des Nutzungsgrads des Flüssigkeitsfilms\n",
    "    dfdx_1 : Ableitung des Restanteils nach der Ortskoordinate an der Stelle X = 1\n",
    "    dfdx_0 : Ableitung des Restanteils nach der Ortskoordinate an der Stelle X = 0\n",
    "    '''\n",
    "    return 1 - dfdx_1 / dfdx_0\n",
    "\n",
    "\n",
    "def cal_Delta_eta_L(Hi, Ha, Da, f_1_L_b, dfdx_0):\n",
    "    '''\n",
    "    Berechnung des Nutzungsgradverlusts\n",
    "    dfdx_0  : Ableitung des Restanteils nach der Ortskoordinate an der Stelle X = 0\n",
    "    Hi      : Hinterland-Verhältnis\n",
    "    Da      : Dammköhler-Zahl\n",
    "    Ha      : Hatta-Zahl\n",
    "    f_1_L_b : Restanteil von A1 in der Kernströmung der Flüssigkeit\n",
    "    '''\n",
    "    return - Hi * Ha**2 / Da * f_1_L_b / dfdx_0\n",
    "\n",
    "def cal_eta_L_b(eta_L_Film, Delta_eta_L):\n",
    "    '''\n",
    "    Berechnung des Nutzungsgrads der Kernströmung\n",
    "    eta_L_Film  : Nutzungsgrads des Flüssigkeitsfilms\n",
    "    Delta_eta_L : Nutzungsgradverlust\n",
    "    '''\n",
    "    return 1 - eta_L_Film - Delta_eta_L"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die Berechnungen für verschiedene $Ha$ und $Da_\\mathrm{I}$ erfolgen jeweils in einer For-Schleife."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Res_Ha_1 = [] # leere Liste zum Speichern der Ergebnisse für verschiedene Ha = 0,3\n",
    "Res_Ha_2 = [] # leere Liste zum Speichern der Ergebnisse für verschiedene Ha = 1\n",
    "Res_Ha_3 = [] # leere Liste zum Speichern der Ergebnisse für verschiedene Ha = 10\n",
    "\n",
    "# Berechnung für Ha = 0,3\n",
    "for Lösung, Da in zip(Lösungen_Ha_1, Da_I_vec):\n",
    "    Ha     = Ha_vec[0]\n",
    "    Da_I   = Da\n",
    "    Res    = np.empty(4) # Array der Größe 4 zum Speichern von E, eta_L_Film, Delta_eta_L, eta_L_b\n",
    "    Res[0] = cal_E(Lösung.y[3,0], Lösung.y[0,0], Lösung.y[0,-1])\n",
    "    Res[1] = cal_eta_L_Film(Lösung.y[3,-1], Lösung.y[3,0])\n",
    "    Res[2] = cal_Delta_eta_L(Hi, Ha, Da_I, Lösung.y[0,-1], Lösung.y[3,0])\n",
    "    Res[3] = cal_eta_L_b(Res[1], Res[2])\n",
    "    Res_Ha_1.append(Res)\n",
    "\n",
    "Res_Ha_1 = np.array((Res_Ha_1))\n",
    "\n",
    "\n",
    "\n",
    "# Berechnung für Ha = 1\n",
    "for Lösung, Da in zip(Lösungen_Ha_2, Da_I_vec):\n",
    "    Ha     = Ha_vec[1]\n",
    "    Da_I   = Da\n",
    "    Res    = np.empty(4) # Array der Größe 4 zum Speichern von E, eta_L_Film, Delta_eta_L, eta_L_b\n",
    "    Res[0] = cal_E(Lösung.y[3,0],Lösung.y[0,0], Lösung.y[0,-1])\n",
    "    Res[1] = cal_eta_L_Film(Lösung.y[3,-1],Lösung.y[3,0])\n",
    "    Res[2] = cal_Delta_eta_L(Hi, Ha, Da_I, Lösung.y[0,-1],Lösung.y[3,0])\n",
    "    Res[3] = cal_eta_L_b(Res[1],Res[2])\n",
    "    Res_Ha_2.append(Res)\n",
    "\n",
    "Res_Ha_2 = np.array((Res_Ha_2))\n",
    "\n",
    "# Berechnung für Ha = 10\n",
    "for Lösung, Da in zip(Lösungen_Ha_3, Da_I_vec):\n",
    "    Ha     = Ha_vec[2]\n",
    "    Da_I   = Da\n",
    "    Res    = np.empty(4) # Array der Größe 4 zum Speichern von E, eta_L_Film, Delta_eta_L, eta_L_b\n",
    "    Res[0] = cal_E(Lösung.y[3,0],Lösung.y[0,0], Lösung.y[0,-1])\n",
    "    Res[1] = cal_eta_L_Film(Lösung.y[3,-1], Lösung.y[3,0])\n",
    "    Res[2] = cal_Delta_eta_L(Hi, Ha, Da_I, Lösung.y[0,-1], Lösung.y[3,0])\n",
    "    Res[3] = cal_eta_L_b(Res[1], Res[2])\n",
    "    Res_Ha_3.append(Res)\n",
    "\n",
    "Res_Ha_3 = np.array((Res_Ha_3))\n",
    "\n",
    "comb_Res        = np.hstack((Res_Ha_1.T, Res_Ha_2.T, Res_Ha_3.T)) # Kombinierter Vektor der Ergebnisse\n",
    "comb_Res[[2,3]] = comb_Res[[3,2]]                                 # Tauschen der letzten mit der vorletzten Reihe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die Ergebnisse werden in einer Tabelle ausgegeben."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------  ----  ----  -----  ----  ----  -----  -----  -----  -----\n",
      "Ha / 1           0.3   0.3    0.3   1     1      1     10     10     10\n",
      "Da_I / 1         1     3     10     1     3     10      1      3     10\n",
      "E / 1            1.06  1.05   1.03  1.48  1.47   1.45   2.97   2.97   2.96\n",
      "eta_L_Film / 1   0.08  0.08   0.05  0.48  0.48   0.46   1      1      1\n",
      "eta_L_b / 1      0.73  0.8    0.87  0.41  0.46   0.51   0      0      0\n",
      "Delta_eta_L / 1  0.19  0.12   0.08  0.1   0.06   0.03   0      0      0\n",
      "---------------  ----  ----  -----  ----  ----  -----  -----  -----  -----\n"
     ]
    }
   ],
   "source": [
    "# Ausgabe der Ergebnisse als Tabelle\n",
    "\n",
    "names_y  = np.array((['Ha / 1'], ['Da_I / 1'], ['E / 1'], ['eta_L_Film / 1'], ['eta_L_b / 1'], ['Delta_eta_L / 1']))\n",
    "Ha_table = np.array((0.3, 0.3, 0.3, 1, 1, 1, 10, 10, 10))  \n",
    "Da_table = np.array((1, 3, 10, 1, 3, 10, 1, 3, 10))\n",
    "\n",
    "# Array für Tabelle\n",
    "table = np.hstack((names_y,np.vstack((Ha_table, Da_table, np.round(comb_Res, 2)))))\n",
    "\n",
    "print(tabulate(table))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die hier ermittelten Zahlenwerte weichen leicht von denen in Tabelle 18.4 des Lehrbuchs ab, insbesondere bei $Da_I = 1$. Nach umfangreicher Überprüfung möglicher Ursachen lässt sich das hauptsächlich auf numerische Gründe zurückführen. Zur Erzeugung der Ergebnisse im Buch wurde der _bvp4c_ Solver aus MatLab mit einer Toleranz von $10^{-3}$ verwendet, wohingegen hier der solver _scipy.integrate.solve_bvp_ mit einer Toleranz von $10^{-10}$ verwendet wird."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Alternative Lösung mit anderer Randbedingung"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternativ zu den Randbedingung im Buch aus der Tabelle 18.3 kann ein Ansatz über die Biot-Zahl $Bi$ gewählt werden, um strukturell gas- und flüssigkeitsseitigen Stofftransport zu berücksichtigen. Die Biot-Zahl wird aber sehr groß ($Bi = 10^{6}$) gewählt, so dass gasseitige Gradienten ausgeschlossen werden können und effektiv die Randbedingungen gem. Tabelle 18.3 im Lehrbuch vorliegen. \n",
    "Die Randbedingung an der Phasengrenzfläche ($\\chi = 0$) der gasförmigen Komponente A1 ändert sich dementsprechend zu:\n",
    "\n",
    "\\begin{align*} \n",
    "\\frac{\\mathrm{d} f_\\mathrm{1,L}}{\\mathrm{d}\\chi} \\Big \\vert_{\\chi = 0} = Bi \\, (f_\\mathrm{1,L} - f_\\mathrm{1,G,b}).\n",
    "\\end{align*}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bc_material(y0, y1):\n",
    "    '''\n",
    "    Randbedingungen der Materialbilanz\n",
    "    y0    : Randbedingungen bei X = 0; \n",
    "                Vektor der Größe 4 : (Restanteil A1, Restanteil A2, Restanteil A3, Ableitung des Restanteils A1 nach der Ortskoordinate, Ableitung des Restanteils A2 nach der Ortskoordinate, \n",
    "                Ableitung des Restanteils A3 nach der Ortskoordinate)\n",
    "    y1    : Randbedingungen bei X = 1;\n",
    "                Vektor der Größe 4 : (Restanteil A1, Restanteil A2, Restanteil A3, Ableitung des Restanteils A1 nach der Ortskoordinate, Ableitung des Restanteils A2 nach der Ortskoordinate, \n",
    "                Ableitung des Restanteils A3 nach der Ortskoordinate)\n",
    "    f_L_b : Restanteil von A1, A2 und A3 in der Kernströmung\n",
    "    f_G_b : Restanteil von A1, A2 und A3 im Bulk des Gases\n",
    "    '''\n",
    "\n",
    "    # Berechnung der Restanteile in der Kernströmung der Flüssigkeit f_L_b (Bilanz Makroskala)\n",
    "    f_L_b = np.empty(3)\n",
    "    f_L_b[0] = - Da_I / (Hi * Ha**2) * y1[3] - (1 + Da_I * (Hi - 1) / Hi) * y1[0]*  y1[1]\n",
    "    f_L_b[1] = kappa_2 - Da_I / (Hi * Ha**2) * y1[4] - (1 + Da_I * (Hi - 1) / Hi) * y1[0]*  y1[1]     \n",
    "    f_L_b[2] = - Da_I / (Hi * Ha**2) * y1[5] + (1 + Da_I * (Hi - 1) / Hi) * y1[0]*  y1[1]\n",
    "    \n",
    "    BC = np.empty(nu.size *2)  # 2 Randbedingungen (RB) pro Komponente\n",
    "    # A1\n",
    "    BC[0] = y0[3] - Bi * (y0[0] - f_1_G_b)    # RB an der Stelle X = 0 für Komponente A1 : df_1dX(X = 0) = Bi * (f_1,L(X = 0) - f_1_G_b)    \n",
    "    BC[1] = y1[0] - f_L_b[0]                  # RB an der Stelle X = 1 für Komponente A1 : 0 = f_1,L(X = 1) - f_1,L,b\n",
    "    # A2\n",
    "    BC[2] = y0[4]                             # RB an der Stelle X = 0 für Komponente A2 : 0 = df_2dX(X = 0)\n",
    "    BC[3] = y1[1] - f_L_b[1]                  # RB an der Stelle X = 1 für Komponente A2 : 0 = f_2,L(X = 1) - f_2,L,b\n",
    "    # A3\n",
    "    BC[4] = y0[5]                             # RB an der Stelle X = 0 für Komponente A3 : 0 = df_3dX(X = 0)\n",
    "    BC[5] = y1[2] - f_L_b[2]                  # RB an der Stelle X = 1 für Komponente A3 : 0 = f_3,L(X = 1) - f_3,L,b\n",
    "    \n",
    "    return BC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nach der Definition der neuen Randbedingung wird die Biot-Zahl parametriert."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "Bi          = 1e6                    # Biot-Zahl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anschließend wird das System mit der neuen Randbedingung wieder für verschiedene Dammköhler-Zahlen $(Da_I)$ und Hinterland-Verhältnisse $(Hi)$ gelöst und die Lösung grafisch veranschaulicht."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "Da_I_vec      = np.array((1, 3, 10))             # Vektor mit Dammköhler-Zahlen für Beispiel 18.5\n",
    "Ha_vec        = np.array((0.3, 1, 10))           # Vektor mit Hatta-Zahlen für Beispiel 18.5\n",
    "\n",
    "Lösungen_Ha_1 = []                               # Leere Liste zum Speichern der Lösungen für Ha = 0,3\n",
    "Lösungen_Ha_2 = []                               # Leere Liste zum Speichern der Lösungen für Ha = 1\n",
    "Lösungen_Ha_3 = []                               # Leere Liste zum Speichern der Lösungen für Ha = 10\n",
    "N             = 101                              # Diskretisierung\n",
    "X             = np.linspace(0, 1, N)             # Diskretisierung der Ortskoordinate\n",
    "init_f        = np.ones((3, N))                  # Startwerte für Restanteile\n",
    "init_df       = np.ones((3, N)) *1e-6            # Startwerte für die Ableitungen der Restanteile\n",
    "init          = np.vstack((init_f, init_df))     # Zusammengefügte Startwerte für die Iterationen des Solvers\n",
    "\n",
    "\n",
    "for DaDa in Da_I_vec:\n",
    "    Ha     = Ha_vec[0]\n",
    "    Da_I   = DaDa\n",
    "    sol    = solve_bvp(material, bc_material, X, init , max_nodes = 1e10, tol = 1e-10)\n",
    "    if sol.success == False:\n",
    "        print(sol)\n",
    "    Lösungen_Ha_1.append(sol)\n",
    "\n",
    "for DaDa in Da_I_vec:\n",
    "    Ha     = Ha_vec[1]\n",
    "    Da_I   = DaDa\n",
    "    sol    = solve_bvp(material, bc_material, X, init , max_nodes = 1e10, tol = 1e-10)\n",
    "    if sol.success == False:\n",
    "        print(sol)\n",
    "    Lösungen_Ha_2.append(sol)\n",
    "\n",
    "for DaDa in Da_I_vec:\n",
    "    Ha     = Ha_vec[2]\n",
    "    Da_I   = DaDa\n",
    "    sol    = solve_bvp(material, bc_material, X, init , max_nodes = 1e10, tol = 1e-10)\n",
    "    if sol.success == False:\n",
    "        print(sol)\n",
    "    Lösungen_Ha_3.append(sol)\n",
    "\n",
    "\n",
    "Lösungen_Ha_1 = np.array((Lösungen_Ha_1))\n",
    "Lösungen_Ha_2 = np.array((Lösungen_Ha_2))\n",
    "Lösungen_Ha_3 = np.array((Lösungen_Ha_3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors_Da = ['black','blue','red']\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize = (10, 5))\n",
    "for Lösung, Da, color in zip(Lösungen_Ha_1,Da_I_vec,colors_Da):\n",
    "    ax[0].plot(Lösung.x, Lösung.y[0], label = str(Da), color = color)\n",
    "    ax[0].hlines(y=Lösung.y[0][-1], xmin=1, xmax=1.1, colors = color)\n",
    "    ax[0].plot(Lösung.x, Lösung.y[1], color = color, linestyle = '--')\n",
    "    ax[0].hlines(y=Lösung.y[1][-1], xmin=1, xmax=1.1, colors = color, linestyle = '--')\n",
    "    ax[0].plot(Lösung.x, Lösung.y[2], color = color, linestyle = ':')\n",
    "    ax[0].hlines(y=Lösung.y[2][-1], xmin=1, xmax=1.1, colors = color, linestyle = ':')\n",
    "\n",
    "for Lösung, Da, color in zip(Lösungen_Ha_2,Da_I_vec,colors_Da):\n",
    "    ax[1].plot(Lösung.x, Lösung.y[0], label = str(Da), color = color)\n",
    "    ax[1].hlines(y=Lösung.y[0][-1], xmin=1, xmax=1.1, colors = color)\n",
    "    ax[1].plot(Lösung.x, Lösung.y[1], color = color, linestyle = '--')\n",
    "    ax[1].hlines(y=Lösung.y[1][-1], xmin=1, xmax=1.1, colors = color, linestyle = '--')\n",
    "    ax[1].plot(Lösung.x, Lösung.y[2], color = color, linestyle = ':')\n",
    "    ax[1].hlines(y=Lösung.y[2][-1], xmin=1, xmax=1.1, colors = color, linestyle = ':')\n",
    "\n",
    "ax[0].vlines(x=1, ymin=0, ymax=3, colors='black')\n",
    "ax[0].set_xlim(0,1.1)\n",
    "ax[0].set_ylim(0,2.05)\n",
    "ax[0].tick_params(axis=\"y\",direction=\"in\", right = True)\n",
    "ax[0].tick_params(axis=\"x\",direction=\"in\", top = True)\n",
    "ax[0].set_ylabel(r'$f_{i,\\mathrm{L}}\\,/\\,1$')\n",
    "ax[0].set_xlabel(r'$\\chi \\,/\\,1$')\n",
    "\n",
    "ax[1].vlines(x=1, ymin=0, ymax=3, colors='black')\n",
    "ax[1].set_ylabel(r'$f_{i,\\mathrm{L}}\\,/\\,1$')\n",
    "ax[1].set_xlabel(r'$\\chi \\,/\\,1$')\n",
    "ax[1].set_xlim(0,1.1)\n",
    "ax[1].set_ylim(0,2.05)\n",
    "ax[1].tick_params(axis=\"y\",direction=\"in\", right = True)\n",
    "ax[1].tick_params(axis=\"x\",direction=\"in\", top = True)\n",
    "ax[1].legend(title = '$Da_\\mathrm{I}$ =',frameon = False, alignment = 'left',ncol=1,loc=(0.7,0.5))\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Weiter werden der Nutzungsgrad des Flüssigkeitsfilms $\\eta_\\mathrm{L,Film}$, der Nutzungsgrad der Kernströmung $\\eta_\\mathrm{L,b}$, der Nutzungsgradverlust $\\Delta\\eta_\\mathrm{L}$ und der Verstärkungsfaktor $E$ ausgewertet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "Res_Ha_1 = [] # leere Liste zum Speichern der Ergebnisse für verschiedene Ha = 0,3\n",
    "Res_Ha_2 = [] # leere Liste zum Speichern der Ergebnisse für verschiedene Ha = 1\n",
    "Res_Ha_3 = [] # leere Liste zum Speichern der Ergebnisse für verschiedene Ha = 10\n",
    "\n",
    "# Berechnung für Ha = 0,3\n",
    "for Lösung, Da in zip(Lösungen_Ha_1, Da_I_vec):\n",
    "    Ha     = Ha_vec[0]\n",
    "    Da_I   = Da\n",
    "    Res    = np.empty(4) # Array der Größe 4 zum Speichern von E, eta_L_Film, Delta_eta_L, eta_L_b\n",
    "    Res[0] = cal_E(Lösung.y[3,0], Lösung.y[0,0], Lösung.y[0,-1])\n",
    "    Res[1] = cal_eta_L_Film(Lösung.y[3,-1], Lösung.y[3,0])\n",
    "    Res[2] = cal_Delta_eta_L(Hi, Ha, Da_I, Lösung.y[0,-1], Lösung.y[3,0])\n",
    "    Res[3] = cal_eta_L_b(Res[1], Res[2])\n",
    "    Res_Ha_1.append(Res)\n",
    "\n",
    "Res_Ha_1 = np.array((Res_Ha_1))\n",
    "\n",
    "\n",
    "\n",
    "# Berechnung für Ha = 1\n",
    "for Lösung, Da in zip(Lösungen_Ha_2, Da_I_vec):\n",
    "    Ha     = Ha_vec[1]\n",
    "    Da_I   = Da\n",
    "    Res    = np.empty(4) # Array der Größe 4 zum Speichern von E, eta_L_Film, Delta_eta_L, eta_L_b\n",
    "    Res[0] = cal_E(Lösung.y[3,0], Lösung.y[0,0], Lösung.y[0,-1])\n",
    "    Res[1] = cal_eta_L_Film(Lösung.y[3,-1], Lösung.y[3,0])\n",
    "    Res[2] = cal_Delta_eta_L(Hi, Ha, Da_I, Lösung.y[0,-1], Lösung.y[3,0])\n",
    "    Res[3] = cal_eta_L_b(Res[1], Res[2])\n",
    "    Res_Ha_2.append(Res)\n",
    "\n",
    "Res_Ha_2 = np.array((Res_Ha_2))\n",
    "\n",
    "# Berechnung für Ha = 10\n",
    "for Lösung, Da in zip(Lösungen_Ha_3, Da_I_vec):\n",
    "    Ha     = Ha_vec[2]\n",
    "    Da_I   = Da\n",
    "    Res    = np.empty(4) # Array der Größe 4 zum Speichern von E, eta_L_Film, Delta_eta_L, eta_L_b\n",
    "    Res[0] = cal_E(Lösung.y[3,0], Lösung.y[0,0], Lösung.y[0,-1])\n",
    "    Res[1] = cal_eta_L_Film(Lösung.y[3,-1], Lösung.y[3,0])\n",
    "    Res[2] = cal_Delta_eta_L(Hi, Ha, Da_I, Lösung.y[0,-1], Lösung.y[3,0])\n",
    "    Res[3] = cal_eta_L_b(Res[1], Res[2])\n",
    "    Res_Ha_3.append(Res)\n",
    "\n",
    "Res_Ha_3 = np.array((Res_Ha_3))\n",
    "\n",
    "comb_Res        = np.hstack((Res_Ha_1.T, Res_Ha_2.T ,Res_Ha_3.T)) # Kombinierter Vektor der Ergebnisse\n",
    "comb_Res[[2,3]] = comb_Res[[3,2]]                                 # Tauschen der letzten mit der vorletzten Reihe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die Ausgabe der Ergebnisse erfolgt tabellarisch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------  ----  ----  -----  ----  ----  -----  -----  -----  -----\n",
      "Ha / 1           0.3   0.3    0.3   1     1      1     10     10     10\n",
      "Da_I / 1         1     3     10     1     3     10      1      3     10\n",
      "E / 1            1.06  1.05   1.03  1.48  1.47   1.45   2.97   2.97   2.96\n",
      "eta_L_Film / 1   0.08  0.08   0.05  0.48  0.48   0.46   1      1      1\n",
      "eta_L_b / 1      0.73  0.8    0.87  0.41  0.46   0.51   0      0      0\n",
      "Delta_eta_L / 1  0.19  0.12   0.08  0.1   0.06   0.03   0      0      0\n",
      "---------------  ----  ----  -----  ----  ----  -----  -----  -----  -----\n"
     ]
    }
   ],
   "source": [
    "# Ausgabe der Ergebnisse als Tabelle\n",
    "\n",
    "names_y  = np.array((['Ha / 1'], ['Da_I / 1'], ['E / 1'], ['eta_L_Film / 1'], ['eta_L_b / 1'], ['Delta_eta_L / 1']))\n",
    "Ha_table = np.array((0.3, 0.3, 0.3, 1, 1, 1, 10, 10, 10))  \n",
    "Da_table = np.array((1, 3, 10, 1, 3, 10, 1, 3, 10))\n",
    "\n",
    "# Array für Tabelle\n",
    "table = np.hstack((names_y,np.vstack((Ha_table, Da_table, np.round(comb_Res,2)))))\n",
    "\n",
    "print(tabulate(table))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Erwartungsgemäß ist kein Unterschied in den ersten zwei Nachkommastellen beim Vergleich der Ergebnisse beider Ansätze festzustellen. Der Ansatz über die Biot-Zahl scheint deshalb ein guter alternativer und allgemeinerer Lösungsweg zu sein."
   ]
  }
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