Lin_Alg_Solver.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "513ea505",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x3000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.sparse import lil_array, csr_array\n",
    "from scipy.sparse.linalg import spsolve\n",
    "from math import ceil, sqrt\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "plt.rcParams[\"figure.figsize\"] = (10, 15)\n",
    "plt.rcParams[\"figure.dpi\"]= 200\n",
    "plt.set_cmap(\"plasma\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aaf6456",
   "metadata": {},
   "source": [
    "## Function definitions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80a70433",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Geometry init\n",
    "The geometry is a grid of lambda values(Thermal conductivity in [w/(m*k)]), set up with the nodes in the middle of each grid square."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5901655e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_l(dx, width, geo):\n",
    "    \"\"\"\n",
    "    Initialize the geometry array 'l' based on the provided parameters.\n",
    "\n",
    "    Args:\n",
    "    dx (float): The spacing or resolution for discretizing the 'l' array.\n",
    "    width (float): The width of the 'l' array.\n",
    "    geo (list): A list describing the geometry of the system.\n",
    "                Each element in the list should be a sublist with the format:\n",
    "                [height, lambda_value, cap_info]\n",
    "                where 'height' is the height of the geometry layer,\n",
    "                'lambda_value' is the corresponding lambda value,\n",
    "                and 'cap_info' is an optional sublist with capillary information.\n",
    "                The capillary information should have the format:\n",
    "                [r1, r2, cap_offset, lambda_cap],\n",
    "                where 'r1' and 'r2' are the inner and outter radii of the capillary,\n",
    "                'cap_offset' is the offset from the lower bound of the layer,\n",
    "                and 'lambda_cap' is the lambda value for the capillary.\n",
    "\n",
    "    Returns:\n",
    "    l (numpy.ndarray): The geometry array 'l' with lambda values.\n",
    "    water_mask (numpy.ndarray): A binary array denoting water locations.\n",
    "    center (float): The center position of the capillary segment.\n",
    "\n",
    "    Note:\n",
    "    - The lambda value for water is set to a default value of 0.6089.\n",
    "    - The 'water_mask' array is a binary array with True values representing water locations.\n",
    "    \"\"\"\n",
    "    l_water = 0.6089 #from wiki\n",
    "    #Initialize the geometry array of l(i, j)\n",
    "    #calculate total system height\n",
    "    height = sum([sublist[0] for sublist in geo])\n",
    "    l = np.zeros((int(height/dx), int(width/dx)))\n",
    "    \n",
    "    #Fill the geometry array\n",
    "    lower_bound = 0\n",
    "    for i in geo:\n",
    "        l_height = int(i[0]/dx)\n",
    "        l[lower_bound:(lower_bound+l_height)] = i[1]\n",
    "\n",
    "        #Fill in capillary\n",
    "        if len(i) == 3:\n",
    "            cap_info = i[2]\n",
    "\n",
    "            # Extract capillary information and set variables\n",
    "            r1 = cap_info[0] / dx # Inner radius of capillary\n",
    "            r2 = cap_info[1] / dx  # Outer radius of capillary\n",
    "            cap_offset = int(lower_bound + cap_info[2]/dx) # Lower bound of capillary\n",
    "            cap_high = int(cap_offset+2*r2)  # Upper bound of capillary height\n",
    "            center = cap_offset  + r2 # Center of the capillary\n",
    "            \n",
    "            # Create 2D distance array\n",
    "            ly, lx = np.ogrid[cap_offset:cap_high+1, 0:ceil(2*r2)+1]\n",
    "            dist = np.sqrt((ly - center) ** 2 + lx ** 2)\n",
    "            \n",
    "            # Fudge coefficient, helps to fill all elements\n",
    "            eps = 0.25\n",
    "            \n",
    "            # Set lambda in the Water zone\n",
    "            l[cap_offset:cap_high + 1, 0:ceil(2 * r2) + 1][dist <= r1] = l_water\n",
    "\n",
    "            # Set capillary lambda\n",
    "            l[cap_offset:cap_high + 1, 0:ceil(2 * r2) + 1][np.logical_and(r1 - eps < dist, dist < r2 + eps)] = cap_info[-1]\n",
    "            \n",
    "\n",
    "\n",
    "        lower_bound += l_height\n",
    "    \n",
    "    #Make binary water mask\n",
    "    water_mask = l==l_water\n",
    "\n",
    "    return l, water_mask, center"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be8c5d1b",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Linear system of equations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6be78358",
   "metadata": {},
   "source": [
    "Now that the medium has been discretized,\n",
    "i use FDM to aproximate $\\nabla (\\lambda \\nabla T ) = 0 $.\n",
    "The equation as solved:\n",
    "\n",
    "$$\n",
    "\\frac{2}{k^{-1}_L+k^{-1}} (T_{i,j-1}-T_{i,j})+\\frac{2}{k^{-1}_R+k^{-1}} (T_{i,j+1}-T_{i,j})\n",
    "+\\frac{2}{k^{-1}_T+k^{-1}} (T_{i+1,j}-T_{i,j})+\\frac{2}{k^{-1}_B+k^{-1}} (T_{i-1,j}-T_{i,j})\n",
    "=0\n",
    "$$\n",
    "with T as temperature(kelvion or celsisus)(i=y,j=x), k as thermal conductivity of the nodes own grid cell or neighbour with: T - top, B - bottom, L - left, R - right. \n",
    "Each of the terms describes a heat flux to a neighboring node.\n",
    "\n",
    "and build a system of linear equations to solve for the stationary state:\n",
    "\n",
    "$AT=b$\n",
    "\n",
    "with A as the matrix of coefficients, T as the unknown temperature vector and b as the source term vector.\n",
    "\n",
    "### Implemented boundary conditions and \"ghost cells\"\n",
    "These are specific to the capillary problem i was solving.\n",
    "\n",
    "#### Bottom\n",
    "\n",
    "Neumann Boundary Condition (Type II), \n",
    "requiers a ghost cell to impose the heat flux, this is where the padding comes in.\n",
    "\n",
    "$\\frac{\\delta T}{\\delta y}_{y=0} = \\frac{q}{\\lambda}$\n",
    "\n",
    "\n",
    "#### Top\n",
    "\n",
    "At the top boundary, a fixed temperature or convective boundary condition can be specified, simulating different scenarios encountered in practical applications.\n",
    "Fixed temperature dosent require an aditonal layer, just that the boundary layer is set to\n",
    "the fixed temperature.\n",
    "But convective boundary requires a ghost cell for the temperature at infinity and to impose the prescribed flux.\n",
    "The convection is described by:\n",
    "\n",
    "$\\frac{\\delta T}{\\delta y}_{y=top} = \\alpha(T-T_0)$\n",
    "\n",
    "With $\\alpha$ as the convective constant, T as temperature of a boundary element of the medium and $T_0$ as temperature at infinity.\n",
    "\n",
    "#### Sides\n",
    "\n",
    "Sides have no heat flux trought them (also Type II), can be tiled to reconstruct a view of the wall cross-section.\n",
    "\n",
    "### Sparse Arrays\n",
    "\n",
    "Because the size of the coefficent matrix scalles as '(nx*ny, nx*ny)',\n",
    "it quiet quickly becomes too large as a numpy array to be held in ram.\n",
    "\n",
    "for example `np.zeros((100*1500,100*1500))` yields:\n",
    "\n",
    "`MemoryError: Unable to allocate 168. GiB for an array with shape (150000, 150000) and data type float64`\n",
    "\n",
    "But its mostly zeros - a [sparse matrix](https://en.wikipedia.org/wiki/Sparse_matrix),\n",
    "so `scipy.sparse` is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e7981bfd",
   "metadata": {},
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   "source": [
    "def init_Ab(l, t_top, t_water, water_mask, q, dx, l_top=None):  \n",
    "    \"\"\"\n",
    "    Initialize the coefficient matrix A, the solution vector b, and the padding value for the heat equation solver.\n",
    "\n",
    "    Args:\n",
    "        l (ndarray): Geometry array of size (ny, nx) representing the thermal conductivity at each grid point.\n",
    "        t_top (float): Temperature at the top boundary.\n",
    "        t_water (float): Temperature of the water.\n",
    "        water_mask (ndarray): Binary array indicating the locations of water cells in the grid.\n",
    "        q (float): Heat flow at the bottom boundary.\n",
    "        dx (float): Grid spacing.\n",
    "        l_top (float, optional): Thermal conductivity at the top boundary for optional convective boundary condition. Defaults to None.\n",
    "\n",
    "    Returns:\n",
    "        csr_matrix: Coefficient matrix A of size (N, N), where N is the total number of grid points.\n",
    "        ndarray: Solution vector b of size (N,).\n",
    "        int: Padding value (1 or 2) used for grid shape modification.\n",
    "\n",
    "    Notes:\n",
    "        - This function initializes the coefficient matrix A, the solution vector b, and determines the required padding for the grid based on the input parameters.\n",
    "        - The heat equation is solved on a 2D grid with ny rows and nx columns.\n",
    "        - The function handles both Dirichlet and convective boundary conditions.\n",
    "        - For Dirichlet boundary conditions, the temperature values at the boundaries are fixed.\n",
    "        - For convective boundary conditions, a convective heat flow term is added to the top boundary.\n",
    "    \"\"\"\n",
    "    # Define the grid parameters\n",
    "    ny, nx = l.shape\n",
    "    \n",
    "    # Shape modification for padding\n",
    "    if l_top == None:\n",
    "        pad = 1\n",
    "        ny += pad # Padding for bottom ghost cell\n",
    "    else:\n",
    "        pad = 2\n",
    "        ny += pad # Adds padding for top ghost cell\n",
    "    \n",
    "    # Create the coefficient matrix A and vector b\n",
    "    A = lil_array((nx*ny, nx*ny))\n",
    "    b = np.zeros((nx*ny))\n",
    "\n",
    "    \n",
    "    # Pad l to avoid indexing problems\n",
    "    line = l[0]\n",
    "    l = np.vstack((line,l))\n",
    "    if pad == 2:\n",
    "        line = l[-1]\n",
    "        l = np.vstack((l,line))\n",
    "    \n",
    "    # Pad water_mask\n",
    "    line = water_mask[0]\n",
    "    water_mask = np.vstack((line,water_mask))\n",
    "    if pad == 2:\n",
    "        line = water_mask[-1]\n",
    "        water_mask = np.vstack((water_mask,line))\n",
    "\n",
    "    \n",
    "    # SET Constants in b\n",
    "    # Top\n",
    "    b[(len(b)-nx):] = t_top\n",
    "        \n",
    "    # Water\n",
    "    b[water_mask.flatten()] = t_water\n",
    "\n",
    "    \n",
    "    # Bottom boundary derivative\n",
    "    b[:nx] = -(0.1/1000)*q/l[1,-1] #h*q/lambda  #q is in (w/m^2), i take its as h thick\n",
    "\n",
    "    \n",
    "    #SET A\n",
    "\n",
    "    #Constant T top bc\n",
    "    if l_top == None:\n",
    "        print(\"CONSTANT BC\")\n",
    "        \n",
    "        # Medium\n",
    "        for i in range(1,ny-1):\n",
    "            for j in range(1,nx-1):\n",
    "                k = i*nx+j\n",
    "\n",
    "                if not water_mask[i,j]:\n",
    "                    ip = 2/(1/l[i+1,j]+1/l[i,j]) #i+1,j\n",
    "                    im = 2/(1/l[i-1,j]+1/l[i,j]) #i-1,j\n",
    "                    jp = 2/(1/l[i,j+1]+1/l[i,j]) #i,j+1\n",
    "                    jm = 2/(1/l[i,j-1]+1/l[i,j]) #i,j-1\n",
    "                    diagonal = -(ip+im+jp+jm)\n",
    "\n",
    "                    A[k,k] = diagonal\n",
    "                    A[k,k+nx] = ip\n",
    "                    A[k,k-nx] = im\n",
    "                    A[k,k+1] = jp\n",
    "                    A[k,k-1] = jm\n",
    "\n",
    "                # Constant for water\n",
    "                else:\n",
    "                    A[k,k]=1 # This misses first elements of each row of water_mask because of j range\n",
    "\n",
    "        # Sides is 3 element average with k corrected coefficents\n",
    "        for i in range(1,ny-1):\n",
    "            # Left\n",
    "            if not water_mask[i,0]:\n",
    "                k = i*nx\n",
    "                ip = 2/(1/l[i+1,j]+1/l[i,j]) #i+1,j\n",
    "                im = 2/(1/l[i-1,j]+1/l[i,j]) #i-1,j\n",
    "                jp = 2/(1/l[i,j+1]+1/l[i,j]) #i,j+1\n",
    "                diagonal = -(ip+im+jp)\n",
    "\n",
    "                A[k,k] = diagonal\n",
    "                A[k,k+nx] = ip\n",
    "                A[k,k-nx] = im\n",
    "                A[k,k+1] = jp\n",
    "\n",
    "            else:\n",
    "                k = i*nx\n",
    "                A[k,k] = 1 #water mask first element fix\n",
    "   \n",
    "            # Right\n",
    "            k = i*nx+nx-1\n",
    "            ip = 2/(1/l[i+1,j]+1/l[i,j]) #i+1,j\n",
    "            im = 2/(1/l[i-1,j]+1/l[i,j]) #i-1,j\n",
    "            jm = 2/(1/l[i,j-1]+1/l[i,j]) #i,j-1\n",
    "            diagonal = -(ip+im+jm)\n",
    "\n",
    "            A[k,k] = diagonal\n",
    "            A[k,k+nx] = ip\n",
    "            A[k,k-nx] = im\n",
    "            A[k,k-1] = jm\n",
    "\n",
    "        #Bottom derivative (q bc) and top bc\n",
    "        for j in range(0,nx):\n",
    "            k = j\n",
    "            A[k,k] = 1\n",
    "            A[k,k+nx] = -1\n",
    "\n",
    "            #Top is constant temp (includig when convective bc)\n",
    "            k = (ny-1)*nx+j\n",
    "            A[k,k] = 1\n",
    "        \n",
    "        \n",
    "    #Convective top bc\n",
    "    else:\n",
    "        print(\"CONVECTIVE BC\")\n",
    "        \n",
    "        # Top \n",
    "        i = ny-2\n",
    "        for j in range(1,nx-1):\n",
    "                k = i*nx+j\n",
    "                # Convective heat flow\n",
    "                ip = ((0.1/1000)*l_top) #i+1,j #sussy\n",
    "                \n",
    "                im = 2/(1/l[i-1,j]+1/l[i,j]) #i-1,j\n",
    "                jp = 2/(1/l[i,j+1]+1/l[i,j]) #i,j+1\n",
    "                jm = 2/(1/l[i,j-1]+1/l[i,j]) #i,j-1\n",
    "                diagonal = -(ip+im+jp+jm)\n",
    "\n",
    "                A[k,k] = diagonal\n",
    "                A[k,k+nx] = ip\n",
    "                A[k,k-nx] = im\n",
    "                A[k,k+1] = jp\n",
    "                A[k,k-1] = jm\n",
    "        \n",
    "        # Top corner elements\n",
    "        # Left\n",
    "        j = 0\n",
    "        k = i*nx+j\n",
    "        \n",
    "        ip = ((0.1/1000)*l_top) #i+1,j\n",
    "        im = 2/(1/l[i-1,j]+1/l[i,j]) #i-1,j\n",
    "        jp = 2/(1/l[i,j+1]+1/l[i,j]) #i,j+1\n",
    "        diagonal = -(ip+im+jp)\n",
    "        \n",
    "        A[k,k] = diagonal\n",
    "        A[k,k+nx] = ip\n",
    "        A[k,k-nx] = im\n",
    "        A[k,k+1] = jp\n",
    "        \n",
    "        # Right\n",
    "        j = nx-1\n",
    "        k = i*nx+j\n",
    "        ip = ((0.1/1000)*l_top) #i+1,j\n",
    "\n",
    "        im = 2/(1/l[i-1,j]+1/l[i,j]) #i-1,j\n",
    "        jm = 2/(1/l[i,j-1]+1/l[i,j]) #i,j-1\n",
    "        diagonal = -(ip+im+jm)\n",
    "        \n",
    "        A[k,k] = diagonal\n",
    "        A[k,k+nx] = ip\n",
    "        A[k,k-nx] = im\n",
    "        A[k,k-1] = jm\n",
    "            \n",
    "        # Medium\n",
    "        for i in range(1,ny-2):\n",
    "            for j in range(1,nx-1):\n",
    "                k = i*nx+j\n",
    "\n",
    "                if not water_mask[i,j]:\n",
    "                    ip = 2/(1/l[i+1,j]+1/l[i,j]) #i+1,j\n",
    "                    im = 2/(1/l[i-1,j]+1/l[i,j]) #i-1,j\n",
    "                    jp = 2/(1/l[i,j+1]+1/l[i,j]) #i,j+1\n",
    "                    jm = 2/(1/l[i,j-1]+1/l[i,j]) #i,j-1\n",
    "                    diagonal = -(ip+im+jp+jm)\n",
    "\n",
    "                    A[k,k] = diagonal\n",
    "                    A[k,k+nx] = ip\n",
    "                    A[k,k-nx] = im\n",
    "                    A[k,k+1] = jp\n",
    "                    A[k,k-1] = jm\n",
    "                    \n",
    "                else:\n",
    "                    A[k,k]=1\n",
    "            \n",
    "        # Sides is 3 element average with k corrected coefficents\n",
    "        for i in range(1,ny-2):\n",
    "            # Left\n",
    "            if not water_mask[i,0]:\n",
    "\n",
    "                k = i*nx\n",
    "                ip = 2/(1/l[i+1,j]+1/l[i,j]) #i+1,j\n",
    "                im = 2/(1/l[i-1,j]+1/l[i,j]) #i-1,j\n",
    "                jp = 2/(1/l[i,j+1]+1/l[i,j]) #i,j+1\n",
    "                diagonal = -(ip+im+jp)\n",
    "\n",
    "                A[k,k] = diagonal\n",
    "                A[k,k+nx] = ip\n",
    "                A[k,k-nx] = im\n",
    "                A[k,k+1] = jp\n",
    "\n",
    "            else:\n",
    "                k = i*nx\n",
    "                A[k,k] = 1 # Water mask first element fix\n",
    "   \n",
    "            # Right\n",
    "            k = i*nx+nx-1\n",
    "            ip = 2/(1/l[i+1,j]+1/l[i,j]) #i+1,j\n",
    "            im = 2/(1/l[i-1,j]+1/l[i,j]) #i-1,j\n",
    "            jm = 2/(1/l[i,j-1]+1/l[i,j]) #i,j-1\n",
    "            diagonal = -(ip+im+jm)\n",
    "\n",
    "            A[k,k] = diagonal\n",
    "            A[k,k+nx] = ip\n",
    "            A[k,k-nx] = im\n",
    "            A[k,k-1] = jm\n",
    "\n",
    "            \n",
    "        #Bottom derivative (q bc) and top bc\n",
    "        for j in range(0,nx):\n",
    "            k = j\n",
    "            A[k,k] = 1\n",
    "            A[k,k+nx] = -1\n",
    "\n",
    "            # Top is constant temp (includig when convective bc)\n",
    "            k = (ny-1)*nx+j\n",
    "            A[k,k] = 1\n",
    "\n",
    "                    \n",
    "\n",
    "\n",
    "    A = A.tocsr()\n",
    "    \n",
    "    return A, b, pad"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74c3fe36",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Temperature fitter function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "47e2a3cb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def t_fit_loop(A, b, pad, l, t_to_fit, t_error, maxiter, water_mask):\n",
    "    \"\"\"\n",
    "    Itteratively fit the water temperature\n",
    "\n",
    "    Args:\n",
    "        A (sparse matrix): Coefficient matrix of the linear system.\n",
    "        b (array): Right-hand side vector of the linear system.\n",
    "        pad (int): Padding value (1 or 2) used for grid shape modification.\n",
    "        l (2D array): Geometry matrix.\n",
    "        t_to_fit (float): Target temperature value to fit.\n",
    "        t_error (float): Acceptable error for convergence.\n",
    "        maxiter (int): Maximum number of iterations allowed.\n",
    "        water_mask (2D boolean array): Mask indicating the water regions.\n",
    "\n",
    "    Returns:\n",
    "        T (2D array): Temperature distribution matrix.\n",
    "        t (float): Water temperature after convergence.\n",
    "    \"\"\"\n",
    "    t = t_to_fit\n",
    "    ny, nx = l.shape\n",
    "    \n",
    "    iteration = 0\n",
    "    diff = 1\n",
    "    \n",
    "    # Pad water_mask\n",
    "    line = water_mask[0]\n",
    "    water_mask = np.vstack((line,water_mask))\n",
    "    if pad == 2:\n",
    "        line = water_mask[-1]\n",
    "        water_mask = np.vstack((water_mask,line))\n",
    "        \n",
    "    b[water_mask.flatten()] = t_to_fit\n",
    "    \n",
    "    while abs(diff)>t_error and iteration < maxiter:\n",
    "        \n",
    "        # Solve the linear system\n",
    "        T = spsolve(A, b)\n",
    "\n",
    "        # Reshape the solution vector into a 2D array\n",
    "        if pad == 2:\n",
    "            T = T[nx:][:-nx].reshape((ny, nx))\n",
    "        else:\n",
    "            T = T[nx:].reshape((ny, nx))\n",
    "            \n",
    "        # Calculate lower surface temp\n",
    "        bot_t = T[0].mean()\n",
    "        \n",
    "        # Adjust guess\n",
    "        diff = t_to_fit - bot_t\n",
    "\n",
    "        iteration+=1\n",
    "        t += diff\n",
    "        b[water_mask.flatten()] = t\n",
    "\n",
    "        print(iteration, diff)\n",
    "    \n",
    "    return T, t"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe479a79-8488-498b-bd92-ce89a0d17500",
   "metadata": {},
   "source": [
    "### Algomerated temperature fit funtion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "70ca85d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_water_t(geo, width, t_to_fit, q, t_top, l_top=None):\n",
    "    \"\"\"\n",
    "    Init and fit water temperature for the desired parameters.\n",
    "\n",
    "    Args:\n",
    "        geo (array): Geometry information.\n",
    "        width (float): Width of the region.\n",
    "        t_to_fit (float): Target temperature value to fit.\n",
    "        q (float): Heat flux.\n",
    "        t_top (float): Temperature at the top boundary.\n",
    "        l_top (2D array, optional): Top boundary condition.\n",
    "\n",
    "    Returns:\n",
    "        t (float): Adjusted target water temperature after convergence.\n",
    "        T (2D array): Temperature distribution matrix.\n",
    "    \"\"\"\n",
    "    dx = 0.1\n",
    "    \n",
    "    # Init geometry\n",
    "    l, water_mask, center = init_l(dx, width, geo)\n",
    "    \n",
    "    # Init eq. system\n",
    "    A, b, pad = init_Ab(l, t_top, t_to_fit, water_mask, q, dx, l_top)\n",
    "    \n",
    "    print(\"init done\")\n",
    "    \n",
    "    # Fit desired water temperature\n",
    "    t_error = 0.001\n",
    "    maxiter = 20\n",
    "\n",
    "    T,t = t_fit_loop(A, b, pad, l, t_to_fit, t_error, maxiter, water_mask)\n",
    "    \n",
    "    return t, T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "937a9ba0-7343-4b7f-962b-0ea205f241af",
   "metadata": {},
   "source": [
    "# Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2dd238a",
   "metadata": {},
   "source": [
    "## Initialisation and compute to fit capillary water temp to system parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "04d6b85c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.07 ms, sys: 2.79 ms, total: 5.85 ms\n",
      "Wall time: 3.64 ms\n",
      "CONVECTIVE BC\n",
      "CPU times: user 1.77 s, sys: 55.7 ms, total: 1.83 s\n",
      "Wall time: 1.83 s\n",
      "1 4.391886435164082\n",
      "2 0.037307025174833086\n",
      "3 0.00031690576429355133\n",
      "CPU times: user 3.01 s, sys: 271 ms, total: 3.28 s\n",
      "Wall time: 3.29 s\n",
      "34.42951036610321\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 2000x3000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dx = 0.1\n",
    "width = 10 #in mm, half of the capilary step\n",
    "\n",
    "#Geometry variables from the bottom up\n",
    "h1 = 10 \n",
    "lambda1 = 0.3 #W/(m*K) #KANUF Thermoboard\n",
    "\n",
    "h2 = 100\n",
    "lambda2 = 0.037 #KNAUF TP 115\n",
    "r1 = (3 / 2)  #inner r of capillay\n",
    "r2 = (3.4 / 2)  #outer r\n",
    "offset = 0 #offset to the start of the capillary\n",
    "lambdaC = 0.22 #Polypropylene\n",
    "\n",
    "h3 = 40 \n",
    "lambda3 = 0.12 #wood\n",
    "\n",
    "\n",
    "#Geometry input\n",
    "geo = [[h1,lambda1],[h2,lambda2,[r1,r2,offset,lambdaC]],[h3,lambda3]]\n",
    "\n",
    "#Geometry init\n",
    "%time l, water_mask, center = init_l(dx, width, geo)\n",
    "\n",
    "#Boundary conditions\n",
    "q = 50 #[w/m^2]\n",
    "t_top = -20\n",
    "t_to_fit = 30\n",
    "l_top = 10\n",
    "\n",
    "#Linear system intit\n",
    "%time A, b, pad = init_Ab(l, t_top, t_to_fit, water_mask, q, dx, l_top)\n",
    "\n",
    "\n",
    "t_error = 0.001\n",
    "maxiter = 10\n",
    "\n",
    "%time T,t = t_fit_loop(A, b, pad, l, t_to_fit, t_error, maxiter, water_mask)\n",
    "print(t)\n",
    "\n",
    "plt.pcolormesh(T)\n",
    "plt.axis('equal')\n",
    "plt.colorbar()\n",
    "plt.rcParams[\"figure.figsize\"] = (6, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a5ec23f4-3d61-4c0e-af8e-4c893a3bab75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x3000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolormesh(l)\n",
    "plt.axis('equal')\n",
    "plt.colorbar()\n",
    "plt.rcParams[\"figure.figsize\"] = (6, 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df2c0f85",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Solve for a single steady state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "27925acf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CONSTANT BC\n",
      "CPU times: user 1.89 s, sys: 44.4 ms, total: 1.94 s\n",
      "Wall time: 1.95 s\n",
      "CPU times: user 1.21 s, sys: 98.2 ms, total: 1.31 s\n",
      "Wall time: 1.32 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7c58d9a4e810>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x3000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "q = 10 #[w/m^2]\n",
    "#l_top = 10 #[w/(m^2*k)]\n",
    "#l_top = None\n",
    "t_water = 45\n",
    "\n",
    "\n",
    "\n",
    "%time A, b, pad = init_Ab(l, t_top, t_water, water_mask, q, dx)\n",
    "\n",
    "# Solve the linear system\n",
    "%time T = spsolve(A, b)\n",
    "\n",
    "# Reshape the solution vector into a 2D array\n",
    "ny, nx = l.shape\n",
    "if pad == 2:\n",
    "    T = T[nx:][:-nx].reshape((ny, nx))\n",
    "else:\n",
    "    T = T[nx:].reshape((ny, nx))\n",
    "\n",
    "\n",
    "plt.pcolormesh(T)\n",
    "plt.axis('equal')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fbd04a1",
   "metadata": {},
   "source": [
    "### Tiled visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c19eb08c-f949-42d5-b287-3dc6a9f3e496",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Temperature distribution')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x3000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Tf = np.flip(T,1)\n",
    "Ts = np.hstack((T,Tf,T,Tf,T,Tf,T,Tf))\n",
    "\n",
    "plt.set_cmap(\"plasma\")\n",
    "\n",
    "plt.pcolormesh(Ts)\n",
    "plt.axis('equal')\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"mm\")\n",
    "plt.ylabel(\"mm\")\n",
    "plt.title(\"Temperature distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0a1df5d7-2352-4065-8884-fbc98f9b2e71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Geometry in terms of lambda')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x3000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lf = np.flip(l,1)\n",
    "ls = np.hstack((l,lf,l,lf,l,lf,l,lf))\n",
    "\n",
    "plt.pcolormesh(ls)\n",
    "\n",
    "plt.xlabel(\"mm\")\n",
    "plt.ylabel(\"mm\")\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.colorbar()\n",
    "plt.title(\"Geometry in terms of lambda\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d54045b2-e7ff-4ead-8359-bc511ee6aef9",
   "metadata": {},
   "source": [
    "### Temperature around the capillary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "631e0117-20e0-4164-b0a3-b01e7cf41713",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7c58d8ff18e0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "size = 50\n",
    "bottom = int(center-size)\n",
    "top = int(center+size)\n",
    "plt.rcParams[\"figure.figsize\"] = (10, 8)\n",
    "plt.pcolormesh(Ts[bottom:top,150:250], cmap = 'inferno')\n",
    "plt.axis('equal')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "050a6abf-462a-44ea-9a6d-adf8f5c7d4af",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7c58d8ff1d90>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contourf(Ts[bottom:top,150:250], cmap = 'plasma', levels = 20)\n",
    "plt.axis('equal')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f5d239d8-9baa-4c41-9479-51c810f5aa8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "mesh = ax1.pcolormesh(ls[bottom:top,150:250], cmap = 'plasma')\n",
    "ax1.axis('equal')\n",
    "\n",
    "layers = ax2.pcolormesh(Ts[bottom:top,150:250], cmap = 'inferno')\n",
    "ax2.axis('equal')\n",
    "plt.rcParams[\"figure.figsize\"] = (8, 4)\n",
    "\n",
    "#plt.savefig(\"capillary geo and temp.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "693c03cb-24c5-47a8-97f2-b24b04fdf561",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}