Bottom_Ekman_Layer_1D_Model_Adams_Bashforth_high_res.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ebf1af93",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "289be46b",
   "metadata": {},
   "source": [
    "The Second Order Runge-Kutta Method is defined by\n",
    "$$ F_{1,n}=f(t_n,x_n)$$\n",
    "$$ F_{2,n}=f(t_n+\\alpha h,x_n+\\alpha h F_{1,n})$$\n",
    "$$ x_{n+1}=x_n+\\beta hF_{1,n}+\\gamma hF_{2,n}$$\n",
    "where $\\beta+\\gamma=1$ and $\\alpha \\gamma=\\frac{1}{2}$. In this case I have chosen $\\beta=\\gamma=\\frac{1}{2}$ and $\\alpha=1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5ae05c0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rk2(t,x,x_f,f,h,*args): # I have chosen alpha=1 and beta=gamma=0.5\n",
    "    F1 = f(t,x_f,*args)\n",
    "    F2 = f(t+h,x_f+h*F1,*args)\n",
    "    return x + 0.5*h*F1+0.5*h*F2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "486b536f",
   "metadata": {},
   "source": [
    "The Quasi-Second Order Adams-Bashforth II method we use is defined as:\n",
    "$$ u^{n+1}=u^n+\\Delta t G^{(n+\\frac{1}{2})}_{u}$$\n",
    "$$ G^{(n+\\frac{1}{2})}_{u}=(\\frac{3}{2}+\\epsilon_{AB})G^{(n)}_{u}-(\\frac{1}{2}+\\epsilon_{AB})G^{(n-1)}_{u}$$\n",
    "Note that we use RK2 for the first two steps to initialize the method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "651870b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def QSAB(t,x,f,h,Gn,Gn1,dudz_Gn,dudz_Gn1,v,w,*args):\n",
    "    eps = 0\n",
    "    Gn12 = (1.5+eps)*f(t,Gn,v,w,dudz_Gn,*args)-(0.5+eps)*f(t,Gn1,v,w,dudz_Gn1,*args)\n",
    "    xn1 = x + h*Gn12\n",
    "    return xn1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2187347b",
   "metadata": {},
   "source": [
    "Below we define the system of equations the solver is resolving, they are\n",
    "$$ \\dfrac{\\partial u}{\\partial t}=fv+\\nu_E\\dfrac{\\partial^2 u}{\\partial z^2}$$\n",
    "$$ \\dfrac{\\partial v}{\\partial t}=f(\\bar{u}-u)+\\nu_E\\dfrac{\\partial^2 v}{\\partial z^2}$$\n",
    "where \n",
    "$$ \\dfrac{\\partial^2 u}{\\partial z^2}\\approx \\dfrac{u(z+\\Delta z)-2u(z)+u(z-\\Delta z)}{\\Delta z^2}$$\n",
    "$$ \\dfrac{\\partial^2 v}{\\partial z^2}\\approx \\dfrac{v(z+\\Delta z)-2v(z)+v(z-\\Delta z)}{\\Delta z^2}.$$\n",
    "Note that we assume at the bottom $u=v=0$ for all times and that $\\dfrac{\\partial u}{\\partial z}=\\dfrac{\\partial u}{\\partial z}=0$ at the top so that the second partial derivative term is 0 at the top."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "592f0cf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dudt(t,v,f,vE,dudz):\n",
    "    out = f*v+vE*dudz # full equation for the derivative of u wrt t\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4fbda5af",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dvdt(t,u,f,vE,dvdz,u_bar):\n",
    "    out = f*(u_bar-u)+vE*dvdz # full equation for the derivative of v wrt t\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0e87817e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dz = 110 # vertical resolution\n",
    "z = np.arange(0,880.001,dz) # calculate vertical points\n",
    "u_bar = 0.0025 # mean flow away from Ekman Layer\n",
    "u_prev = np.append(0,u_bar*np.ones(z.shape[0]-1)) # define initial conditions\n",
    "v_prev = 0*np.ones(z.shape[0])\n",
    "data_u = np.array([u_prev]).T # store initial conditions in a numpy array\n",
    "data_v = np.array([v_prev]).T\n",
    "h = 32 # steps size\n",
    "total = 16000000 # total amount of time the model is run\n",
    "T = np.arange(0,total,h) # time domain\n",
    "omega = ((2*np.pi)/(24*60*60)) # rotation frequenct\n",
    "phi = 0.5 #latitude\n",
    "f = 2*omega*np.sin(np.radians(phi)) # coriolis parameter\n",
    "vE = 1*10**(-2) # eddy viscosity\n",
    "uGn = u_prev\n",
    "vGn = v_prev\n",
    "for i in T[:2]: # iterate over time domain\n",
    "    dudz = (u_prev[2:]-2*u_prev[1:-1]+u_prev[0:-2])/dz**2\n",
    "    dvdz = (v_prev[2:]-2*v_prev[1:-1]+v_prev[0:-2])/dz**2\n",
    "    u = np.array([0]) # bottom conditions\n",
    "    u = np.append(u,rk2(i,u_prev[1:-1],v_prev[1:-1],dudt,h,f,vE,dudz))\n",
    "    u = np.append(u,u_bar) # top conditions\n",
    "    v = np.array([0]) # bottom conditions\n",
    "    v  = np.append(v,rk2(i,v_prev[1:-1],u_prev[1:-1],dvdt,h,f,vE,dvdz,u_bar))\n",
    "    v = np.append(v,0) # top conditions\n",
    "    if i%16000==0: #Note: For the sake of timeliness, please do not save at every time step\n",
    "        data_u = np.append(data_u,np.array([u]).T,axis=1) # data u and data v have the data from each time step stored,\n",
    "        data_v = np.append(data_v,np.array([v]).T,axis=1) # with the first dimension serving as time and second as point in z\n",
    "    u_prev = u\n",
    "    v_prev = v\n",
    "    uGn1 = uGn\n",
    "    vGn1 = vGn\n",
    "    uGn = u_prev\n",
    "    vGn = v_prev\n",
    "for i in T[2:]: # iterate over time domain\n",
    "    dudz_Gn = (uGn[2:]-2*uGn[1:-1]+uGn[0:-2])/dz**2\n",
    "    dudz_Gn1 = (uGn1[2:]-2*uGn1[1:-1]+uGn1[0:-2])/dz**2\n",
    "    dvdz_Gn = (vGn[2:]-2*vGn[1:-1]+vGn[0:-2])/dz**2\n",
    "    dvdz_Gn1 = (vGn1[2:]-2*vGn1[1:-1]+vGn1[0:-2])/dz**2\n",
    "    u = np.array([0]) # bottom conditions\n",
    "    u = np.append(u,QSAB(i,u_prev[1:-1],dudt,h,vGn[1:-1],vGn1[1:-1],dudz_Gn,dudz_Gn1,f,vE))\n",
    "    u = np.append(u,u_bar) # top conditions\n",
    "    v = np.array([0])\n",
    "    v = np.append(v,QSAB(i,v_prev[1:-1],dvdt,h,uGn[1:-1],uGn1[1:-1],dvdz_Gn,dvdz_Gn1,f,vE,u_bar))\n",
    "    v = np.append(v,0) # top conditions\n",
    "    if i%16000==0: #Note: For the sake of timeliness, please do not save at every time step\n",
    "        data_u = np.append(data_u,np.array([u]).T,axis=1) # data u and data v have the data from each time step stored,\n",
    "        data_v = np.append(data_v,np.array([v]).T,axis=1) # with the first dimension serving as time and second as point in z\n",
    "    u_prev = u\n",
    "    v_prev = v\n",
    "    uGn1 = uGn\n",
    "    vGn1 = vGn\n",
    "    uGn = u_prev\n",
    "    vGn = v_prev"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43e39dfd",
   "metadata": {},
   "source": [
    "Below calculates the Ekman depth:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b93cfa8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Ekman Depth is 125.52956019134824\n"
     ]
    }
   ],
   "source": [
    "d = np.sqrt(2*vE/f)\n",
    "print(f\"The Ekman Depth is {d}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c79c8a1",
   "metadata": {},
   "source": [
    "Below are contour plots for the components of velocity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "37143da9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f39fb707090>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cs = plt.contourf(data_u)\n",
    "plt.colorbar(cs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e6fbeb26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f39fb65f7d0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cs = plt.contourf(data_v)\n",
    "plt.colorbar(cs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77d5aff8",
   "metadata": {},
   "source": [
    "Below are the plots of the u and v components of velocity in the water column at a specific time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8203421d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'depth')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top = int(100/dz) # number of meters above bottom the graph ends at\n",
    "time = -1# time\n",
    "plt.plot(data_u[:,time],z)\n",
    "plt.xlabel(\"u component of velocity\")\n",
    "plt.ylabel(\"depth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3a5036d9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'depth')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data_v[:,time],z)\n",
    "\n",
    "plt.xlabel(\"v component of velocity\")\n",
    "plt.ylabel(\"depth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ff1e7f66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The net transport of fluid is 0.12723100608040314\n"
     ]
    }
   ],
   "source": [
    "V = np.sum(dz*data_v[:,-1])\n",
    "print(f\"The net transport of fluid is {V}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27fb2de4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71afece4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Ocean",
   "language": "python",
   "name": "ocean2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}