Case2-I2VIS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "337d84ad",
   "metadata": {},
   "source": [
    "## Importing Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6ecaf39e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "plots (generic function with 1 method)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import necessary packages\n",
    "using SparseArrays\n",
    "using LinearAlgebra\n",
    "using IterativeSolvers\n",
    "using WriteVTK\n",
    "using Printf\n",
    "using Statistics \n",
    "include(\"Grid.jl\")\n",
    "include(\"GridOperations.jl\")\n",
    "include(\"Markers.jl\")\n",
    "include(\"Stokes.jl\")\n",
    "include(\"Temperature.jl\")\n",
    "\n",
    "# note that we import pyplot last to avoid a name conflict with grid.\n",
    "using PyPlot\n",
    "include(\"Visualization.jl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "413bf5dc",
   "metadata": {},
   "source": [
    "## Material Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eee22870",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "initial_conditions! (generic function with 1 method)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "struct Materials\n",
    "    # 1 - mantle\n",
    "    # 2 - medium \n",
    "    # 3 - sticky air\n",
    "    # 4 - plume\n",
    "    alpha::Vector{Float64} # Thermal expansion (1/K)\n",
    "    rho0::Vector{Float64} # Density (kg/m^3)\n",
    "    Hr::Vector{Float64} # Radiogenic heat production (W/m^3)\n",
    "    Cp::Vector{Float64} # Heat capacity (J/kg*K)\n",
    "    kThermal::Vector{Float64} # Thermal conductivity (W/m*K)\n",
    "    eta::Vector{Float64} # Viscosity (Pa*s)\n",
    "    function Materials()\n",
    "        new([3e-5,2e-5,0.0,2e-5],[3300.0,3300.0,1.0,3200.0],[2e-8,3e-8,0.0,3e-8],[1000.0,1000.0,3.3e6,1000.0],[3.0,2.0,300.0,2.0],[1e21,1e23,1e17,1e20])\n",
    "    end    \n",
    "end\n",
    "\n",
    "function update_marker_prop!(markers::Markers,materials::Materials)\n",
    "    eta = markers.scalarFields[\"eta\"]\n",
    "    rho = markers.scalarFields[\"rho\"]\n",
    "    T = markers.scalarFields[\"T\"]\n",
    "    mmat = markers.integers[markers.integerFields[\"material\"],:]\n",
    "    for i in 1:markers.nmark\n",
    "        markers.scalars[rho,i] = materials.rho0[mmat[i]]\n",
    "    end\n",
    "end\n",
    "\n",
    "function initial_conditions!(markers::Markers,materials::Materials)\n",
    "    material = markers.integerFields[\"material\"]\n",
    "    T = markers.scalarFields[\"T\"]\n",
    "    eta = markers.scalarFields[\"eta\"]\n",
    "    alpha = markers.scalarFields[\"alpha\"]\n",
    "    Cp = markers.scalarFields[\"Cp\"]\n",
    "    Hr = markers.scalarFields[\"Hr\"]\n",
    "    \n",
    "    for i in 1:markers.nmark\n",
    "        mx = markers.x[1,i]\n",
    "        my = markers.x[2,i]\n",
    "        mr = ((mx-1.4e6)^2 + (my-4.57e5)^2)^0.5\n",
    "        if my < 5.0e4\n",
    "             # sticky air\n",
    "             markers.integers[material,i] = 3\n",
    "             markers.scalars[T,i] = 273.0\n",
    "             markers.scalars[eta,i] = materials.eta[3]\n",
    "             markers.scalars[alpha,i] = materials.alpha[3]  \n",
    "             markers.scalars[Cp,i] = materials.Cp[3]  \n",
    "             markers.scalars[Hr,i] = materials.Hr[3]  \n",
    "         elseif mr < 1.57e5\n",
    "             # plume\n",
    "             markers.integers[material,i] = 4\n",
    "             markers.scalars[T,i] = 1800.0\n",
    "             markers.scalars[eta,i] = materials.eta[4]\n",
    "             markers.scalars[alpha,i] = materials.alpha[4]            \n",
    "             markers.scalars[Cp,i] = materials.Cp[4]  \n",
    "             markers.scalars[Hr,i] = materials.Hr[4]  \n",
    "         else\n",
    "            # ambient mantle\n",
    "            markers.integers[material,i] = 1\n",
    "            markers.scalars[T,i] = 1500.0            \n",
    "            markers.scalars[eta,i] = materials.eta[1]\n",
    "            markers.scalars[alpha,i] = materials.alpha[1]            \n",
    "            markers.scalars[Cp,i] = materials.Cp[1]\n",
    "            markers.scalars[Hr,i] = materials.Hr[1]  \n",
    "         end\n",
    "         if 5.0e4 < my < 1.57e5\n",
    "            # medium\n",
    "            markers.integers[material,i] = 2\n",
    "            markers.scalars[T,i] = 1800.0\n",
    "            markers.scalars[eta,i] = materials.eta[2]\n",
    "            markers.scalars[alpha,i] = materials.alpha[2]\n",
    "            markers.scalars[Cp,i] = materials.Cp[2]\n",
    "            markers.scalars[Hr,i] = materials.Hr[2]  \n",
    "         end \n",
    "    end\n",
    "    # end loop over markers\n",
    "    update_marker_prop!(markers,materials)\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42f9cdca",
   "metadata": {},
   "source": [
    "## Model Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b50b54a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating Markers...\n",
      "  0.069890 seconds (19 allocations: 97.205 MiB, 58.33% gc time)\n",
      "Initial condition...\n",
      "  0.230143 seconds (3.11 M allocations: 49.772 MiB)\n",
      "Finished Step 1 time=0.011954738866213182 Myr\n",
      "  5.156012 seconds (7.36 M allocations: 1.135 GiB, 2.51% gc time, 37.54% compilation time)\n"
     ]
    }
   ],
   "source": [
    "function run(nstep::Int64)\n",
    "    nx = 208\n",
    "    ny = 140\n",
    "    W = 2.8e6\n",
    "    H = 7.57e5\n",
    "    gx = 0.0\n",
    "    gy = 10.0\n",
    "\n",
    "    Tbctype = [-1,-1,1,1] #left, right, top, bottom\n",
    "    Tbcval = [0.0,0.0,273.0,1500.0]\n",
    "    \n",
    "    bc = BoundaryConditions(0,0,0,0) # currently does nothing but is required argument to stokes solver.\n",
    "    materials = Materials()\n",
    "    \n",
    "    markx = 6\n",
    "    marky = 6\n",
    "    seconds_in_year = 3.15e7\n",
    "    plot_interval = 1e6*seconds_in_year # plot interval in seconds\n",
    "    end_time = 3e7*seconds_in_year\n",
    "    dtmax = plot_interval\n",
    "    grid = CartesianGrid(W,H,nx,ny)\n",
    "    println(\"Creating Markers...\")\n",
    "    @time markers = Markers(grid,[\"alpha\",\"T\",\"rho\",\"eta\",\"Cp\",\"Hr\"],[\"material\"] ; nmx=markx,nmy=marky,random=true)\n",
    "    println(\"Initial condition...\")\n",
    "    @time initial_conditions!(markers, materials)\n",
    "    \n",
    "    air_medium_interface = zeros(Float64,grid.nx+1)\n",
    "    mantle_medium_interface = zeros(Float64,grid.nx+1)\n",
    "\n",
    "    # define arrays for k, rho, cp, H at the basic nodes. Fill them with constant values for now.\n",
    "    kThermal = 3.0 .*ones(grid.ny,grid.nx);\n",
    "\n",
    "    time = 0.0\n",
    "    iout=0\n",
    "    last_plot = 0.0\n",
    "    rho_c = nothing\n",
    "    dt = 1e10\n",
    "\n",
    "      local rho_c\n",
    "      local rho_vx\n",
    "      local rho_vy\n",
    "      local alpha\n",
    "      local Hr\n",
    "      local Cp_c\n",
    "      local eta_s\n",
    "      local eta_n\n",
    "      local vxc\n",
    "      local vyc\n",
    "      local T\n",
    "      local dt\n",
    "      local dTmax\n",
    "      local dTemp\n",
    "      local Tnew\n",
    "      local Tlast\n",
    "\n",
    "    output_dir = \"test\"\n",
    "    for itime in 1:nstep\n",
    "        # 1. Transfer properties markers -> nodes\n",
    "        update_marker_prop!(markers,materials)\n",
    "        # 1a. Basic Nodes\n",
    "        eta_s_new, = marker_to_stag(markers,grid,[\"eta\",],\"basic\");\n",
    "        # 1b. Cell Centers\n",
    "        rho_c_new,Cp_c_new,alpha_new,eta_n_new,Tlast_new,Hr_new = marker_to_stag(markers,grid,[\"rho\",\"Cp\",\"alpha\",\"eta\",\"T\",\"Hr\"],\"center\")\n",
    "        # 1c. Vx and Vy nodes:\n",
    "        rho_vx_new, = marker_to_stag(markers,grid,[\"rho\",],\"vx\")\n",
    "        rho_vy_new, = marker_to_stag(markers,grid,[\"rho\",],\"vy\")\n",
    "\n",
    "        # deal with any NaN values from interpolation:\n",
    "        if itime > 1\n",
    "            if any(isnan.(eta_s_new))\n",
    "                println(\"found nan values\")\n",
    "            end\n",
    "            replace_nan!(eta_s,eta_s_new)\n",
    "            replace_nan!(rho_c,rho_c_new)\n",
    "            replace_nan!(Hr,Hr_new)\n",
    "            replace_nan!(Cp_c,Cp_c_new)\n",
    "            replace_nan!(alpha,alpha_new)\n",
    "            replace_nan!(eta_n,eta_n_new)\n",
    "            replace_nan!(Tlast,Tlast_new)\n",
    "            replace_nan!(rho_vx,rho_vx_new)\n",
    "            replace_nan!(rho_vy,rho_vy_new)\n",
    "        end\n",
    "        # Copy field data \n",
    "        rho_vx = copy(rho_vx_new)\n",
    "        rho_vy = copy(rho_vy_new)\n",
    "        rho_c = copy(rho_c_new)\n",
    "        Hr = copy(Hr_new)\n",
    "        Cp_c = copy(Cp_c_new)\n",
    "        alpha = copy(alpha_new)\n",
    "        eta_s = copy(eta_s_new)\n",
    "        eta_n = copy(eta_n_new)\n",
    "        Tlast = copy(Tlast_new)\n",
    "    \n",
    "        # 2. Assemble and solve the stokes equations\n",
    "        L,R = form_stokes(grid,eta_s,eta_n,rho_vx,rho_vy,bc,gx,gy;dt=dt)\n",
    "        stokes_solution = L\\R\n",
    "        vx,vy,P = unpack(stokes_solution,grid;ghost=true)\n",
    "    \n",
    "        # Get the velocity at the cell centers:\n",
    "        vxc,vyc = velocity_to_centers(grid,vx,vy)\n",
    "        adiabatic_heating = compute_adiabatic_heating(grid,rho_c,Tlast,alpha,gx,gy,vxc,vyc)\n",
    "        shear_heating = compute_shear_heating(grid,vx,vy,eta_n,eta_s)\n",
    "        H = (adiabatic_heating .+ shear_heating .+ Hr)\n",
    "    \n",
    "        # 3. Compute the advection timestep:\n",
    "        if itime > 1\n",
    "            this_dtmax = min(1.2*dt,dtmax)\n",
    "        else\n",
    "            this_dtmax = dtmax\n",
    "        end\n",
    "        dt = compute_timestep(grid,vxc,vyc ; dtmax=this_dtmax)\n",
    "        \n",
    "        dTmax = Inf\n",
    "        dTemp = nothing\n",
    "        Tnew = nothing\n",
    "        titer=1\n",
    "        for titer=1:2\n",
    "            # assemble and solve the energy equation\n",
    "#             println(\"Trying with timestep \",dt/3.15e7/1e6,\" Myr\")\n",
    "            L,R = assemble_energy_equation_center(grid,rho_c,Cp_c,kThermal,H,Tlast,dt,Tbctype,Tbcval)\n",
    "            Tnew = L\\R;\n",
    "            Tnew = reshape(Tnew,grid.ny,grid.nx);\n",
    "            Tnew = ghost_temperature_center(grid,Tnew,Tbctype,Tbcval);\n",
    "\n",
    "            T = copy(Tnew)\n",
    "\n",
    "            dTemp = Tnew-Tlast\n",
    "            # compute the maximum temperature change\n",
    "            dTmax = maximum(abs.(dTemp[2:end-1,2:end-1])) \n",
    "#             println(\"dTmax=\",dTmax,\" dt=\",dt/3.15e7/1e6)\n",
    "            dt = min(dt,dTmax < 10.0 ? dt : dt*10.0/dTmax)\n",
    "            if dTmax < 10.0\n",
    "                break\n",
    "            end\n",
    "        end\n",
    "\n",
    "        dT_subgrid_node = subgrid_temperature_relaxation_center!(markers,grid,Tlast,Cp_c[1,1],kThermal[1,1],dt)\n",
    "        dT_remaining = dTemp - dT_subgrid_node\n",
    "\n",
    "        cell_center_change_to_markers!(markers,grid,dT_remaining,\"T\")\n",
    "        \n",
    "        mat, = marker_to_stag(markers,grid,markers.integers[[markers.integerFields[\"material\"]],:],\"center\")\n",
    "        function get_interface(grid::CartesianGrid,mat,contour_value)\n",
    "            interface_position = zeros(Float64,grid.nx+1);\n",
    "            for j in 1:grid.nx+1\n",
    "                i = 1\n",
    "                while i <= grid.ny\n",
    "                    if mat[i,j] == contour_value\n",
    "                        interface_position[j] = grid.yc[i]\n",
    "                        break\n",
    "                elseif mat[i+1,j] < contour_value\n",
    "                        # interface is located within this cell.\n",
    "                        interface_position[j] = grid.yc[i] + (grid.yc[i+1]-grid.yc[i])/(mat[i+1,j]-mat[i,j])*(contour_value-mat[i,j])\n",
    "                        break\n",
    "                    end\n",
    "                    i = i+1\n",
    "                end\n",
    "            end\n",
    "            return interface_position\n",
    "        end\n",
    "        air_medium_interface = get_interface(grid,mat,2.5)\n",
    "        mantle_medium_interface = get_interface(grid,mat,1.5)\n",
    "\n",
    "#         if time == 0.0 || time - last_plot >= plot_interval\n",
    "#             last_plot = time \n",
    "#             name = @sprintf(\"%s/viz.%04d.vtr\",output_dir,iout)\n",
    "#             println(\"Writing visualization fle \",name)\n",
    "#             vn = velocity_to_basic_nodes(grid,vxc,vyc)\n",
    "\n",
    "#         visualization(grid,rho_c,eta_s,vn,P,Tnew[2:end-1,2:end-1],time/seconds_in_year;filename=name)\n",
    "#             name = @sprintf(\"%s/markers.%04d.vtp\",output_dir,iout)\n",
    "#             visualization(markers,time/seconds_in_year;filename=name)\n",
    "#             iout += 1\n",
    "#         end\n",
    "        \n",
    "#         println(\"Min/Max velocity: \",minimum(vyc),\" \",maximum(vyc))\n",
    "        move_markers_rk4!(markers,grid,vx,vy,dt,continuity_weight=1/3)\n",
    "        time += dt\n",
    "        println(\"Finished Step \",itime,\" time=\",time/seconds_in_year/1e6,\" Myr\")\n",
    "    end\n",
    "    return grid,markers,vxc,vyc,rho_c,eta_n,Tnew,time,air_medium_interface,mantle_medium_interface;\n",
    "end\n",
    "@time grid,markers,vxc,vyc,rho_c,eta_n,Tnew,time,air_medium_interface,mantle_medium_interface = run(1);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beeaad45",
   "metadata": {},
   "source": [
    "## Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "369d4302",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 1200x200 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 1200x400 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Figure(PyObject <Figure size 1200x400 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mat, = marker_to_stag(markers,grid,markers.integers[[markers.integerFields[\"material\"]],:],\"center\")\n",
    "x_time = @sprintf(\"%.3g\",time/3.15e7/1e6)\n",
    "\n",
    "# Model Schematic\n",
    "figure(figsize=(12,2))\n",
    "pcolor(grid.xc/1000,grid.yc/1000,mat)\n",
    "colorbar()\n",
    "plot(grid.xc/1000,air_medium_interface/1000,\"r\",label=\"air/medium interface\")\n",
    "plot(grid.xc/1000,mantle_medium_interface/1000,\"m\",label=\"mantle/medium interface\")\n",
    "title(L\"Model\\,\\,Schematic\\,\\,at\\,\\,%$x_time\\,Myr\")\n",
    "gca().invert_yaxis()\n",
    "gca().set_ylabel(L\"Depth\\,(km)\")\n",
    "gca().set_xlabel(L\"Width\\,(km)\")\n",
    "gca().set_aspect(\"equal\")\n",
    "legend(loc=\"upper right\",bbox_to_anchor=[1.6,1.0])\n",
    "show()\n",
    "\n",
    "# Temperature Profile \n",
    "figure(figsize=(12,4))\n",
    "scatter(markers.x[1,:]/1000,markers.x[2,:]/1000,c=markers.scalars[markers.scalarFields[\"T\"],:],s=0.1)\n",
    "plot(grid.xc/1000,air_medium_interface/1000,\"r\",label=\"air/medium interface\")\n",
    "plot(grid.xc/1000,mantle_medium_interface/1000,\"m\",label=\"mantle/medium interface\")\n",
    "title(L\"Temperature\\,\\,Profile\\,\\,at\\,\\,%$x_time\\,Myr\")\n",
    "colorbar(label=L\"K\")\n",
    "gca().invert_yaxis()\n",
    "gca().set_ylabel(L\"Height\\,(km)\")\n",
    "gca().set_xlabel(L\"Width\\,(km)\")\n",
    "gca().set_aspect(\"equal\")\n",
    "legend(loc=\"upper right\",bbox_to_anchor=[1.5,1.0])\n",
    "show()\n",
    "\n",
    "# Viscosity Profile \n",
    "figure(figsize=(12,4))\n",
    "scatter(markers.x[1,:]/1000,markers.x[2,:]/1000,c=log10.(markers.scalars[markers.scalarFields[\"eta\"],:]),s=0.1)\n",
    "plot(grid.xc/1000,air_medium_interface/1000,\"r\",label=\"air/medium interface\")\n",
    "plot(grid.xc/1000,mantle_medium_interface/1000,\"m\",label=\"mantle/medium interface\")\n",
    "title(L\"Viscosity\\,\\,Profile\\,\\,at\\,\\,%$x_time\\,Myr\")\n",
    "colorbar(label=L\"Pa\\cdot{s}\")\n",
    "gca().invert_yaxis()\n",
    "gca().set_ylabel(L\"Height\\,(km)\")\n",
    "gca().set_xlabel(L\"Width\\,(km)\")\n",
    "gca().set_aspect(\"equal\")\n",
    "legend(loc=\"upper right\",bbox_to_anchor=[1.5,1.0])\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ce62ec4",
   "metadata": {},
   "outputs": [],
   "source": [
    "C = (1/16) * () * (7.57e5/5.0e4)^3 * (1e17/1e21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5fa0131",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 1.8.2",
   "language": "julia",
   "name": "julia-1.8"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}