docs/casestudies/SWIFT/precursor1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Set up of SWIFT neutral case for AMR-Wind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Load the modules\n",
    "amrwindfedir = '/projects/wind_uq/lcheung/amrwind-frontend/'  # where amrwind-frontend resides\n",
    "import sys, os\n",
    "sys.path.insert(1, amrwindfedir)\n",
    "\n",
    "# Load the libraries\n",
    "import amrwind_frontend as amrwind\n",
    "import matplotlib.pyplot    as plt\n",
    "\n",
    "# Also ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Make all plots inline \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Start the case\n",
    "case = amrwind.MyApp.init_nogui()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Set the basic parameters -- run for 20,000 seconds with dt=0.5\n",
    "case.setAMRWindInput('time.stop_time', 20000.0)\n",
    "case.setAMRWindInput('time.max_step',  40000)\n",
    "\n",
    "case.setAMRWindInput('time.fixed_dt',  0.5)\n",
    "case.setAMRWindInput('time.checkpoint_interval',  2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Set some basic parameters\n",
    "\n",
    "# Run an ABL case with WENO-Z\n",
    "case.setAMRWindInput('incflo.physics',      ['ABL'])\n",
    "case.setAMRWindInput('incflo.verbose',      3)\n",
    "case.setAMRWindInput('io.check_file',       'chk')\n",
    "\n",
    "case.setAMRWindInput('incflo.use_godunov',  True)\n",
    "case.setAMRWindInput('incflo.godunov_type', 'weno')\n",
    "\n",
    "# Use the 1-equation k-sgs model and corresponding source term\n",
    "case.setAMRWindInput('turbulence.model',    ['OneEqKsgsM84'])\n",
    "case.setAMRWindInput('TKE.source_terms',    ['KsgsM84Src'])\n",
    "\n",
    "# Set the viscosity\n",
    "case.setAMRWindInput('transport.viscosity', 1.5e-05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# This part is optional, set some tolerances\n",
    "# We'll load it in via a string input\n",
    "tols = \"\"\"\n",
    "nodal_proj.mg_rtol                       = 1e-06               \n",
    "nodal_proj.mg_atol                       = 1e-12               \n",
    "mac_proj.mg_rtol                         = 1e-06               \n",
    "mac_proj.mg_atol                         = 1e-12               \n",
    "diffusion.mg_rtol                        = 1e-06               \n",
    "diffusion.mg_atol                        = 1e-12               \n",
    "temperature_diffusion.mg_rtol            = 1e-10               \n",
    "temperature_diffusion.mg_atol            = 1e-13               \n",
    "\"\"\"\n",
    "case.loadAMRWindInput(tols, string=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Domain, mesh, and BC's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Set the basic dimensions of the grid: 4km x 4km x 0.96 km with 10x10x10 cells\n",
    "case.setAMRWindInput('geometry.prob_lo', [   0.0,    0.0, 0.0 ])\n",
    "case.setAMRWindInput('geometry.prob_hi', [4000.0, 4000.0, 960.0])\n",
    "case.setAMRWindInput('amr.n_cell',       [400,    400,    96])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Make it horizontally periodic\n",
    "case.setAMRWindInput('is_periodicx', True)\n",
    "case.setAMRWindInput('is_periodicy', True)\n",
    "case.setAMRWindInput('is_periodicz', False)\n",
    "\n",
    "# Set the lower wall boundary conditions\n",
    "case.setAMRWindInput('zlo.type',              'wall_model')          \n",
    "case.setAMRWindInput('zlo.temperature_type',  'wall_model')          \n",
    "case.setAMRWindInput('zlo.tke_type',          'zero_gradient')       \n",
    "case.setAMRWindInput('zhi.type',              'slip_wall')           \n",
    "case.setAMRWindInput('zhi.temperature_type',  'fixed_gradient')      \n",
    "case.setAMRWindInput('zhi.temperature',       0.003) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CANNOT update: ABLMeanBoussinesq use forcechange=True in setval()\n"
     ]
    }
   ],
   "source": [
    "# Set the ABL parameters\n",
    "case.setAMRWindInput('ICNS.source_terms',     ['ABLForcing','BoussinesqBuoyancy', 'CoriolisForcing'])\n",
    "\n",
    "case.setAMRWindInput('ABL.stats_output_frequency',   1)                   \n",
    "case.setAMRWindInput('ABL.stats_output_format',      'netcdf')\n",
    "\n",
    "# This sets the ABL wind speed to be 8.69 m/s in x direction (270)\n",
    "case.setAMRWindInput('incflo.velocity',     [8.69, 0.0, 0.0])\n",
    "case.setAMRWindInput('ABLForcing.abl_forcing_height',   32.1)\n",
    "case.setAMRWindInput('ABL.kappa',                       0.4) \n",
    "\n",
    "case.setAMRWindInput('ABL.normal_direction',      2)\n",
    "case.setAMRWindInput('ABL.surface_roughness_z0',  0.01)\n",
    "case.setAMRWindInput('ABL.reference_temperature', 314.4)\n",
    "case.setAMRWindInput('ABL.surface_temp_rate',     0.0)\n",
    "case.setAMRWindInput('ABL.surface_temp_flux',     0.0)\n",
    "\n",
    "case.setAMRWindInput('ABL.mo_beta_m',             16.0)\n",
    "case.setAMRWindInput('ABL.mo_gamma_m',            5.0)\n",
    "case.setAMRWindInput('ABL.mo_gamma_h',            5.0)\n",
    "case.setAMRWindInput('ABL.random_gauss_mean',     0.0)\n",
    "case.setAMRWindInput('ABL.random_gauss_var',      1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# This should be the location of the SWIFT site\n",
    "case.setAMRWindInput('CoriolisForcing.latitude',  33.6)\n",
    "case.setAMRWindInput('BoussinesqBuoyancy.reference_temperature', 314.4) \n",
    "\n",
    "# Set the initial profile with inversion at z=650m\n",
    "case.setAMRWindInput('ABL.temperature_heights', '0     650.0 750.0 1000.0')\n",
    "case.setAMRWindInput('ABL.temperature_values',  '314.4 314.4 322.4 323.15')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# This is a case where we don't want to use the defaults in amrwind-frontend\n",
    "case.setAMRWindInput('ABL.perturb_ref_height', 50.0)\n",
    "case.setAMRWindInput('ABL.Uperiods', 4.0)\n",
    "case.setAMRWindInput('ABL.Vperiods', 4.0)\n",
    "case.setAMRWindInput('ABL.deltaU',   1.0)\n",
    "case.setAMRWindInput('ABL.deltaV',   1.0)\n",
    "case.setAMRWindInput('ABL.theta_amplitude',   None)\n",
    "case.setAMRWindInput('ABL.cutoff_height',   None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set the sample outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "case.setAMRWindInput('time.plot_interval',        2000)\n",
    "\n",
    "case.setAMRWindInput('incflo.post_processing',    ['sampling'])            \n",
    "case.setAMRWindInput('sampling.output_frequency', 1000)                 \n",
    "case.setAMRWindInput('sampling.fields',           ['velocity', 'temperature'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Create a series of hub-height sampling planes at z=18.6, 32.1, and 45.6m\n",
    "\n",
    "sampleplane = case.get_default_samplingdict()\n",
    "# Modify the geometry\n",
    "sampleplane['sampling_name']         = 'p_hub'\n",
    "sampleplane['sampling_type']         = 'PlaneSampler'\n",
    "sampleplane['sampling_p_num_points'] = [401, 401]\n",
    "sampleplane['sampling_p_origin']     = [0, 0, 0]\n",
    "sampleplane['sampling_p_axis1']      = [4000, 0, 0]\n",
    "sampleplane['sampling_p_axis2']      = [0, 4000, 0]\n",
    "sampleplane['sampling_p_normal']     = [0, 0, 1]\n",
    "sampleplane['sampling_p_offsets']    = '18.6 32.1 45.6'\n",
    "case.add_sampling(sampleplane)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Plot the domain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
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5XO/w4cNO+/btnYYNGzpHjhxxHMdxLrvsMqdFixYn/J5Ls379emfNmjVlvu6PepKtQTg9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\n8ND/Ayovv9SnW/WNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fffa7e81a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7,7), facecolor='w', dpi=125)\n",
    "\n",
    "# Set any additional items to plot\n",
    "case.popup_storteddata['plotdomain']['plot_sampleprobes']    = ['p_hub']\n",
    "case.plotDomain(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ESTIMATED MESH SIZE\n",
      "   Level       Ncells                      Cell Size\n",
      "       0     15360000             10.0 x 10.0 x 10.0\n",
      "  TOTAL:     15360000\n"
     ]
    }
   ],
   "source": [
    "# Check the grid size\n",
    "case.estimateMeshSize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- Checking inputs --\n",
      "[ PASS] max_level:           max_level = 0 >= 0\n",
      "[ PASS] dt & CFL:            DT and CFL OK\n",
      "[ PASS] Actuator physics:    incflo.physics and ICNS.source_terms OK for Actuators\n",
      "[ SKIP] Actuator FST:        No Actuators\n",
      "[ PASS] Sampling probes:p_hub \n",
      "\n",
      "Results: \n",
      " 4 PASS\n",
      " 1 SKIP\n",
      " 0 FAIL\n",
      " 0 WARN\n"
     ]
    }
   ],
   "source": [
    "# Check that all of the inputs are set correctly\n",
    "check=case.validate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# --- Simulation time control parameters ---\n",
      "time.stop_time                           = 20000.0             # Max (simulated) time to evolve [s]\n",
      "time.max_step                            = 40000               \n",
      "time.fixed_dt                            = 0.5                 # Fixed timestep size (in seconds). If negative, then time.cfl is used\n",
      "time.checkpoint_interval                 = 2000                \n",
      "incflo.physics                           = ABL                 # List of physics models to include in simulation.\n",
      "incflo.verbose                           = 3                   \n",
      "io.check_file                            = chk                 \n",
      "incflo.use_godunov                       = true                \n",
      "incflo.godunov_type                      = weno                \n",
      "turbulence.model                         = OneEqKsgsM84        \n",
      "TKE.source_terms                         = KsgsM84Src          \n",
      "nodal_proj.mg_rtol                       = 1e-06               \n",
      "nodal_proj.mg_atol                       = 1e-12               \n",
      "mac_proj.mg_rtol                         = 1e-06               \n",
      "mac_proj.mg_atol                         = 1e-12               \n",
      "diffusion.mg_rtol                        = 1e-06               \n",
      "diffusion.mg_atol                        = 1e-12               \n",
      "temperature_diffusion.mg_rtol            = 1e-10               \n",
      "temperature_diffusion.mg_atol            = 1e-13               \n",
      "incflo.gravity                           = 0.0 0.0 -9.81       # Gravitational acceleration vector (x,y,z) [m/s^2]\n",
      "incflo.density                           = 1.0                 # Fluid density [kg/m^3]\n",
      "transport.viscosity                      = 1.5e-05             # Fluid dynamic viscosity [kg/m-s]\n",
      "transport.laminar_prandtl                = 0.7                 # Laminar prandtl number\n",
      "transport.turbulent_prandtl              = 0.3333              # Turbulent prandtl number\n",
      "\n",
      "# --- Geometry and Mesh ---\n",
      "geometry.prob_lo                         = 0.0 0.0 0.0         \n",
      "geometry.prob_hi                         = 4000.0 4000.0 960.0 \n",
      "amr.n_cell                               = 400 400 96          # Number of cells in x, y, and z directions\n",
      "amr.max_level                            = 0                   \n",
      "geometry.is_periodic                     = 1 1 0               \n",
      "zlo.type                                 = wall_model          \n",
      "zlo.temperature_type                     = wall_model          \n",
      "zlo.tke_type                             = zero_gradient       \n",
      "zhi.type                                 = slip_wall           \n",
      "zhi.temperature_type                     = fixed_gradient      \n",
      "zhi.temperature                          = 0.003               \n",
      "\n",
      "# --- ABL parameters ---\n",
      "ICNS.source_terms                        = ABLForcing BoussinesqBuoyancy CoriolisForcing  \n",
      "ABL.stats_output_frequency               = 1                   \n",
      "ABL.stats_output_format                  = netcdf              \n",
      "incflo.velocity                          = 8.69 0.0 0.0        \n",
      "ABLForcing.abl_forcing_height            = 32.1                \n",
      "ABL.kappa                                = 0.4                 \n",
      "ABL.normal_direction                     = 2                   \n",
      "ABL.surface_roughness_z0                 = 0.01                \n",
      "ABL.reference_temperature                = 314.4               \n",
      "ABL.surface_temp_rate                    = 0.0                 \n",
      "ABL.surface_temp_flux                    = 0.0                 # Surface temperature flux [K-m/s]\n",
      "ABL.mo_beta_m                            = 16.0                # Monin-Obukhov Beta m parameter\n",
      "ABL.mo_gamma_m                           = 5.0                 # Monin-Obukhov Gamma m parameter\n",
      "ABL.mo_gamma_h                           = 5.0                 # Monin-Obukhov Gamma h parameter\n",
      "ABL.random_gauss_mean                    = 0.0                 \n",
      "ABL.random_gauss_var                     = 1.0                 \n",
      "CoriolisForcing.latitude                 = 33.6                \n",
      "CoriolisForcing.rotational_time_period   = 86164.0900027       \n",
      "CoriolisForcing.north_vector             = 0.0 1.0 0.0         \n",
      "CoriolisForcing.east_vector              = 1.0 0.0 0.0         \n",
      "BoussinesqBuoyancy.reference_temperature = 314.4               \n",
      "ABL.temperature_heights                  = 0     650.0 750.0 1000.0\n",
      "ABL.temperature_values                   = 314.4 314.4 322.4 323.15\n",
      "ABL.perturb_velocity                     = false               \n",
      "ABL.perturb_ref_height                   = 50.0                \n",
      "ABL.Uperiods                             = 4.0                 \n",
      "ABL.Vperiods                             = 4.0                 \n",
      "ABL.deltaU                               = 1.0                 \n",
      "ABL.deltaV                               = 1.0                 \n",
      "ABL.perturb_temperature                  = false               \n",
      "time.plot_interval                       = 2000                \n",
      "io.plot_file                             = plt                 \n",
      "io.KE_int                                = -1                  \n",
      "incflo.post_processing                   = sampling            \n",
      "\n",
      "# --- Sampling parameters ---\n",
      "sampling.output_frequency                = 1000                \n",
      "sampling.fields                          = velocity temperature\n",
      "\n",
      "#---- sample defs ----\n",
      "sampling.labels                          = p_hub               \n",
      "sampling.p_hub.type                      = PlaneSampler        \n",
      "sampling.p_hub.num_points                = 401 401             \n",
      "sampling.p_hub.origin                    = 0.0 0.0 0.0         \n",
      "sampling.p_hub.axis1                     = 4000.0 0.0 0.0      \n",
      "sampling.p_hub.axis2                     = 0.0 4000.0 0.0      \n",
      "sampling.p_hub.normal                    = 0.0 0.0 1.0         \n",
      "sampling.p_hub.offsets                   = 18.6 32.1 45.6      \n",
      "\n",
      "#---- extra params ----\n",
      "#== END AMR-WIND INPUT ==\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Write the input file to SWIFT_Neutral87_precursor.inp\n",
    "print(case.writeAMRWindInput('SWIFT_Neutral87_precursor.inp'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
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