docs/tutorial3/tutorial3python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 3: Setting up a farm calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: this python notebook was automatically generated by make_tutorial3.py\n",
    "\n",
    "# Load the amrwind-frontend module\n",
    "amrwindfedir = '../../'  # Location of amrwind-frontend directory\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": {},
   "outputs": [],
   "source": [
    "# Start the AMR-Wind case\n",
    "case = amrwind.MyApp.init_nogui()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Introduction\n",
    "\n",
    "This tutorial will demonstrate the following features: \n",
    "- How to set up a wind farm layout with multiple turbines using\n",
    "  `amrwind-frontend`.\n",
    "- How to run a parameter sweep over wind speed and wind direction, and\n",
    "  have `amrwind-frontend` generate the necessary input files for each\n",
    "  case.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Set some wind properties\n",
    "\n",
    "For the purposes of demonstrating how to setup a wind farm layout,\n",
    "we'll start with some arbitrary wind speed and wind direction values.\n",
    "The exact values don't really matter, as later on in the tutorial,\n",
    "we'll change these values, but for now they'll be useful when plotting\n",
    "the results.\n",
    "\n",
    "Below we'll use a wind speed of 10 m/s and wind direction of 225\n",
    "degrees.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "case.setAMRWindInput('useWSDir', True)\n",
    "case.setAMRWindInput('ABL_windspeed', 10.0, forcechange=True)\n",
    "case.setAMRWindInput('ABL_winddir', 225.0, forcechange=True)\n",
    "case.ABL_calculateWindVector()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "case.setAMRWindInput('incflo.physics', ['FreeStream', 'Actuator'])\n",
    "case.setAMRWindInput('time.fixed_dt', 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create wind farm layout and domain\n",
    "\n",
    "We'll use a CSV format to provde the turbine layout information.  This\n",
    "will generally take the form of something like \n",
    "\n",
    "```\n",
    "# CSV file should have columns with\n",
    "# name, x, y, type, yaw, hubheight, options\n",
    "T0, 480, 280, UnifCtTest, , ,\n",
    "T1, 480, 680, UnifCtTest, , ,\n",
    "```\n",
    "\n",
    "Here's what each of the columns mean:\n",
    "\n",
    "### TURBINE LAYOUT SPECIFICATIONS\n",
    "\n",
    "| column      | description                                                                       | required/optional |\n",
    "| ---         | ---                                                                               | ---               |\n",
    "| `name`      | The name of the turbine (can be arbitrary).                                       | required          |\n",
    "| `x`         | The x position of the turbine.                                                    | required          |\n",
    "| `y`         | The y position of the turbine.                                                    | required          |\n",
    "| `type`      | The turbine type (must be listed under turbine types tab)                         | required          |\n",
    "| `yaw`       | The turbine yaw heading.  If not specified, turbine points directly into the wind | optional          |\n",
    "| `hubheight` | The turbine hub-height.  If not specified, uses the default from the turbine type | optional          |\n",
    "| `options`   | Optional parameters                                                               | optional          |\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "turbinescsv=\"\"\"\n",
    "# CSV file should have columns with\n",
    "# name, x, y, type, yaw, hubheight, options\n",
    "T0, 480, 280, UnifCtTest, , ,\n",
    "T1, 480, 680, UnifCtTest, , ,\n",
    "\"\"\"\n",
    "case.setAMRWindInput('turbines_csvtextbox',  turbinescsv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that if you already have a csv file saved somewhere, you can load that directly using the `loadTurbineCSVFile()` command, like\n",
    "```python\n",
    "case.loadTurbineCSVFile('turbines.csv')\n",
    "```\n",
    "instead of using `setAMRWindInput()` to set the variable directly.\n",
    "\n",
    "We'll also need to set some thing things like the turbine domainsize\n",
    "of `[960, 960, 960]` and background mesh size of `10`m."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "case.setAMRWindInput('turbines_domainsize', [960.0, 960.0, 960.0])\n",
    "case.setAMRWindInput('turbines_backgroundmeshsize', 10.0)\n",
    "case.setAMRWindInput('turbines_deleteprev', True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to set the upper and lower Z boundaries to\n",
    "`slip_wall`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "case.setAMRWindInput('zlo_type', 'slip_wall')\n",
    "case.setAMRWindInput('zhi_type', 'slip_wall')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can preview the locations and domain size using the `turbines_previewAllTurbines() command:`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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JtTmla1ZAQIBuvvlmSRd/AKkq54eUFBcX6+2333b9Kf33DPPVcN4guHHjxirPqWwQLnvs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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fff8a0ae9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preview the turbine layout\n",
    "fig, ax = plt.subplots(figsize=(5,5), facecolor='w', dpi=150)\n",
    "case.turbines_previewAllTurbines(ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this next step, we tell it to actually create the turbines specified in the CSV input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['T0', 'T1']\n"
     ]
    }
   ],
   "source": [
    "case.turbines_createAllTurbines()\n",
    "\n",
    "# Print out existing list of turbines, just to confirm that the turbines got made\n",
    "print(case.listboxpopupwindict['listboxactuator'].getitemlist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to set the density global for all of the uniform Ct\n",
    "disks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "case.setAMRWindInput('Actuator_UniformCtDisk_density', 1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create wind farm refinement zones  \n",
    "\n",
    "Now around each turbine, we'll add some refinement zones so the\n",
    "turbine rotors can be resolved.  Similar to the turbine\n",
    "specifications, we can specify all of the refinement zones through CSV\n",
    "input.  Here the `refine_csvtextbox` variable can be used to hold the\n",
    "string input, while `loadRefineCSVFile()` can be used to read a CSV\n",
    "file.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "refinementcsv=\"\"\"\n",
    "# CSV file should have columns with\n",
    "# level, upstream, downstream, lateral, below, above, options\n",
    "level, upstream, downstream, lateral, below, above, options\n",
    "0,     1,    1,   1,   0.75, 1,\n",
    "1,     0.5,  0.5, 0.5, 0.75, 1,\n",
    "\"\"\"\n",
    "case.setAMRWindInput('refine_csvtextbox', refinementcsv)\n",
    "case.setAMRWindInput('refine_deleteprev', True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The specifications for the CSV columns are given below, along\n",
    "with some optional parameters:\n",
    "\n",
    "### REFINEMENT SPECIFICATIONS\n",
    "\n",
    "| column       | description                                                      | required/optional |\n",
    "| ---          | ---                                                              | ---               |\n",
    "| `level`      | The mesh level to apply the refinement.                          | required          |\n",
    "| `upstream`   | The number of diameters or meters upstream to apply refinement   | required          |\n",
    "| `downstream` | The number of diameters or meters downstream to apply refinement | required          |\n",
    "| `lateral`    | The number of diameters or meters off to side of each turbine    | required          |\n",
    "| `below`      | The number of diameters or meters below the turbine hub-height   | required          |\n",
    "| `above`      | The number of diameters or meters above the turbine hub-height   | required          |\n",
    "| `options`    | Any additional options (see below)                               | optional          |\n",
    "\n",
    "### OPTIONAL INPUTS\n",
    "| keyword       | default    | possible values               | description                                          |\n",
    "| ---           | ---        | ---                           | ---                                                  |\n",
    "| `orientation` | `winddir`  | `winddir`, `nacdir`, `x`, `y` | Which direction the refinement box should be pointed |\n",
    "| `units`       | `diameter` | `diameter`, `meter`           | What units the values are in                         |\n",
    "| `center`      | `turbine`  | `turbine`, `farm`             | Where the center of the refinement zone is located   |\n",
    "\n",
    "\n",
    "To create the actual refinement zones in the domain, use the\n",
    "`refine_createAllZones()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['T0_level_0_zone', 'T1_level_0_zone', 'T0_level_1_zone', 'T1_level_1_zone']\n"
     ]
    }
   ],
   "source": [
    "case.refine_createAllZones()\n",
    "# Print out existing list of turbines\n",
    "print(case.listboxpopupwindict['listboxtagging'].getitemlist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's plot the turbine layouts using the plotDomain()\n",
    "method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting turbines\n"
     ]
    },
    {
     "data": {
      "image/png": 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YVYpe8qDidEKf7C3Ts+uKAj2lcyKWAdgBoQwAZ7BjJD9mvKJ3R0R4lxHLvkEs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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fff8a0d6850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the domain\n",
    "fig, ax = plt.subplots(figsize=(5,5), facecolor='w', dpi=150)\n",
    "case.popup_storteddata['plotdomain']['plot_turbines']        = case.listboxpopupwindict['listboxactuator'].getitemlist()\n",
    "case.popup_storteddata['plotdomain']['plot_refineboxes']     = case.listboxpopupwindict['listboxtagging'].getitemlist()\n",
    "case.plotDomain(ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create sampling planes\n",
    "\n",
    "Now that we have the refinement zones specified, we'd like to add some\n",
    "sampling probes in front of every turbine, and also for the wind farm\n",
    "as a whole.\n",
    "\n",
    "Here the `samplingcsv` variable will be used to hold the\n",
    "specifications for all of the things we'd like to plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "samplingcsv=\"\"\"\n",
    "# CSV file should have columns withturbinescsv=\n",
    "# name, type, upstream, downstream, lateral, below, above, n1, n2, options\n",
    "name, type, upstream, downstream, lateral, below, above, n1, n2, options\n",
    "cl1, centerline, 1,  0, none, none,  none,  11, 11, none\n",
    "rp1, rotorplane, 2,  0, none, none,  none,  11, 11, none\n",
    "sw1, streamwise, 2,  1, 1, 0.5,  0.5,  11, 11, usedx:0.25 noffsets:1\n",
    "hh,  hubheight,400,400,400, 0,  none,  11, 11, usedx:10 units:meter center:farm orientation:x\n",
    "\"\"\"\n",
    "case.setAMRWindInput('sampling_csvtextbox', samplingcsv)\n",
    "case.setAMRWindInput('sampling_deleteprev', True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The definitions for each of the columns and the possible options\n",
    "are given in the tables below:\n",
    "\n",
    "### REFINEMENT SPECIFICATIONS\n",
    "\n",
    "| column       | description                                                      | required/optional |\n",
    "| ---          | ---                                                              | ---               |\n",
    "| `level`      | The mesh level to apply the refinement.                          | required          |\n",
    "| `upstream`   | The number of diameters or meters upstream to apply refinement   | required          |\n",
    "| `downstream` | The number of diameters or meters downstream to apply refinement | required          |\n",
    "| `lateral`    | The number of diameters or meters off to side of each turbine    | required          |\n",
    "| `below`      | The number of diameters or meters below the turbine hub-height   | required          |\n",
    "| `above`      | The number of diameters or meters above the turbine hub-height   | required          |\n",
    "| `options`    | Any additional options (see below)                               | optional          |\n",
    "\n",
    "### OPTIONAL INPUTS\n",
    "| keyword       | default    | possible values               | description                                          |\n",
    "| ---           | ---        | ---                           | ---                                                  |\n",
    "| `orientation` | `winddir`  | `winddir`, `nacdir`, `x`, `y` | Which direction the refinement box should be pointed |\n",
    "| `units`       | `diameter` | `diameter`, `meter`           | What units the values are in                         |\n",
    "| `center`      | `turbine`  | `turbine`, `farm`             | Where the center of the refinement zone is located   |\n",
    "\n",
    "\n",
    "Now let's create the sampling probes using the\n",
    "`sampling_createAllProbes()` method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['T0_cl1', 'T1_cl1', 'T0_rp1', 'T1_rp1', 'T0_sw1', 'T1_sw1', 'Farm_hh']\n"
     ]
    }
   ],
   "source": [
    "case.sampling_createAllProbes(verbose=False)\n",
    "# Print out existing list of turbines\n",
    "print(case.listboxpopupwindict['listboxsampling'].getitemlist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to plot the domain we'll use the use the `plotDomain()` method again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting turbines\n"
     ]
    },
    {
     "data": {
      "image/png": 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fZ/n5RT1LqJJMvdAomXph7lZzBmXXC+3FaynXqtjWze/PxSPVHV+3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\nN7mv1ukWLHwuWBzLbj1PsVqVK2vQ1osoWlFuiBMnT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I3z9oWk1K1umrxKe+bMmcBV0/oV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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fff8a014ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the domain\n",
    "fig, ax = plt.subplots(figsize=(5,5), facecolor='w', dpi=150)\n",
    "case.popup_storteddata['plotdomain']['plot_refineboxes']     = case.listboxpopupwindict['listboxtagging'].getitemlist()\n",
    "case.popup_storteddata['plotdomain']['plot_sampleprobes']    = case.listboxpopupwindict['listboxsampling'].getitemlist()\n",
    "case.plotDomain(ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Set up a wind sweep\n",
    "\n",
    "The above instructions were to set up a single case with one wind\n",
    "speed (10 m/s) and one wind direction (225) degrees.  Now we'll\n",
    "show what happens when you want to vary these to run a parameter\n",
    "sweep. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "case.setAMRWindInput('sweep_windspeeds', '10 20')\n",
    "case.setAMRWindInput('sweep_winddirs', '270 225')\n",
    "case.setAMRWindInput('sweep_caseprefix', 'Tutorial3_Case_{CASENUM}')\n",
    "case.setAMRWindInput('sweep_usenewdirs', False)\n",
    "case.setAMRWindInput('sweep_logfile', 'Tutorial3_logfile.yaml')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "To run the sweep, use the `sweep_SetupRunParamSweep()` call:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       NUM           WS         WDir            Case name\n",
      "       ---           --         ----            ---------\n",
      "         0     10.00000    270.00000     Tutorial3_Case_0\n",
      "         1     10.00000    225.00000     Tutorial3_Case_1\n",
      "         2     20.00000    270.00000     Tutorial3_Case_2\n",
      "         3     20.00000    225.00000     Tutorial3_Case_3\n"
     ]
    }
   ],
   "source": [
    "# Set up the cases\n",
    "case.sweep_SetupRunParamSweep(verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check to make sure that the appropriate input files got\n",
    "created:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Tutorial3_Case_2.inp',\n",
       " 'Tutorial3_Case_3.inp',\n",
       " 'Tutorial3_Case_0.inp',\n",
       " 'Tutorial3_Case_1.inp']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List the amr-wind input files\n",
    "import glob\n",
    "glob.glob('Tutorial3_Case_{CASENUM}'.format(CASENUM='*'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Save the wind farm setup\n",
    "\n",
    "After doing all of this wind farm setup work, we can save all of these\n",
    "settings so that they can be reloaded and modified for future use.\n",
    "\n",
    "\n",
    "To do this we'll use the `writeFarmSetupYAML()` method, and also save\n",
    "some comments into the file at the same time.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved farm setup to Tutorial3_WindFarmSetup.yaml\n"
     ]
    }
   ],
   "source": [
    "# Save the wind farm setup\n",
    "case.setAMRWindInput('farm_usercomments', 'Tutorial3 wind farm setup parameters.')\n",
    "case.writeFarmSetupYAML('Tutorial3_WindFarmSetup.yaml')"
   ]
  }
 ],
 "metadata": {},
 "nbformat": 4,
 "nbformat_minor": 2
}