{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Using Pypbomb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Let's go over some quick examples of how you might use pypbomb to design a detonation tube for your research. Please keep in mind that the purpose of this package is to allow you to quickly iterate on design parameters, and that this process is _not_ meant to be a replacement for a more in-depth analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from itertools import product\n",
    "import warnings\n",
    "\n",
    "import cantera as ct\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pint\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "\n",
    "from pypbomb import Tube, Flange, DDT, Window, Bolt\n",
    "\n",
    "warnings.simplefilter(\"ignore\", pint.UnitStrippedWarning)\n",
    "\n",
    "ureg = pint.UnitRegistry()\n",
    "quant = ureg.Quantity\n",
    "\n",
    "sns.set_context(\"notebook\")\n",
    "sns.set_style(\"white\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Determine tube size and operating limits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "First let us pick a mixture of stoichiometric propane/air. For this example we will use ``gri30.yaml`` as the mechanism of choice for our Cantera calculations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "fuel = \"C3H8\"\n",
    "oxidizer = \"O2:1 N2:3.76\"\n",
    "material = \"316L\"\n",
    "mechanism = \"gri30.yaml\"\n",
    "gas = ct.Solution(mechanism)\n",
    "gas.set_equivalence_ratio(1, fuel, oxidizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Next, let's consider 316L since it's a commonly used stainless steel.\n",
    "\n",
    "For a list of all available tube materials, see ``Tube.available_materials``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "material = \"316L\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "At this point we should probably figure out which schedules are available across all of the potential NPS pipe sizes that we'd like to consider."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "{'10', '10s', '160', '40', '40s', '5', '5s', '80', '80s', 'XXH'}"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "potential_sizes = [\"1\", \"3\", \"4\", \"6\"]\n",
    "common_sizes = set(Tube.get_available_pipe_schedules(potential_sizes[0]))\n",
    "for size in potential_sizes[1:]:\n",
    "    common_sizes.intersection_update(set(Tube.get_available_pipe_schedules(size)))\n",
    "common_sizes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Given these options, let's choose schedules 40, 80, 120, and XXH for consideration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "potential_schedules = [\"40\", \"80\", \"XXH\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Let's also look at a range of operating temperatures, in case we need to preheat our tube."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "initial_temperatures = quant(np.linspace(20, 400, 6), \"degC\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Now let's figure out what we can do with each combination of pipe size, pipe schedule, and initial temperature. The steps we will use for each combination are:\n",
    "\n",
    "1. look up the tube dimensions,\n",
    "2. look up the maximum allowable stress,\n",
    "3. calculate the corresponding maximum pressure,\n",
    "4. look up the elastic modulus, density, and Poisson ratio of our tube material, and\n",
    "5. calculate the estimated maximum safe initial pressure that we can test at.\n",
    "\n",
    "Note that we are setting ``multiprocessing=False``, since multiprocessing can cause unexpected misbehavior when run from within a jupyter notebook. Additionally, note that steps 1-4 are entirely optional. NPS pipe dimensions, max allowable stress lookups, max pressure calculations, and selected material property values are provided as a convenience. You may provide any values you are comfortable with using as pint quantities, in any unit system supported by pint.\n",
    "\n",
    "Tabulated maximum allowable stress values are from ASME B31.1.  \n",
    "NPS Pipe dimensions are imported from [Engineers Edge](https://www.engineersedge.com/pipe_schedules.htm).  \n",
    "Material property values are imported from AzoM via the following links:\n",
    "\n",
    "* [304](https://www.azom.com/properties.aspx?ArticleID=965)\n",
    "* [316](https://www.azom.com/properties.aspx?ArticleID=863)\n",
    "* [317](https://www.azom.com/article.aspx?ArticleID=6799)\n",
    "\n",
    "Mean values are used for any properties given as a range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "calculate_results = True\n",
    "results_file = \"tube_size_results.h5\"\n",
    "\n",
    "combinations = list(product(potential_schedules, potential_sizes, initial_temperatures))\n",
    "if calculate_results:\n",
    "    results = pd.DataFrame(\n",
    "        columns=[\n",
    "            \"schedule\",\n",
    "            \"size\",\n",
    "            \"max initial pressure (psi)\",\n",
    "            \"initial temperature (F)\",\n",
    "            \"tube_temp\",\n",
    "            \"max_pressure\",\n",
    "            \"DLF\",\n",
    "        ]\n",
    "    )\n",
    "    for i, (schedule, size, initial_temperature) in enumerate(combinations):\n",
    "        dims = Tube.get_dimensions(size, schedule, unit_registry=ureg)\n",
    "        max_stress = Tube.calculate_max_stress(initial_temperature, material, welded=False, unit_registry=ureg)\n",
    "        max_pressure = Tube.calculate_max_pressure(dims[\"inner_diameter\"], dims[\"outer_diameter\"], max_stress)\n",
    "        elastic_modulus = Tube.get_elastic_modulus(material, ureg)\n",
    "        density = Tube.get_density(material, ureg)\n",
    "        poisson = Tube.get_poisson(material)\n",
    "        initial_pressure, dlf = Tube.calculate_max_initial_pressure(\n",
    "            dims[\"inner_diameter\"],\n",
    "            dims[\"outer_diameter\"],\n",
    "            initial_temperature,\n",
    "            gas.mole_fraction_dict(),\n",
    "            mechanism,\n",
    "            max_pressure.to(\"Pa\"),\n",
    "            elastic_modulus,\n",
    "            density,\n",
    "            poisson,\n",
    "            use_multiprocessing=False,\n",
    "            return_dlf=True,\n",
    "        )\n",
    "\n",
    "        current_results = pd.Series(dtype=\"object\")\n",
    "        current_results[\"schedule\"] = schedule\n",
    "        current_results[\"size\"] = size\n",
    "        current_results[\"max initial pressure (psi)\"] = initial_pressure.to(\"psi\").magnitude\n",
    "        current_results[\"initial temperature (F)\"] = initial_temperature.to(\"degF\").magnitude\n",
    "        current_results[\"tube_temp\"] = initial_temperature\n",
    "        current_results[\"max_pressure\"] = max_pressure\n",
    "        current_results[\"inner_diameter\"] = dims[\"inner_diameter\"]\n",
    "        current_results[\"DLF\"] = dlf\n",
    "        results = pd.concat((results, current_results.to_frame().T), ignore_index=True)\n",
    "\n",
    "    float_keys = [\"max initial pressure (psi)\", \"initial temperature (F)\", \"DLF\"]\n",
    "    results[float_keys] = results[float_keys].astype(float)\n",
    "\n",
    "    with warnings.catch_warnings():\n",
    "        warnings.simplefilter(\"ignore\")\n",
    "        with pd.HDFStore(results_file, \"w\") as store:\n",
    "            store.put(\"data\", results)\n",
    "\n",
    "else:\n",
    "    with pd.HDFStore(results_file, \"r\") as store:\n",
    "        results = store.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 782.975x360 with 4 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(\n",
    "    x=\"initial temperature (F)\",\n",
    "    y=\"max initial pressure (psi)\",\n",
    "    col=\"size\",\n",
    "    style=\"schedule\",\n",
    "    kind=\"line\",\n",
    "    data=results,\n",
    "    aspect=0.5,\n",
    ")\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Here we have operating limits for our mixture over varying initial temperatures across three schedules and four pipe sizes. At NPS 3 something interesting happens: schedule 80 pipe has a lower initial pressure than schedule 40 pipe. This is caused by the dynamic load factor, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 4 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"schedule\", y=\"DLF\", col=\"size\", kind=\"bar\", data=results, aspect=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Let us select NPS 6 schedule 80 pipe for the main section of our detonation tube, in order to maximize the size of the viewing windows and allow for a wider range of cell sizes to be captured by our camera."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "   schedule size             tube_temp  \\\n42       80    6   20.0 degree_Celsius   \n43       80    6   96.0 degree_Celsius   \n44       80    6  172.0 degree_Celsius   \n45       80    6  248.0 degree_Celsius   \n46       80    6  324.0 degree_Celsius   \n47       80    6  400.0 degree_Celsius   \n\n                              max_pressure  DLF inner_diameter  \n42  2.1903439367027286 kip_per_square_inch  2.0     5.761 inch  \n43   1.846139060229291 kip_per_square_inch  2.0     5.761 inch  \n44  1.5963561440335863 kip_per_square_inch  2.0     5.761 inch  \n45  1.4192312611012432 kip_per_square_inch  2.0     5.761 inch  \n46  1.3029337639270142 kip_per_square_inch  2.0     5.761 inch  \n47  1.2265926045535283 kip_per_square_inch  2.0     5.761 inch  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>schedule</th>\n      <th>size</th>\n      <th>tube_temp</th>\n      <th>max_pressure</th>\n      <th>DLF</th>\n      <th>inner_diameter</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>42</th>\n      <td>80</td>\n      <td>6</td>\n      <td>20.0 degree_Celsius</td>\n      <td>2.1903439367027286 kip_per_square_inch</td>\n      <td>2.0</td>\n      <td>5.761 inch</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>80</td>\n      <td>6</td>\n      <td>96.0 degree_Celsius</td>\n      <td>1.846139060229291 kip_per_square_inch</td>\n      <td>2.0</td>\n      <td>5.761 inch</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>80</td>\n      <td>6</td>\n      <td>172.0 degree_Celsius</td>\n      <td>1.5963561440335863 kip_per_square_inch</td>\n      <td>2.0</td>\n      <td>5.761 inch</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>80</td>\n      <td>6</td>\n      <td>248.0 degree_Celsius</td>\n      <td>1.4192312611012432 kip_per_square_inch</td>\n      <td>2.0</td>\n      <td>5.761 inch</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>80</td>\n      <td>6</td>\n      <td>324.0 degree_Celsius</td>\n      <td>1.3029337639270142 kip_per_square_inch</td>\n      <td>2.0</td>\n      <td>5.761 inch</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>80</td>\n      <td>6</td>\n      <td>400.0 degree_Celsius</td>\n      <td>1.2265926045535283 kip_per_square_inch</td>\n      <td>2.0</td>\n      <td>5.761 inch</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nps_main = \"6\"\n",
    "schedule_main = \"80\"\n",
    "results_main = results[(results[\"size\"] == nps_main) & (results[\"schedule\"] == schedule_main)].copy()\n",
    "results_original = results_main[[\"max initial pressure (psi)\", \"initial temperature (F)\"]].astype(float)\n",
    "results_main.drop([\"max initial pressure (psi)\", \"initial temperature (F)\"], axis=1, inplace=True)\n",
    "results_main"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "With operational limits for the main detonation section out of the way, let's consider a smaller, parallel tube with an internal fan to facilitate reactant mixing. Let's also try to use schedule 40, since it is cheaper and more readily available than schedule 80. An important thing to check here is that the mixing section can handle at least as much pressure as the main section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mix tube is safe :)\n"
     ]
    },
    {
     "data": {
      "text/plain": "  schedule size             tube_temp                            max_pressure  \\\n0       40    1   20.0 degree_Celsius  3.5331641285956006 kip_per_square_inch   \n1       40    1   96.0 degree_Celsius  2.9779397631133677 kip_per_square_inch   \n2       40    1  172.0 degree_Celsius  2.5750240270727587 kip_per_square_inch   \n3       40    1  248.0 degree_Celsius   2.289310321489002 kip_per_square_inch   \n4       40    1  324.0 degree_Celsius  2.1017150592216587 kip_per_square_inch   \n5       40    1  400.0 degree_Celsius  1.9785719120135372 kip_per_square_inch   \n\n   DLF                       max_main_pressure  safe  \n0  4.0  2.1903439367027286 kip_per_square_inch  True  \n1  4.0   1.846139060229291 kip_per_square_inch  True  \n2  4.0  1.5963561440335863 kip_per_square_inch  True  \n3  4.0  1.4192312611012432 kip_per_square_inch  True  \n4  4.0  1.3029337639270142 kip_per_square_inch  True  \n5  4.0  1.2265926045535283 kip_per_square_inch  True  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>schedule</th>\n      <th>size</th>\n      <th>tube_temp</th>\n      <th>max_pressure</th>\n      <th>DLF</th>\n      <th>max_main_pressure</th>\n      <th>safe</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>40</td>\n      <td>1</td>\n      <td>20.0 degree_Celsius</td>\n      <td>3.5331641285956006 kip_per_square_inch</td>\n      <td>4.0</td>\n      <td>2.1903439367027286 kip_per_square_inch</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>40</td>\n      <td>1</td>\n      <td>96.0 degree_Celsius</td>\n      <td>2.9779397631133677 kip_per_square_inch</td>\n      <td>4.0</td>\n      <td>1.846139060229291 kip_per_square_inch</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>40</td>\n      <td>1</td>\n      <td>172.0 degree_Celsius</td>\n      <td>2.5750240270727587 kip_per_square_inch</td>\n      <td>4.0</td>\n      <td>1.5963561440335863 kip_per_square_inch</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>40</td>\n      <td>1</td>\n      <td>248.0 degree_Celsius</td>\n      <td>2.289310321489002 kip_per_square_inch</td>\n      <td>4.0</td>\n      <td>1.4192312611012432 kip_per_square_inch</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>40</td>\n      <td>1</td>\n      <td>324.0 degree_Celsius</td>\n      <td>2.1017150592216587 kip_per_square_inch</td>\n      <td>4.0</td>\n      <td>1.3029337639270142 kip_per_square_inch</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>40</td>\n      <td>1</td>\n      <td>400.0 degree_Celsius</td>\n      <td>1.9785719120135372 kip_per_square_inch</td>\n      <td>4.0</td>\n      <td>1.2265926045535283 kip_per_square_inch</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nps_mix = \"1\"\n",
    "schedule_mix = \"40\"\n",
    "results_mix = results[(results[\"size\"] == nps_mix) & (results[\"schedule\"] == schedule_mix)].drop(\n",
    "    [\"max initial pressure (psi)\", \"initial temperature (F)\", \"inner_diameter\"], axis=1\n",
    ")\n",
    "results_mix[\"max_main_pressure\"] = results_main[\"max_pressure\"].values\n",
    "results_mix[\"safe\"] = results_mix[\"max_pressure\"] > results_mix[\"max_main_pressure\"]\n",
    "if all(results_mix[\"safe\"]):\n",
    "    print(\"Mix tube is safe :)\")\n",
    "else:\n",
    "    print(\"Mix tube is unsafe :(\")\n",
    "results_mix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Select proper flanges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Now we need to know what class of flanges each of our sections require. For this we will use the maximum system pressure, which is the pressure behind the reflected detonation wave at the maximum allowable initial pressure.\n",
    "\n",
    "For a list of all available flange materials, see ``Flange.available_materials``.\n",
    "\n",
    "Note that flange sizing lookups are performed using ASME B16.5 flange ratings. Ratings are included for material groups 2.1, 2.2, and 2.3. If you wish to add a different material, you may add it to lookup_data/materials_list.csv. If your new material isn't in the listed material groups, create a new flange ratings group CSV for your material. The material will have to be added to the B31.1 stress limits CSV files as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Main section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "'1500'"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_main[\"flange class\"] = results_main.apply(\n",
    "    lambda x: Flange.get_class(x[\"max_pressure\"], x[\"tube_temp\"], material, ureg), axis=1\n",
    ")\n",
    "results_main[\"flange class\"].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Mixing section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "'1500'"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_mix[\"flange class\"] = results_mix.apply(\n",
    "    lambda x: Flange.get_class(x[\"max_main_pressure\"], x[\"tube_temp\"], material, ureg), axis=1\n",
    ")\n",
    "results_mix[\"flange class\"].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Determine window dimensions and bolt pattern"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Window Dimensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Since our main tube is NPS-6 schedule 80 pipe, the inner diameter is 5.76 inches. Therefore, let's design a viewing section with a visible window height of 5.75 inches. First, let's figure out how thick a fused quartz window needs to be in order to have a safety factor of 4. Also, let's try to constrain ourselves to keeping the window thickness under 1 inch if we can."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "window_height = quant(5.75, \"in\")\n",
    "window_lengths = np.linspace(0.25, 7, 100)\n",
    "window_thicknesses = (\n",
    "    Window.minimum_thickness(\n",
    "        length=window_height,\n",
    "        width=quant(window_lengths, \"in\"),\n",
    "        safety_factor=4,\n",
    "        pressure=results_main[\"max_pressure\"].max(),\n",
    "        rupture_modulus=(197.9, \"MPa\"),\n",
    "        unit_registry=ureg,\n",
    "    )\n",
    "    .to(\"in\")\n",
    "    .magnitude\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_desired_thickness = 1  # inch\n",
    "plt.plot(window_lengths, window_thicknesses, \"k\")\n",
    "plt.fill_between(\n",
    "    window_lengths[window_thicknesses <= max_desired_thickness],\n",
    "    window_thicknesses[window_thicknesses <= max_desired_thickness],\n",
    "    0,\n",
    "    color=\"g\",\n",
    "    alpha=0.25,\n",
    "    zorder=-1,\n",
    ")\n",
    "plt.xlim([window_lengths.min(), window_lengths.max()])\n",
    "plt.ylim([0, plt.ylim()[1]])\n",
    "plt.xlabel(\"Window length (in)\")\n",
    "plt.ylabel(\"Window thickness (in)\")\n",
    "plt.title(\n",
    "    \"Window minimum thickness vs. length\\n\"\n",
    "    \"Max length {:3.2f} in at {:3.2f} in. thick\".format(\n",
    "        window_lengths[window_thicknesses <= max_desired_thickness].max(), max_desired_thickness\n",
    "    )\n",
    ")\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "let's go with a 2.25 inch wide window (due to camera limitations), and stick to 1 inch thick. what's the safety factor?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "7.95962038236468"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "window_length = quant(2.25, \"in\")\n",
    "Window.safety_factor(\n",
    "    window_length,\n",
    "    window_height,\n",
    "    quant(1, \"in\"),\n",
    "    pressure=results_main[\"max_pressure\"].max(),\n",
    "    rupture_modulus=quant(197.9, \"MPa\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Nice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Bolt pattern"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Now let's see how many 1/4-28 grade 8 bolts we need to clamp our window in place if we can tap them 1/2 inch into the viewing section plate.\n",
    "\n",
    "Note: currently only ANSI inch bolt dimensional lookups are supported. [Internal](https://www.engineersedge.com/thread_strength/internal_screw_threads_chart.htm) and [external](https://www.engineersedge.com/screw_threads_chart.htm) thread dimensions are from Engineers Edge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_bolts = np.array(range(1, 25))\n",
    "bolt_safety_factors = Bolt.calculate_safety_factors(\n",
    "    max_pressure=results_main[\"max_pressure\"].max(),\n",
    "    window_area=window_length * window_height,\n",
    "    num_bolts=num_bolts,\n",
    "    thread_size=\"1/4-28\",\n",
    "    thread_class=\"2\",\n",
    "    bolt_max_tensile=(150, \"kpsi\"),  # grade 8\n",
    "    plate_max_tensile=(485, \"MPa\"),  # 316L,\n",
    "    engagement_length=(0.5, \"in\"),\n",
    "    unit_registry=ureg,\n",
    ")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(num_bolts, bolt_safety_factors[\"bolt\"], \"k--\", label=\"bolt\")\n",
    "ax.plot(num_bolts, bolt_safety_factors[\"plate\"], \"k-.\", label=\"plate\")\n",
    "\n",
    "ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "ax.legend()\n",
    "ax.set_ylim([0, ax.get_ylim()[1]])\n",
    "ax.set_xlim([num_bolts.min() - 1, num_bolts.max() + 1])\n",
    "ax.set_xlabel(\"Number of bolts\")\n",
    "ax.set_ylabel(\"Safety factor\")\n",
    "ax.set_title(\"Bolt and plate safety factors vs. number of bolts\")\n",
    "ax.fill_between(\n",
    "    ax.get_xlim(),\n",
    "    2,\n",
    "    zorder=-1,\n",
    "    color=\"r\",\n",
    "    alpha=0.25,\n",
    ")\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "For a safety factor of 2.5, we will need a minimum of 12 bolts. Another important thing to pay attention to: The bolt safety factor is less than the plate safety factor. This is desirable; bolts are much cheaper to replace than a detonation tube, particularly when the part of the tube in question is machined from a very large and expensive piece of stainless steel!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Estimate DDT length"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Now let's estimate how long it will take a deflagration ignited at one end of the tube to transition into a detonation. The `calculate_blockage_diameter` and `calculate_blockage_ratio` functions included in `DDT` are for a Shchelkin spiral. This is not the only blockage pattern that can be used, however if you want to use an arbitrary blockage you will have to handle blockage ratio calculations on your own. That being said, the `DDT.calculate_run_up` function accepts arguments of blockage ratio and tube diameter, meaning that its only geometric assumption is a circular chamber cross-section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target blockage diameter: 0.74 inch\n"
     ]
    }
   ],
   "source": [
    "main_id = results_main[\"inner_diameter\"].iloc[0]\n",
    "target_blockage_diameter = DDT.calculate_blockage_diameter(main_id, 0.45, unit_registry=ureg)\n",
    "print(\"Target blockage diameter: {:3.2f}\".format(target_blockage_diameter))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual blockage ratio: 45.3%\n"
     ]
    }
   ],
   "source": [
    "blockage_actual = DDT.calculate_blockage_ratio(main_id, (0.75, \"in\"), unit_registry=ureg)\n",
    "print(\"Actual blockage ratio: {:4.1f}%\".format(blockage_actual * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Runup distance: 6.14 millifurlong\n"
     ]
    }
   ],
   "source": [
    "runup = DDT.calculate_run_up(\n",
    "    blockage_actual, main_id, (70, \"degF\"), (1, \"atm\"), gas.mole_fraction_dict(), mechanism, unit_registry=ureg\n",
    ")\n",
    "print(\"Runup distance: {:1.2f}\".format(runup.to(\"millifurlong\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Of course, since this package makes use of `pint`, you may use whatever ridiculous units you want for inputs and outputs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Find safe operation limits for a new mixture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Finally, since the dynamic load factor is a function of wave speed as well as the tube's geometry and material properties, the operational limits of your tube may change from mixture to mixture. Let's take a look at what would happen to our safe operation limits for this tube if we decided to pack it with hydrogen and oxygen instead of propane and air."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "results_file = \"second_mixture_results.h5\"\n",
    "\n",
    "calculate_results = True\n",
    "new_fuel = \"H2\"\n",
    "new_oxidizer = \"O2\"\n",
    "gas2 = ct.Solution(mechanism)\n",
    "gas2.set_equivalence_ratio(1, new_fuel, new_oxidizer)\n",
    "\n",
    "if calculate_results:\n",
    "    results = pd.DataFrame(\n",
    "        columns=[\n",
    "            \"max initial pressure (psi)\",\n",
    "            \"initial temperature (F)\",\n",
    "        ]\n",
    "    )\n",
    "    for i, initial_temperature in enumerate(initial_temperatures):\n",
    "        dims = Tube.get_dimensions(nps_main, schedule_main, unit_registry=ureg)\n",
    "        max_stress = Tube.calculate_max_stress(initial_temperature, material, welded=False, unit_registry=ureg)\n",
    "        max_pressure = Tube.calculate_max_pressure(dims[\"inner_diameter\"], dims[\"outer_diameter\"], max_stress)\n",
    "        elastic_modulus = Tube.get_elastic_modulus(material, ureg)\n",
    "        density = Tube.get_density(material, ureg)\n",
    "        poisson = Tube.get_poisson(material)\n",
    "        initial_pressure = Tube.calculate_max_initial_pressure(\n",
    "            dims[\"inner_diameter\"],\n",
    "            dims[\"outer_diameter\"],\n",
    "            initial_temperature,\n",
    "            gas2.mole_fraction_dict(),\n",
    "            mechanism,\n",
    "            max_pressure.to(\"Pa\"),\n",
    "            elastic_modulus,\n",
    "            density,\n",
    "            poisson,\n",
    "            use_multiprocessing=False,\n",
    "        )\n",
    "\n",
    "        current_results = pd.Series(dtype=\"object\")\n",
    "        current_results[\"max initial pressure (psi)\"] = initial_pressure.to(\"psi\").magnitude\n",
    "        current_results[\"initial temperature (F)\"] = initial_temperature.to(\"degF\").magnitude\n",
    "        results = pd.concat((results, current_results.to_frame().T), ignore_index=True)\n",
    "\n",
    "    with warnings.catch_warnings():\n",
    "        warnings.simplefilter(\"ignore\")\n",
    "        with pd.HDFStore(results_file, \"w\") as store:\n",
    "            store.put(\"data\", results)\n",
    "\n",
    "else:\n",
    "    with pd.HDFStore(results_file, \"r\") as store:\n",
    "        results = store.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_original[\"mixture\"] = \"original\"\n",
    "results[\"mixture\"] = \"new\"\n",
    "sns.lineplot(\n",
    "    x=\"initial temperature (F)\",\n",
    "    y=\"max initial pressure (psi)\",\n",
    "    data=pd.concat((results_original, results)),\n",
    "    style=\"mixture\",\n",
    "    color=\"k\",\n",
    ")\n",
    "plt.title(\"Mixture comparison\\nMax safe initial pressure vs. Temperature\")\n",
    "sns.despine()"
   ]
  }
 ],
 "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}