3-Uvalue_calculation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from seaborn import heatmap\n",
    "from seaborn import pairplot\n",
    "from besos import eppy_funcs as ef\n",
    "from besos import sampling\n",
    "from besos.evaluator import EvaluatorEP\n",
    "from besos.parameters import RangeParameter, FieldSelector, FilterSelector, Parameter, expand_plist, wwr, CategoryParameter, GenericSelector\n",
    "from besos.problem import EPProblem\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "in our project the u-value for both case of ventilation (on/off) is the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\n",
       " Material:NoMass,\n",
       "     LinearBridgingLayer,      !- Name\n",
       "     Rough,                    !- Roughness\n",
       "     0.6267,                   !- Thermal Resistance\n",
       "     0.01,                     !- Thermal Absorptance\n",
       "     0.01,                     !- Solar Absorptance\n",
       "     0.01;                     !- Visible Absorptance,\n",
       " \n",
       " Material:NoMass,\n",
       "     3_RVAL_3,                 !- Name\n",
       "     Rough,                    !- Roughness\n",
       "     0.18,                     !- Thermal Resistance\n",
       "     0.9,                      !- Thermal Absorptance\n",
       "     0.7,                      !- Solar Absorptance\n",
       "     0.7;                      !- Visible Absorptance,\n",
       " \n",
       " Material:NoMass,\n",
       "     4_RVAL_2,                 !- Name\n",
       "     Rough,                    !- Roughness\n",
       "     0.18,                     !- Thermal Resistance\n",
       "     0.9,                      !- Thermal Absorptance\n",
       "     0.7,                      !- Solar Absorptance\n",
       "     0.7;                      !- Visible Absorptance,\n",
       " \n",
       " Material:NoMass,\n",
       "     5_RVAL_2,                 !- Name\n",
       "     Rough,                    !- Roughness\n",
       "     0.15,                     !- Thermal Resistance\n",
       "     0.9,                      !- Thermal Absorptance\n",
       "     0.7,                      !- Solar Absorptance\n",
       "     0.7;                      !- Visible Absorptance,\n",
       " \n",
       " Material:NoMass,\n",
       "     6_RVAL_2,                 !- Name\n",
       "     Rough,                    !- Roughness\n",
       "     0.15,                     !- Thermal Resistance\n",
       "     0.9,                      !- Thermal Absorptance\n",
       "     0.7,                      !- Solar Absorptance\n",
       "     0.7;                      !- Visible Absorptance]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "building = ef.get_building('./input/on_double.idf')\n",
    "Materials = [material for material in building.idfobjects[\"MATERIAL\"]]\n",
    "Materials_NoMass = [materials for materials in building.idfobjects[\"material:NoMass\"]]\n",
    "Materials_NoMass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fixed values\n",
    "ascExt = 0.04\n",
    "ascInt = 0.1\n",
    "horExt = 0.04\n",
    "horInt = 0.13\n",
    "desExt = 0.04\n",
    "desInt = 0.17\n",
    "condutivity_insulation = .034"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To calculate U-Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>U-Value</th>\n",
       "      <th>Thickness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.8670</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.5966</td>\n",
       "      <td>0.039778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3550</td>\n",
       "      <td>0.078556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.2527</td>\n",
       "      <td>0.117333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.1962</td>\n",
       "      <td>0.156111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.1603</td>\n",
       "      <td>0.194889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.1355</td>\n",
       "      <td>0.233667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.1174</td>\n",
       "      <td>0.272444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.1035</td>\n",
       "      <td>0.311222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0926</td>\n",
       "      <td>0.350000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   U-Value  Thickness\n",
       "0   1.8670   0.001000\n",
       "1   0.5966   0.039778\n",
       "2   0.3550   0.078556\n",
       "3   0.2527   0.117333\n",
       "4   0.1962   0.156111\n",
       "5   0.1603   0.194889\n",
       "6   0.1355   0.233667\n",
       "7   0.1174   0.272444\n",
       "8   0.1035   0.311222\n",
       "9   0.0926   0.350000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uValue_Wall_temp = []\n",
    "for tick in np.linspace(0.001,0.35,10):    \n",
    "    layer_wall = [6,7,8]    \n",
    "    resistance = 0\n",
    "    for i in layer_wall:    \n",
    "        thickness_ = Materials[i].Thickness\n",
    "        conductivity_ = Materials[i].Conductivity\n",
    "        resistance += (thickness_/conductivity_)\n",
    "    resistance += (tick/condutivity_insulation)\n",
    "    U_val = (round(1/(horExt+horInt+resistance),4))\n",
    "    uValue_Wall_temp.append({'U-Value': U_val,\n",
    "                             'Thickness': tick})\n",
    "uValue_Wall = pd.DataFrame.from_dict(uValue_Wall_temp)\n",
    "uValue_Wall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>U-Value</th>\n",
       "      <th>Thickness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1598</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.4993</td>\n",
       "      <td>0.039778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3181</td>\n",
       "      <td>0.078556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.2334</td>\n",
       "      <td>0.117333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.1844</td>\n",
       "      <td>0.156111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.1523</td>\n",
       "      <td>0.194889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.1298</td>\n",
       "      <td>0.233667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.1130</td>\n",
       "      <td>0.272444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.1001</td>\n",
       "      <td>0.311222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0899</td>\n",
       "      <td>0.350000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   U-Value  Thickness\n",
       "0   1.1598   0.001000\n",
       "1   0.4993   0.039778\n",
       "2   0.3181   0.078556\n",
       "3   0.2334   0.117333\n",
       "4   0.1844   0.156111\n",
       "5   0.1523   0.194889\n",
       "6   0.1298   0.233667\n",
       "7   0.1130   0.272444\n",
       "8   0.1001   0.311222\n",
       "9   0.0899   0.350000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uValue_roof_temp = []\n",
    "for tick in np.linspace(0.001,0.35,10):    \n",
    "    layer_roof = [1,3]    \n",
    "    resistance = 0\n",
    "    for i in layer_roof:    \n",
    "        thickness_ = Materials[i].Thickness\n",
    "        conductivity_ = Materials[i].Conductivity\n",
    "        resistance += (thickness_/conductivity_)\n",
    "    resistance += (tick/condutivity_insulation)\n",
    "    resistance += Materials_NoMass[1].Thermal_Resistance\n",
    "    U_val = (round(1/(ascExt+ascInt+desInt+desExt+resistance),4))\n",
    "    uValue_roof_temp.append({'U-Value': U_val,\n",
    "                             'Thickness': tick})\n",
    "uValue_roof = pd.DataFrame.from_dict(uValue_roof_temp)\n",
    "uValue_roof"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.plot(uValue_roof['U-Value'],uValue_roof['Thickness'],label='Roof')\n",
    "plt.plot(uValue_Wall['U-Value'],uValue_Wall['Thickness'],label='Wall')\n",
    "\n",
    "plt.xlabel('U-Value')\n",
    "plt.ylabel('Thickness')\n",
    "plt.title('U-Value vs Thickness')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"It is possible to infer that U-Value is inversely proportional with respect to insulation thickness. A thinner layer will conserve less heat, therefore the U-Value will be higher.\")\n",
    "print(\"It is also possible to see that for the same thickness, the roof has a better U-value in this case than the wall.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}