3.Xfoil_runner_extract_value.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7180b991",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"Runs an XFOIL analysis for a given airfoil and flow conditions\"\"\"\n",
    "import os\n",
    "import subprocess\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "import warnings\n",
    "%run 2.Hicks_Henne_NEW_Final.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f89db345",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Inputs\n",
    "alpha = 5\n",
    "Re = 1000000\n",
    "n_iter = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b0d3ca2d-1af4-47e1-a472-05adaaf82d8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "if globals().get('__file__', '') == '':\n",
    "    # Save the current directory\n",
    "    original_directory = os.getcwd()\n",
    "    \n",
    "    # Navigate to another folder\n",
    "    os.chdir(original_directory + '\\\\Deformed_foils')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "16c7a6f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Xfoil_runner(alpha, Re, n_iter, airfoil_name):\n",
    "    \n",
    "    if os.path.exists(\"polar_file.txt\"):\n",
    "        os.remove(\"polar_file.txt\")\n",
    "\n",
    "    input_file = open(\"input_file.in\", 'w')\n",
    "    input_file.write(\"LOAD {0}.dat\\n\".format(airfoil_name))\n",
    "    input_file.write(airfoil_name + '\\n')\n",
    "#     input_file.write(\"PANE\\nN {0}\".format(n_panels))\n",
    "    input_file.write(\"PANE\\n\")\n",
    "    input_file.write(\"OPER\\n\")\n",
    "    input_file.write(\"Visc {0}\\n\".format(Re))\n",
    "#     input_file.write(\"Mach {0}\\n\".format(Mach))\n",
    "    input_file.write(\"PACC\\n\")\n",
    "    input_file.write(\"polar_file.txt\\n\\n\")\n",
    "    input_file.write(\"ITER {0}\\n\".format(n_iter))\n",
    "#     input_file.write(\"Aseq {0} {1} {2}\\n\".format(alpha_i,alpha_f,step))\n",
    "    input_file.write(\"Alfa {0}\\n\".format(alpha))\n",
    "    input_file.write(\"\\n\\n\")\n",
    "    input_file.write(\"quit\\n\")\n",
    "    input_file.close()\n",
    "\n",
    "    subprocess.call(f'xfoil.exe < input_file.in & echo quit', shell=True)\n",
    "\n",
    "    \n",
    "    with warnings.catch_warnings(record=True) as w:\n",
    "        warnings.simplefilter(\"always\")\n",
    "        \n",
    "        # Try to load data from the file\n",
    "        polar_data = np.loadtxt(\"polar_file.txt\", skiprows=12)\n",
    "\n",
    "        # Check for the specific UserWarning\n",
    "        for warning in w:\n",
    "            if issubclass(warning.category, UserWarning) and \"input contained no data\" in str(warning.message):\n",
    "#                 print(f\"Warning encountered in iteration {i}: {warning.message}\")\n",
    "                break  # Stop checking for warnings once the relevant one is found\n",
    "                    \n",
    "    if len(polar_data)==0:\n",
    "        return np.nan,np.nan\n",
    "        \n",
    "    else:\n",
    "        return polar_data[1], polar_data[2]\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a2cc0228-22f4-481d-b79a-3b596b48cbec",
   "metadata": {},
   "outputs": [],
   "source": [
    "def deformed_foil_data_from_Xfoil(alpha, Re, n_iter, num_samples):\n",
    "    #outputs\n",
    "    CL = []\n",
    "    CD = []\n",
    "    for i in range(num_samples):\n",
    "        airfoil_name = \"Deformed_airfoil_\"+str(i)\n",
    "        cl_i, cd_i = Xfoil_runner(alpha, Re, n_iter, airfoil_name)\n",
    "        CL.append(cl_i)\n",
    "        CD.append(cd_i)\n",
    "        print(i,\"-------->\",cl_i,cd_i)\n",
    "    data = {'CL': CL,'CD': CD}\n",
    "    CL_CD = pd.DataFrame(data)\n",
    "    CL_CD\n",
    "    \n",
    "    # Return to the original directory\n",
    "    os.chdir(original_directory)\n",
    "    CL_CD.to_excel('Cl_Cd.xlsx')\n",
    "    return CL_CD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "190b2e8e-a840-4792-a6f4-d1bc0338d79f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 --------> 0.5294 0.00733\n",
      "1 --------> 0.6747 0.00947\n",
      "2 --------> 0.4693 0.00959\n",
      "3 --------> 0.5034 0.00928\n",
      "4 --------> 0.6249 0.00916\n",
      "5 --------> 0.3759 0.00897\n",
      "6 --------> nan nan\n",
      "7 --------> 0.6193 0.0078\n",
      "8 --------> nan nan\n",
      "9 --------> 0.4217 0.00862\n",
      "10 --------> 0.4623 0.00919\n",
      "11 --------> 0.7092 0.0088\n",
      "12 --------> 0.676 0.00872\n",
      "13 --------> 0.576 0.01041\n",
      "14 --------> 0.4666 0.00986\n",
      "15 --------> 0.4724 0.00907\n",
      "16 --------> 0.6419 0.00857\n",
      "17 --------> 0.4837 0.01024\n",
      "18 --------> 0.6614 0.01014\n",
      "19 --------> 0.4563 0.00897\n",
      "20 --------> 0.5525 0.00722\n",
      "21 --------> 0.5126 0.00869\n",
      "22 --------> 0.6488 0.00895\n",
      "23 --------> 0.4382 0.00872\n",
      "24 --------> 0.5918 0.00784\n",
      "25 --------> 0.4516 0.00813\n",
      "26 --------> 0.6858 0.01056\n",
      "27 --------> 0.4035 0.00821\n",
      "28 --------> 0.6334 0.00862\n",
      "29 --------> 0.4084 0.01023\n",
      "30 --------> 0.5371 0.00812\n",
      "31 --------> 0.5877 0.00902\n",
      "32 --------> 0.5784 0.00815\n",
      "33 --------> 0.4289 0.00881\n",
      "34 --------> 0.6127 0.01047\n",
      "35 --------> 0.4925 0.00877\n",
      "36 --------> 0.4787 0.00762\n",
      "37 --------> 0.6082 0.00974\n",
      "38 --------> 0.6259 0.00844\n",
      "39 --------> 0.5188 0.00892\n",
      "40 --------> 0.5236 0.00808\n",
      "41 --------> 0.5355 0.00925\n",
      "42 --------> 0.6814 0.0097\n",
      "43 --------> 0.4946 0.00841\n",
      "44 --------> nan nan\n",
      "45 --------> 0.4136 0.00951\n",
      "46 --------> 0.5022 0.00886\n",
      "47 --------> 0.6352 0.00862\n",
      "48 --------> nan nan\n",
      "49 --------> 0.5862 0.01007\n",
      "50 --------> 0.5289 0.00925\n",
      "51 --------> 0.5567 0.00878\n",
      "52 --------> 0.5731 0.00782\n",
      "53 --------> 0.544 0.00905\n",
      "54 --------> 0.4784 0.00907\n",
      "55 --------> 0.6627 0.008\n",
      "56 --------> 0.5621 0.00734\n",
      "57 --------> 0.5314 0.00885\n",
      "58 --------> 0.3508 0.00952\n",
      "59 --------> 0.7004 0.00926\n",
      "60 --------> 0.572 0.00881\n",
      "61 --------> 0.5138 0.01008\n",
      "62 --------> 0.5527 0.00954\n",
      "63 --------> 0.5935 0.00819\n",
      "64 --------> 0.7555 0.00879\n",
      "65 --------> 0.4036 0.00871\n",
      "66 --------> 0.6148 0.01086\n",
      "67 --------> 0.4159 0.00867\n",
      "68 --------> 0.6542 0.00723\n",
      "69 --------> 0.4158 0.0111\n",
      "70 --------> 0.5195 0.00893\n",
      "71 --------> 0.6043 0.00816\n",
      "72 --------> 0.687 0.0086\n",
      "73 --------> 0.4173 0.00906\n",
      "74 --------> 0.5765 0.00926\n",
      "75 --------> 0.6369 0.00846\n",
      "76 --------> 0.5644 0.00848\n",
      "77 --------> 0.4517 0.00926\n",
      "78 --------> 0.6692 0.00992\n",
      "79 --------> 0.4572 0.00878\n",
      "80 --------> 0.6246 0.00815\n",
      "81 --------> 0.51 0.00933\n",
      "82 --------> 0.4485 0.01016\n",
      "83 --------> 0.6117 0.00881\n",
      "84 --------> 0.5319 0.00825\n",
      "85 --------> 0.5544 0.00905\n",
      "86 --------> 0.6362 0.00814\n",
      "87 --------> 0.5075 0.0083\n",
      "88 --------> 0.4839 0.00768\n",
      "89 --------> 0.6634 0.00947\n",
      "90 --------> 0.5464 0.00896\n",
      "91 --------> nan nan\n",
      "92 --------> 0.6554 0.00954\n",
      "93 --------> 0.4848 0.00948\n",
      "94 --------> 0.5273 0.00914\n",
      "95 --------> 0.5295 0.00858\n",
      "96 --------> 0.5238 0.00762\n",
      "97 --------> 0.5756 0.00938\n",
      "98 --------> 0.4023 0.01008\n",
      "99 --------> 0.6352 0.00891\n",
      "100 --------> 0.5088 0.009\n",
      "101 --------> 0.5526 0.00948\n",
      "102 --------> 0.5805 0.00901\n",
      "103 --------> 0.5356 0.00798\n",
      "104 --------> 0.5179 0.00823\n",
      "105 --------> 0.7187 0.009\n",
      "106 --------> 0.5108 0.00915\n",
      "107 --------> 0.4807 0.00817\n",
      "108 --------> 0.5636 0.00843\n",
      "109 --------> 0.5802 0.00948\n",
      "110 --------> 0.458 0.00942\n",
      "111 --------> 0.5743 0.00947\n",
      "112 --------> 0.5855 0.00843\n",
      "113 --------> 0.4867 0.00971\n",
      "114 --------> 0.5786 0.01068\n",
      "115 --------> 0.5489 0.00888\n",
      "116 --------> 0.7122 0.00769\n",
      "117 --------> 0.4471 0.00943\n",
      "118 --------> 0.4577 0.00879\n",
      "119 --------> 0.5599 0.00877\n",
      "120 --------> 0.798 0.00831\n",
      "121 --------> 0.4072 0.00816\n",
      "122 --------> 0.4803 0.00975\n",
      "123 --------> 0.5037 0.00883\n",
      "124 --------> 0.4237 0.00798\n",
      "125 --------> 0.5359 0.01019\n",
      "126 --------> 0.6374 0.0088\n",
      "127 --------> 0.5924 0.00868\n",
      "128 --------> 0.4941 0.00744\n",
      "129 --------> 0.524 0.00975\n",
      "130 --------> nan nan\n",
      "131 --------> 0.4702 0.00918\n",
      "132 --------> 0.6972 0.00856\n",
      "133 --------> 0.3121 0.00937\n",
      "134 --------> 0.7092 0.00962\n",
      "135 --------> 0.5349 0.00829\n",
      "136 --------> 0.634 0.00788\n",
      "137 --------> 0.473 0.00948\n",
      "138 --------> 0.6887 0.01003\n",
      "139 --------> 0.4685 0.00894\n",
      "140 --------> 0.5101 0.00864\n",
      "141 --------> 0.4771 0.00912\n",
      "142 --------> 0.6699 0.00973\n",
      "143 --------> 0.4604 0.00834\n",
      "144 --------> 0.685 0.00853\n",
      "145 --------> 0.4171 0.00898\n",
      "146 --------> 0.4 0.00927\n",
      "147 --------> 0.6303 0.00877\n",
      "148 --------> 0.5519 0.00798\n",
      "149 --------> nan nan\n",
      "150 --------> 0.5698 0.00966\n",
      "151 --------> 0.6183 0.0093\n",
      "152 --------> 0.5761 0.00828\n",
      "153 --------> 0.5081 0.00832\n",
      "154 --------> 0.591 0.01041\n",
      "155 --------> 0.4695 0.00868\n",
      "156 --------> 0.5669 0.00796\n",
      "157 --------> 0.4566 0.01009\n",
      "158 --------> 0.4584 0.00807\n",
      "159 --------> 0.6745 0.00855\n",
      "160 --------> nan nan\n",
      "161 --------> 0.5618 0.00866\n",
      "162 --------> 0.3672 0.01052\n",
      "163 --------> 0.5746 0.00806\n",
      "164 --------> 0.4513 0.0081\n",
      "165 --------> 0.5701 0.01051\n",
      "166 --------> 0.4748 0.00865\n",
      "167 --------> 0.6859 0.00891\n",
      "168 --------> 0.4882 0.00831\n",
      "169 --------> 0.5522 0.00937\n",
      "170 --------> 0.5617 0.00871\n",
      "171 --------> 0.594 0.00865\n",
      "172 --------> 0.5929 0.00802\n",
      "173 --------> 0.5663 0.00997\n",
      "174 --------> 0.4075 0.00973\n",
      "175 --------> 0.5929 0.00822\n",
      "176 --------> 0.5279 0.00819\n",
      "177 --------> 0.562 0.0097\n",
      "178 --------> 0.6143 0.00967\n",
      "179 --------> 0.454 0.00822\n",
      "180 --------> 0.6258 0.00767\n",
      "181 --------> 0.3971 0.00927\n",
      "182 --------> 0.5444 0.00862\n",
      "183 --------> 0.6293 0.00916\n",
      "184 --------> nan nan\n",
      "185 --------> 0.557 0.00929\n",
      "186 --------> 0.5266 0.00975\n",
      "187 --------> 0.5864 0.0096\n",
      "188 --------> 0.5568 0.0088\n",
      "189 --------> 0.5154 0.00887\n",
      "190 --------> 0.6796 0.00914\n",
      "191 --------> nan nan\n",
      "192 --------> 0.5672 0.00793\n",
      "193 --------> 0.5858 0.00847\n",
      "194 --------> 0.6262 0.01036\n",
      "195 --------> 0.4158 0.00837\n",
      "196 --------> 0.5886 0.00877\n",
      "197 --------> 0.6333 0.01005\n",
      "198 --------> 0.4175 0.0075\n",
      "199 --------> 0.5708 0.00878\n",
      "200 --------> 0.6202 0.00857\n",
      "201 --------> 0.5063 0.00896\n",
      "202 --------> 0.4741 0.00932\n",
      "203 --------> 0.6155 0.00794\n",
      "204 --------> 0.5303 0.0078\n",
      "205 --------> 0.5384 0.0094\n",
      "206 --------> 0.5824 0.00912\n",
      "207 --------> 0.4927 0.00901\n",
      "208 --------> 0.6141 0.00823\n",
      "209 --------> 0.3423 0.00959\n",
      "210 --------> 0.605 0.00979\n",
      "211 --------> 0.6301 0.00865\n",
      "212 --------> 0.6454 0.00809\n",
      "213 --------> 0.525 0.00926\n",
      "214 --------> 0.6315 0.00928\n",
      "215 --------> 0.4181 0.00882\n",
      "216 --------> 0.723 0.00789\n",
      "217 --------> 0.5356 0.00938\n",
      "218 --------> 0.5222 0.0099\n",
      "219 --------> 0.4763 0.00928\n",
      "220 --------> 0.6793 0.00833\n",
      "221 --------> 0.3152 0.00963\n",
      "222 --------> 0.5794 0.0097\n",
      "223 --------> 0.5328 0.00737\n",
      "224 --------> 0.6315 0.00774\n",
      "225 --------> 0.4522 0.00906\n",
      "226 --------> 0.4874 0.00951\n",
      "227 --------> 0.6553 0.00995\n",
      "228 --------> 0.5442 0.00845\n",
      "229 --------> 0.4804 0.0097\n",
      "230 --------> 0.6985 0.00931\n",
      "231 --------> nan nan\n",
      "232 --------> 0.5765 0.00848\n",
      "233 --------> 0.5221 0.00942\n",
      "234 --------> 0.5295 0.01006\n",
      "235 --------> 0.5442 0.00825\n",
      "236 --------> 0.7002 0.00816\n",
      "237 --------> 0.408 0.0095\n",
      "238 --------> 0.5107 0.00929\n",
      "239 --------> 0.5961 0.00881\n",
      "240 --------> nan nan\n",
      "241 --------> 0.631 0.00897\n",
      "242 --------> 0.5142 0.00956\n",
      "243 --------> 0.4771 0.00855\n",
      "244 --------> 0.5795 0.00887\n",
      "245 --------> 0.6531 0.00967\n",
      "246 --------> 0.5001 0.0086\n",
      "247 --------> nan nan\n",
      "248 --------> 0.6255 0.00865\n",
      "249 --------> nan nan\n",
      "250 --------> 0.4993 0.01039\n",
      "251 --------> 0.5649 0.00824\n",
      "252 --------> 0.4281 0.00764\n",
      "253 --------> 0.6392 0.01037\n",
      "254 --------> 0.4434 0.00834\n",
      "255 --------> 0.602 0.00918\n"
     ]
    }
   ],
   "source": [
    "if globals().get('__file__', '') == '':\n",
    "    CL_CD = deformed_foil_data_from_Xfoil(alpha, Re, n_iter, num_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f98cbaf-1d50-4032-a8d5-d94212bb6aa7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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