5.Scipy_optimization_Aero_eff_max.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "77bdf148",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.optimize import minimize\n",
    "import joblib\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "%run 2.Hicks_Henne_NEW_Final.ipynb\n",
    "%run 3.Xfoil_runner_extract_value.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8bede254",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\backend.py:1398: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = joblib.load('model__24.joblib')\n",
    "scaler = joblib.load('scaler_file__24.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b55e805b-f7ff-4229-b774-5d8218ae06ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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": "f4275fc9-4a66-486e-b783-e340c4355ce1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5572 0.00848\n"
     ]
    }
   ],
   "source": [
    "airfoil_name = '0rig_NACA0012'\n",
    "\n",
    "CL_org, CD_org = Xfoil_runner(alpha, Re, n_iter, airfoil_name)\n",
    "\n",
    "print(CL_org, CD_org)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a95e83ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = [\"x1\", \"x2\", \"x3\", \"x4\", \"x5\", \"x6\", \"x7\", \"x8\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1eaea160",
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective_function(design_parameters):\n",
    "    design_parameters = design_parameters.reshape(1,8)\n",
    "    design_parameters = pd.DataFrame(design_parameters, columns=feature_names)\n",
    "    design_parameters = scaler.transform(design_parameters)\n",
    "    \n",
    "    predictions = model.predict(design_parameters)\n",
    "    CL = predictions[0, 0]\n",
    "    CD = predictions[0, 1]\n",
    "    \n",
    "    efficiency = CL / CD\n",
    "    return -efficiency\n",
    "\n",
    "def CD_constraint(design_parameters):\n",
    "    design_parameters = design_parameters.reshape(1,8)\n",
    "    design_parameters = pd.DataFrame(design_parameters, columns=feature_names)\n",
    "    design_parameters = scaler.transform(design_parameters)\n",
    "    \n",
    "    predictions = model.predict(design_parameters)\n",
    "    CL = predictions[0, 0]\n",
    "    CD = predictions[0, 1]  \n",
    "    \n",
    "    return CD_org - CD\n",
    "    \n",
    "def area_constraint(design_parameters):\n",
    "    design_parameters = np.array(design_parameters).reshape((1, 8))\n",
    "    i = 0\n",
    "    y_mod = hicks_henne_overall(x, y , n, design_parameters, i, w, xM)\n",
    "    polygon1 = Polygon(zip(x, y))\n",
    "    area_orig = polygon1.area\n",
    "    polygon2 = Polygon(zip(x, y_mod))\n",
    "    area_mod = polygon2.area\n",
    "    return area_orig - area_mod\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "968bd949",
   "metadata": {},
   "outputs": [],
   "source": [
    "bounds = [(-limit, limit), (-limit, limit), (-limit, limit), (-limit, limit), (-limit, limit), (-limit, limit), (-limit, limit), (-limit, limit) ]\n",
    "#scale\n",
    "initial_guess = np.full(8,0.008)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "794f5a4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 371ms/step\n",
      "1/1 [==============================] - 0s 42ms/step\n",
      "1/1 [==============================] - 0s 43ms/step\n",
      "1/1 [==============================] - 0s 38ms/step\n",
      "1/1 [==============================] - 0s 37ms/step\n",
      "1/1 [==============================] - 0s 47ms/step\n",
      "1/1 [==============================] - 0s 35ms/step\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "1/1 [==============================] - 0s 84ms/step\n",
      "1/1 [==============================] - 0s 58ms/step\n",
      "1/1 [==============================] - 0s 67ms/step\n",
      "1/1 [==============================] - 0s 54ms/step\n",
      "1/1 [==============================] - 0s 93ms/step\n",
      "1/1 [==============================] - 0s 44ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 108ms/step\n",
      "1/1 [==============================] - 0s 103ms/step\n",
      "1/1 [==============================] - 0s 90ms/step\n",
      "1/1 [==============================] - 0s 43ms/step\n",
      "1/1 [==============================] - 0s 58ms/step\n",
      "1/1 [==============================] - 0s 78ms/step\n",
      "1/1 [==============================] - 0s 86ms/step\n",
      "1/1 [==============================] - 0s 80ms/step\n",
      "1/1 [==============================] - 0s 61ms/step\n",
      "1/1 [==============================] - 0s 44ms/step\n",
      "1/1 [==============================] - 0s 89ms/step\n",
      "1/1 [==============================] - 0s 96ms/step\n",
      "1/1 [==============================] - 0s 54ms/step\n",
      "1/1 [==============================] - 0s 76ms/step\n",
      "1/1 [==============================] - 0s 77ms/step\n",
      "1/1 [==============================] - 0s 63ms/step\n",
      "1/1 [==============================] - 0s 41ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 45ms/step\n",
      "1/1 [==============================] - 0s 42ms/step\n",
      "1/1 [==============================] - 0s 38ms/step\n",
      "1/1 [==============================] - 0s 34ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 36ms/step\n",
      "1/1 [==============================] - 0s 116ms/step\n",
      "1/1 [==============================] - 0s 57ms/step\n",
      "1/1 [==============================] - 0s 76ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 59ms/step\n",
      "1/1 [==============================] - 0s 78ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 95ms/step\n",
      "1/1 [==============================] - 0s 97ms/step\n",
      "1/1 [==============================] - 0s 102ms/step\n",
      "1/1 [==============================] - 0s 63ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 51ms/step\n",
      "1/1 [==============================] - 0s 78ms/step\n",
      "1/1 [==============================] - 0s 60ms/step\n",
      "1/1 [==============================] - 0s 67ms/step\n",
      "1/1 [==============================] - 0s 98ms/step\n",
      "1/1 [==============================] - 0s 62ms/step\n",
      "1/1 [==============================] - 0s 84ms/step\n"
     ]
    }
   ],
   "source": [
    "from shapely.geometry import Polygon\n",
    "constraints = [{'type': 'ineq', 'fun': CD_constraint},{'type': 'eq', 'fun': area_constraint}]\n",
    "# constraints = [{'type': 'ineq', 'fun': CD_constraint}]\n",
    "result = minimize(objective_function, initial_guess, bounds=bounds, constraints=constraints, method='SLSQP')\n",
    "\n",
    "optimal_design_parameters = result.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "067cecd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization successful!\n"
     ]
    }
   ],
   "source": [
    "if result.success:\n",
    "    print(\"Optimization successful!\")\n",
    "else:\n",
    "    print(\"Optimization failed. Message:\", result.message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7358f734",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 56ms/step\n",
      "Optimal Design Parameters: [[0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012]]\n",
      "Optimal Lift Coefficient: 0.8033277\n",
      "Optimal Drag Coefficient: 0.0073773097\n",
      "Optimal Aerodynamic Efficiency: 108.891685\n"
     ]
    }
   ],
   "source": [
    "optimal_design_parameters = np.array(optimal_design_parameters).reshape((1, 8))\n",
    "optimal_design_parameters_df = pd.DataFrame(optimal_design_parameters, columns=feature_names)\n",
    "optimal_design_parameters_scaled = scaler.transform(optimal_design_parameters_df)\n",
    "Optimized = model.predict(optimal_design_parameters_scaled)\n",
    "CL_opt = Optimized[0, 0]\n",
    "CD_opt = Optimized[0, 1]\n",
    "optimal_efficiency = CL_opt/CD_opt\n",
    "print(\"Optimal Design Parameters:\", optimal_design_parameters)\n",
    "print(\"Optimal Lift Coefficient:\", CL_opt )\n",
    "print(\"Optimal Drag Coefficient:\", CD_opt)\n",
    "print(\"Optimal Aerodynamic Efficiency:\", optimal_efficiency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9a1ca168",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.012, 0.012, 0.012, 0.012, 0.012, 0.012, 0.012, 0.012]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimal_design_parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "186b9979",
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "67552762",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_opt = hicks_henne_overall(x, y , n, optimal_design_parameters, i, w, xM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9d088165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, label = \"Original Airfoil\")\n",
    "plt.plot(x, y_opt, label = \"Optimized Airfoil\")\n",
    "plt.gca().set_aspect('equal', adjustable='box')\n",
    "plt.title('Original Airfoil vs Optimized Airfoil')\n",
    "plt.xlabel('X-coordinate')\n",
    "plt.ylabel('Y-coordinate')\n",
    "plt.legend(loc=(1,1))\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "201d2c4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Y-coordinate')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y_opt)\n",
    "plt.gca().set_aspect('equal', adjustable='box')\n",
    "plt.title('Optimized Airfoil')\n",
    "plt.xlabel('X-coordinate')\n",
    "plt.ylabel('Y-coordinate')\n",
    "# plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0bbdf6bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_opt = pd.DataFrame({'x': x, 'y_modified': y_opt})\n",
    "path = \"Optimized_airfoil_3.dat\"\n",
    "df_opt.to_csv(path, index=False, sep=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a25d4e09-abdb-4db2-a80e-618d95f9204c",
   "metadata": {},
   "outputs": [],
   "source": [
    "airfoil_name = 'Optimized_airfoil_3'\n",
    "\n",
    "CL, CD = Xfoil_runner(alpha, Re, n_iter, airfoil_name)\n",
    "\n",
    "print(CL, CD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98daad60",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Surrogate model optimization:')\n",
    "print(\"Optimal Design Parameters:\", optimal_design_parameters)\n",
    "print(\"Optimal Lift Coefficient(Surrogate model prediction):\", CL_opt )\n",
    "print(\"Optimal Drag Coefficient(Surrogate model prediction):\", CD_opt)\n",
    "print(\"Optimal Aerodynamic Efficiency:\", optimal_efficiency)\n",
    "print(\"Lift Coefficient for Optimal Design Parameter from XFOIL:\", CL )\n",
    "print(\"Drag Coefficient for Optimal Design Parameter from XFOIL:\", CD)\n",
    "Opt_eff = CL/CD\n",
    "print(\"Optimal Aerodynamic Efficiency from XFOIL:\", Opt_eff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8013af06",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Error%\")\n",
    "print(\"Lift Coefficient Error:\", abs((CL_opt-CL)*100/CL))\n",
    "print(\"Drag Coefficient Error:\", abs((CD_opt-CD)*100/CD))\n",
    "print(\"Optimal Aerodynamic Efficiency Error:\", abs((Opt_eff-optimal_efficiency)*100/Opt_eff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c9c9d4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "Org_Eff = CL_org/CD_org\n",
    "print(\"Improvement in Efiiciency\")\n",
    "print(\"Lift and Drag Coefficient of Original Airfoil:\", CL_org , CD_org)\n",
    "print(\"Efiiciency of Original Airfoil:\", CL_org/CD_org)\n",
    "print(\"Lift and Drag Coefficient of Optimized Airfoil:\", CL ,CD)\n",
    "print(\"Efiiciency of Optimized Airfoil:\", Opt_eff)\n",
    "print(\"Improvement in Efiiciency:\", abs((Opt_eff-Org_Eff)*100/Org_Eff),\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f1830e7-649d-44c7-8247-3e6d604b75f6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac392efe-95d2-47e4-a919-4a9cf8c38702",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}