5.Scipy_optimization_Lift_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",
    "    return -CL\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(8,0.00)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "794f5a4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n"
     ]
    }
   ],
   "source": [
    "from shapely.geometry import Polygon\n",
    "constraints = [{'type': 'eq', '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 26ms/step\n",
      "Optimal Design Parameters: [[ 0.0023782   0.01199999 -0.0028495   0.01197218 -0.01175872  0.01198441\n",
      "   0.012       0.012     ]]\n",
      "Optimal Lift Coefficient: 0.7484439\n",
      "Optimal Drag Coefficient: 0.008479285\n",
      "Optimal Aerodynamic Efficiency: 88.267334\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([[ 0.0023782 ,  0.01199999, -0.0028495 ,  0.01197218, -0.01175872,\n",
       "         0.01198441,  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_2.dat\"\n",
    "df_opt.to_csv(path, index=False, sep=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a25d4e09-abdb-4db2-a80e-618d95f9204c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7546 0.00906\n"
     ]
    }
   ],
   "source": [
    "airfoil_name = 'Optimized_airfoil_2'\n",
    "\n",
    "CL, CD = Xfoil_runner(alpha, Re, n_iter, airfoil_name)\n",
    "\n",
    "print(CL, CD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "98daad60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Surrogate model optimization:\n",
      "Optimal Design Parameters: [[ 0.0023782   0.01199999 -0.0028495   0.01197218 -0.01175872  0.01198441\n",
      "   0.012       0.012     ]]\n",
      "Optimal Lift Coefficient(Surrogate model prediction): 0.7484439\n",
      "Optimal Drag Coefficient(Surrogate model prediction): 0.008479285\n",
      "Optimal Aerodynamic Efficiency: 88.267334\n",
      "Lift Coefficient for Optimal Design Parameter from XFOIL: 0.7546\n",
      "Drag Coefficient for Optimal Design Parameter from XFOIL: 0.00906\n",
      "Optimal Aerodynamic Efficiency from XFOIL: 83.28918322295806\n"
     ]
    }
   ],
   "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": 19,
   "id": "8013af06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error%\n",
      "Lift Coefficient Error: 0.8158094965744992\n",
      "Drag Coefficient Error: 6.409657241624452\n",
      "Optimal Aerodynamic Efficiency Error: 5.97694750840677\n"
     ]
    }
   ],
   "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": 20,
   "id": "9c9c9d4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Improvement in Efiiciency\n",
      "Lift and Drag Coefficient of Original Airfoil: 0.5572 0.00848\n",
      "Efiiciency of Original Airfoil: 65.70754716981132\n",
      "Lift and Drag Coefficient of Optimized Airfoil: 0.7546 0.00906\n",
      "Efiiciency of Optimized Airfoil: 83.28918322295806\n",
      "Improvement in Efiiciency: 26.757407345779672 %\n"
     ]
    }
   ],
   "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
}