presentation.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jorisgauliard/windturbine/blob/main/presentation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Adjustable Incidence Vertical Windturbine\n",
        "### Power Calculation and Optimal Incidence Definition\n",
        "\n",
        "I collaborated with a windturbine compagny to compare experimental findings with a theoretical power calculation.\n",
        "\n",
        "The windturbine technology is patented.\n",
        "\n",
        "All the dimensions that are used here are fictional."
      ],
      "metadata": {
        "id": "n0vxaPKRSHL-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "QZ_Utx8bPqF8"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z3zGo7CdMwKj"
      },
      "source": [
        "# Definition of the incidence and power fucntions"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def incidence(Io,threshold,s,type=2):\n",
        "    \n",
        "    # calcul préalables a partir des paramètres d'entrée\n",
        "    \n",
        "    a = np.linspace(0, 2*np.pi,s)        # definition de l'angle\n",
        "    I = np.zeros(s)                      # creation d'une liste vide pour la variable incidence\n",
        "\n",
        "    if threshold >= 1:                   # threshold >= 1 equivalent a allure carrée\n",
        "        type = 1\n",
        "        \n",
        "    # creation de la base incidence a allure carree\n",
        "    \n",
        "    for i in range(s):                   # on parcourt sur s iteration\n",
        "\n",
        "        if np.cos(a[i]) > 0:             # condition sur l'angle\n",
        "            I[i] = Io                    # attribution de la valeure I0 a I si condition validée\n",
        "        elif np.cos(a[i]) == 0:\n",
        "            I[i] = 0                     # attribution de la valeure 0 a I si condition validée\n",
        "        else:\n",
        "            I[i] = -Io                   # attribution de la valeure -I0 a I si condition validée\n",
        "\n",
        "    # retour de I si le choix est type 1 \n",
        "    \n",
        "    if type == 1:                            # condition 1\n",
        "        \n",
        "        return I                             # retour de I et sortie de la fonction incidence\n",
        "    \n",
        "    # calcul de I si choix 2\n",
        "    \n",
        "    elif type == 2:\n",
        "        \n",
        "        A0 = np.arcsin(threshold)                # calcul de l'angle de seuil in\n",
        "        A1 = np.pi / 2 + np.arccos(threshold)    # calcul de l'angle de seuil out\n",
        "        T = 2 * (A1 - A0)                        # periode de la sinusoïde entre in et out\n",
        "        f = 1 / T                                # frequence de la sinusoïde entre in et out\n",
        "        omega = 2 * np.pi * f                    # vitesse angulaire de la sinusoïde entre in et out\n",
        "        phi1 = - omega*A0 + np.pi/2              # dephasage sur la partie haute a cos positif\n",
        "        phi2 = - omega*(A0+np.pi) - np.pi/2      # dephasage sur la partie haute a cos negatif\n",
        "        \n",
        "        for i in range(s):\n",
        "\n",
        "            if np.sin(a[i]) > threshold:\n",
        "                I[i] = Io * np.sin(omega * a[i] + phi1)\n",
        "\n",
        "            elif np.sin(a[i]) < -threshold:\n",
        "                I[i] = Io * np.sin(omega * a[i] + phi2)\n",
        "                \n",
        "        return I\n",
        "                \n",
        "    else:\n",
        "        \n",
        "        print(\"Type must be 1 or 2 and is now set as\",type)"
      ],
      "metadata": {
        "id": "G5jksknmPqXd"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def power(Vr, Rv, R, l, L, I0, threshold, s):\n",
        "    \n",
        "    #Calculated parameters\n",
        "    Vd = Rv * Vr\n",
        "    w = Vd / (2 * np.pi * R) # vitesse de rotation en m/s\n",
        "    # Cd & Cl table import\n",
        "    Coeffs = pd.read_excel(r'coeff.xlsx')\n",
        "    Coeffs.Alpha[1]\n",
        "    \n",
        "    #variables names initialisation\n",
        "    variables_list = ['a', 'Theta', 'Tabsolue', 'vent_apparent','Portance', 'Trainee' , 'Cl', 'Cd', 'Couple_moteur', 'Couple_resistant', 'Couple_resultant', 'Travail_resultant']\n",
        "    variables_dict = {}\n",
        "    for x in variables_list:\n",
        "        variables_dict[x] = np.linspace(0, 2*np.pi,s)\n",
        "    \n",
        "    I = incidence(I0, threshold, s) #application de la focntion incidence\n",
        "    \n",
        "    # calculation of parameters\n",
        "    for i in range(s):\n",
        "        variables_dict['vent_apparent'][i] = np.sqrt(Vr ** 2 + Vd ** 2 - 2 * Vr * Vd * np.cos(np.pi / 2 + variables_dict['a'][i]))\n",
        "        variables_dict['Theta'][i] = np.arcsin(Vr / variables_dict['vent_apparent'][i] * np.sin(variables_dict['a'][i] - np.pi / 2))\n",
        "        variables_dict['Tabsolue'][i] = np.abs(variables_dict['Theta'][i])\n",
        "        variables_dict['Cl'][i] = Coeffs.Cl[(np.abs(Coeffs.Alpha - I[i])).argmin()]\n",
        "        variables_dict['Cd'][i] = Coeffs.Cd[(np.abs(Coeffs.Alpha - I[i])).argmin()]\n",
        "        variables_dict['Portance'][i] = 1 / 2 * 1.225 * variables_dict['vent_apparent'][i] ** 2 * L * l * variables_dict['Cl'][i]\n",
        "        variables_dict['Trainee'][i] = 1 / 2 * 1.225 * variables_dict['vent_apparent'][i] ** 2 * L * l * variables_dict['Cd'][i]\n",
        "        variables_dict['Couple_moteur'][i] = np.abs(variables_dict['Portance'][i] * np.sin(variables_dict['Tabsolue'][i])) * R\n",
        "        variables_dict['Couple_resistant'][i] = variables_dict['Trainee'][i] * np.cos(variables_dict['Tabsolue'][i]) * R\n",
        "        variables_dict['Couple_resultant'][i] = variables_dict['Couple_moteur'][i] - variables_dict['Couple_resistant'][i]\n",
        "        variables_dict['Travail_resultant'][i] = variables_dict['Couple_resultant'][i] * 2 * np.pi / s\n",
        "    \n",
        "    travail_total = sum(variables_dict['Travail_resultant'])\n",
        "    puissance = 4 * travail_total * w\n",
        "    puissance_surfacique = puissance / (2 * L * R)\n",
        "    \n",
        "    return [w,puissance, puissance_surfacique]"
      ],
      "metadata": {
        "id": "eFuzBYnyPqy-"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Power Calculation\n",
        "---\n",
        "## Visualisation de l'incidence\n",
        "\n",
        "La fonction **incidence** permet de définir l'incidence réelle : l'angle formé entre une pale et le vent apparent.\n",
        "4 paramètres en entrée sont nécessaires :\n",
        "- **I0** *l'incidence optimale*, égale à 7° dans cette étude\n",
        "- **threshold** *le seuil ajustable*, valeure subjective entre 0 et 1\n",
        "- **s** *le nombre d'échantillons*, au choix\n",
        "- **type** *l'allure (carrée ou sinusoïdale)*, sinusoïdal par défaut"
      ],
      "metadata": {
        "id": "ty6YHa5rPQEu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "wTVwjSudMwKt"
      },
      "outputs": [],
      "source": [
        "I0 = 10                  # incidence optimale\n",
        "threshold = 0.8          # seuil\n",
        "s = 1000                 # nombre d'échantillons"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NDxAjdaZMwKu"
      },
      "source": [
        "On définit une variable **angle** représentant l'angle au vent réel, sur **s** échantillons"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "l2mqPqwOMwKu"
      },
      "outputs": [],
      "source": [
        "angle = np.linspace(0, 2*np.pi, s)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UmyhfYlrMwKu"
      },
      "source": [
        "Appel de la fonction **incidence**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "N5nfE1XEMwKu"
      },
      "outputs": [],
      "source": [
        "incidence_finale = incidence(I0, threshold, s)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K04c1zCtMwKu"
      },
      "source": [
        "Visualisation de l'incidence"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "scrolled": true,
        "id": "i0IEhhcwMwKv",
        "outputId": "7618cf14-4ffc-4ba6-933c-c080a2d18bf0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure()                                    # création d'une figure\n",
        "plt.title('Incidence réelle')                   # titre du graphique\n",
        "plt.xlabel('Angle au vent réel (°)')            # label de l'axe x\n",
        "plt.plot(180 / np.pi * angle, incidence_finale) # tracé avec angle en degrés\n",
        "plt.show()                                      # affichage"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aDE7rWiWMwKv"
      },
      "source": [
        "Il est intéressant de visualiser l'incidence pour différentes valeures de seuil comme ci-dessous :"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "scrolled": false,
        "id": "rCPW6jYrMwKw",
        "outputId": "5ffd5ba4-7258-46f5-d44d-7de627484af1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x360 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(16,5))\n",
        "\n",
        "plt.subplot(1, 3, 1)\n",
        "plt.title('Seuil = 0')                  \n",
        "plt.xlabel('Angle au vent réel (°)')            \n",
        "plt.plot(180 / np.pi * angle, incidence(I0, 0, s))\n",
        "\n",
        "plt.subplot(1, 3, 2)\n",
        "plt.title('Seuil = 0.6')                  \n",
        "plt.xlabel('Angle au vent réel (°)')            \n",
        "plt.plot(180 / np.pi * angle, incidence(I0, 0.6, s))\n",
        "\n",
        "plt.subplot(1, 3, 3)\n",
        "plt.title('Seuil = 1')                  \n",
        "plt.xlabel('Angle au vent réel (°)')            \n",
        "plt.plot(180 / np.pi * angle, incidence(I0, 1, s))\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RgIyN_HmMwKw"
      },
      "source": [
        "De manière idéale on cherche a tendre vers un seuil de 1 mais cela est, de manière pratique, impossible a mettre en oeuvre pour des raisons mécaniques. En effet cela entrainerait un changement de bord brutal.\n",
        "On utilise donc un seuil choisit arbitrairement à 0.6 pour la suite de l'étude."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zi3EGWa2MwKw"
      },
      "source": [
        "## Calcul de la puissance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nT0SYoTgMwKx"
      },
      "outputs": [],
      "source": [
        "from function_wind import power"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C-IaM45_MwKx"
      },
      "source": [
        "On définit la vitesse du vent réel ainsi que le ratio entre vent réel et vent due au mouvement de rotation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "l30Nw1sZMwKx"
      },
      "outputs": [],
      "source": [
        "Vr = 6.0 #real wind in m/s\n",
        "Rv = 2.0 # linear/real wind ratio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OtlUJTp5MwKy"
      },
      "source": [
        "Puis on définit les parametres dimentionelles de l'eolienne"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "_ua2dW64MwKy"
      },
      "outputs": [],
      "source": [
        "R = 1.15  #radius\n",
        "l = 0.25  #chord\n",
        "L = 2     #longueur pale"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ZFD7zLGMwKy"
      },
      "source": [
        "Pour ensuite call la fonction power"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "7h1QIVRzMwKy",
        "outputId": "4648afca-9443-4c04-92c8-31bd2e6bb1bb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "La vitesse de rotation est de  99 tr/min\n",
            "La puissance est de  657 W\n",
            "La puissance surfacique est de  143 W/m2\n"
          ]
        }
      ],
      "source": [
        "output = power(Vr, Rv, R, l, L, I0, threshold, s)\n",
        "print('La vitesse de rotation est de ', int(output[0] * 60), 'tr/min')\n",
        "print('La puissance est de ', int(output[1]), 'W')\n",
        "print('La puissance surfacique est de ', int(output[2]), 'W/m2')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T9cRdlCxMwKy"
      },
      "source": [
        "## Visualisation\n",
        "On peut étudier la puissance en fonction de différents paramètres"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "9nHLH6irMwKy",
        "outputId": "6ac9c0b0-e96a-4a21-9392-47935d9c7987",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "Rvi = 0\n",
        "Rvf = 30\n",
        "s2 = 15\n",
        "Rv = np.linspace(Rvi,Rvf,s2)\n",
        "Power_variable = np.linspace(Rvi,Rvf,s2)\n",
        "\n",
        "for i in range(s2):\n",
        "    Power_variable[i] = power(Vr, Rv[i], R, l, L, I0, threshold, s)[1]\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(np.linspace(Rvi,Rvf,s2),Power_variable)\n",
        "ax.grid()"
      ]
    }
  ],
  "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": "3.7.6"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}