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  "name": "",
  "signature": "sha256:18938a9221b742b2f7b4267fff81d2771384abc6c99fba2e7385d073c911e5b7"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# WIND"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import gpkit\n",
      "import gpkit.interactive\n",
      "gpkit.interactive.init_printing()\n",
      "\n",
      "%matplotlib inline\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Maximizing Differential Power"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "var = gpkit.Variable\n",
      "vec = gpkit.VectorVariable\n",
      "\n",
      "dCpdy = var(\"\\\\frac{dC_P}{dy}\")\n",
      "a = var(\"a\")\n",
      "b = var(\"b\")\n",
      "lam = var(\"\\\\lambda\")\n",
      "s = var(\"s\")\n",
      "xi = var(\"\\\\xi\")\n",
      "F = var(\"F\")\n",
      "y = var(\"y\")\n",
      "eps = var(\"\\\\epsilon\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gp = gpkit.GP(1/dCpdy,\n",
      "              [1 >= a + b,\n",
      "               2*a*b >= s*lam*y + s*xi,\n",
      "               xi**2 >= (lam*y)**2 + 4*a*b,\n",
      "               s*b >= dCpdy/(8*lam*y**2*F) + eps*a*b],\n",
      "              {F: 1, y: 0.75, eps: 1.0/30.0})\n",
      "gp"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\begin{array}[ll]\n",
        "\\text{}\n",
        "\\text{minimize}\n",
        "    & \\frac{1}{\\frac{dC_P}{dy}} \\\\\n",
        "\\text{subject to}\n",
        "    & 1 \\geq a + b \\\\\n",
        "    & 2a b \\geq \\lambda s y + s \\xi \\\\\n",
        "    & \\xi^{2} \\geq 4a b + \\lambda^{2} y^{2} \\\\\n",
        "    & b s \\geq 0.125\\frac{\\frac{dC_P}{dy}}{\\lambda F y^{2}} + a b \\epsilon \\\\\n",
        "\\text{substituting}\n",
        "    & F = 1 \\\\\n",
        "    & \\epsilon = 0.03333 \\\\\n",
        "    & y = 0.75 \\\\\n",
        "\\end{array}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "                               gpkit.GP( # minimize                           \n",
        "                                    \\frac{dC_P}{dy}**-1,                      \n",
        "                                      [   # subject to                        \n",
        "                                          1 >= a + b,                         \n",
        "                                 2*a*b >= \\lambda*s*y + s*\\xi,                \n",
        "                               \\xi**2 >= 4*a*b + \\lambda**2*y**2,             \n",
        "              b*s >= 0.12*\\frac{dC_P}{dy}*\\lambda**-1*F**-1*y**-2 + a*b*\\epsil\n",
        "                                             ],                               \n",
        "              substitutions={ y: 0.75, F: 1, \\epsilon: 0.03333333333333333 }, \n",
        "                                      solver=\"cvxopt\")                        \n",
        "\n",
        "   \n",
        "   \n",
        "   \n",
        "   \n",
        "   \n",
        "   \n",
        "on,\n",
        "   \n",
        "   \n",
        "   "
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print gp.solve().table()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using solver 'cvxopt'\n",
        "Solving took 0.0341 seconds\n",
        "    1.3203 : Cost\n",
        "           | Free variables\n",
        "\\frac{dC_P}{dy} : 0.757    [-] \n",
        "   \\lambda : 2.57     [-] \n",
        "       \\xi : 2.14     [-] \n",
        "         a : 0.315    [-] \n",
        "         b : 0.685    [-] \n",
        "         s : 0.106    [-] \n",
        "           |\n",
        "           | Constants\n",
        "         F : 1        [-] \n",
        "  \\epsilon : 0.0333   [-] \n",
        "         y : 0.75     [-] \n",
        "           |\n",
        "           | Constant sensitivities\n",
        "         F : -1       [-] \n",
        "  \\epsilon : 0.11     [-] \n",
        "         y : -1       [-] \n",
        "           |\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Maximizing Total Power"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "B = 3\n",
      "N = 10\n",
      "\n",
      "f = var(\"f\")\n",
      "pi = var(\"\\\\pi\", np.pi)\n",
      "DCp = var(\"(\\Delta C_P)\")\n",
      "xi = vec(N, \"\\\\xi\")\n",
      "zeta = vec(N, \"\\\\zeta\")\n",
      "y = vec(N, \"y\")\n",
      "dy = vec(N, \"(\\Delta y)\")\n",
      "lam = var(\"\\\\lambda\", ('sweep', np.linspace(0.01, 8, 20)))\n",
      "\n",
      "constraints = [1 >= a + b,\n",
      "               2*a*b >= s*lam*y + s*xi,\n",
      "               xi**2 >= (lam*y)**2 + 4*a*b,\n",
      "               s*b >= DCp/(8*lam*dy**2*F) + eps*a*b]\n",
      "\n",
      "constraints += [ y[-1] + dy[-1]/2 <= 1,\n",
      "                [y[i] + dy[i]/2 + dy[i+1]/2 <= y[i+1] for i in range(N-1)],\n",
      "                 #B*zeta[0]/(2*pi) + dy[0]/2 <= y[0],#switch0\n",
      "                 dy[0]/2 <= y[0],#switch0\n",
      "                 1/y >= 1 + 2*f*s/(B*a),\n",
      "                 1 >= F**1.56 + 1.655*F**5.51*f**-2.76   ]\n",
      "\n",
      "sol = gpkit.GP(1/(N*DCp), constraints, {eps: ('sweep', [0.2, 0.1, 0.05, 0.033])}).solve('mosek')\n",
      "print sol.table()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using solver 'mosek'\n",
        "Sweeping 2 variables over 80 passes\n",
        "Solving took 3.87 seconds"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "    92.607 : Cost (average of 80 values)\n",
        "           | Free variables (average)\n",
        "(\\Delta C_P) : 0.0047   [-] \n",
        "(\\Delta y) : 0.0726   [-] \n",
        "         F : 0.859    [-] \n",
        "       \\xi : 2.4      [-] \n",
        "         a : 0.298    [-] \n",
        "         b : 0.702    [-] \n",
        "         f : 2.95     [-] \n",
        "         s : 0.123    [-] \n",
        "         y : 0.363    [-] \n",
        "           |\n",
        "           | Constants (average)\n",
        "  \\epsilon : 0.0958   [-] \n",
        "   \\lambda : 4        [-] \n",
        "           |\n",
        "           | Constant sensitivities (average)\n",
        "  \\epsilon : 0.526    [-] \n",
        "   \\lambda : 0.179    [-] \n",
        "           |\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Plotting $C_P$ vs $\\Lambda$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "B = 3\n",
      "N = 20\n",
      "Nlams = 20\n",
      "\n",
      "f = var(\"f\")\n",
      "pi = var(\"\\\\pi\", np.pi)\n",
      "DCp = var(\"(\\Delta C_P)\")\n",
      "xi = vec(N, \"\\\\xi\")\n",
      "zeta = vec(N, \"\\\\zeta\")\n",
      "y = vec(N, \"y\")\n",
      "dy = vec(N, \"(\\Delta y)\")\n",
      "lam = var(\"\\\\lambda\", ('sweep', np.linspace(0.01, 8, Nlams)))\n",
      "\n",
      "constraints = [1 >= a + b,\n",
      "               2*a*b >= s*lam*y + s*xi,\n",
      "               xi**2 >= (lam*y)**2 + 4*a*b,\n",
      "               s*b >= DCp/(8*lam*dy**2*F*N/1.7) + eps*a*b]\n",
      "\n",
      "constraints += [ y[-1] + dy[-1]/2 <= 1,\n",
      "                [y[i] + dy[i]/2 + dy[i+1]/2 <= y[i+1] for i in range(N-1)],\n",
      "                 #B*zeta[0]/(2*pi) + dy[0]/2 <= y[0],#switch0\n",
      "                 dy[0]/2 <= y[0],#switch0\n",
      "                 1/y >= 1 + 2*f*s/(B*a),\n",
      "                 1 >= F**1.56 + 1.655*F**5.51*f**-2.76   ]\n",
      "\n",
      "sol = gpkit.GP(1/(N*DCp), constraints, {eps: ('sweep', [0.2, 0.1, 0.05, 0.033])}).solve('mosek')\n",
      "\n",
      "plt.plot(sol(lam).reshape(4, Nlams).T, sol(N*DCp).reshape(4, Nlams).T, '--', linewidth=1.5)\n",
      "plt.gca().set_color_cycle(None)\n",
      "Fsub = sol(F)\n",
      "lamsub = sol(lam)\n",
      "epssub = sol(eps)\n",
      "\n",
      "##\n",
      "\n",
      "lams = []\n",
      "NDCps = []\n",
      "for j in range(Fsub.size):\n",
      "    lam = var(\"\\\\lambda\", lamsub[j])\n",
      "\n",
      "    constraints = [1 >= a + b,\n",
      "                   2*a*b >= s*lam*y + s*xi,\n",
      "                   xi**2 >= (lam*y)**2 + 4*a*b,\n",
      "                   s*b >= DCp/(8*lam*dy**2*F*N/1.7) + eps*a*b]\n",
      "\n",
      "    constraints += [ y[-1] + dy[-1]/2 <= 1,\n",
      "                    [y[i] + dy[i]/2 + dy[i+1]/2 <= y[i+1] for i in range(N-1)],\n",
      "                     #B*zeta[0]/(2*pi) + dy[0]/2 <= y[0],#switch0\n",
      "                     dy[0]/2 <= y[0],#switch0\n",
      "                    ]\n",
      "\n",
      "    sol = gpkit.GP(1/(N*DCp), constraints, {F: Fsub[j], eps: epssub[j]}).solve('mosek', printing=False)\n",
      "    \n",
      "    lams.append(sol(lam))\n",
      "    NDCps.append(sol(N*DCp))\n",
      "    \n",
      "lams = np.array(lams)\n",
      "NDCps = np.array(NDCps)\n",
      "plt.plot(lams.reshape(4, Nlams).T, NDCps.reshape(4, Nlams).T, linewidth=1.5)\n",
      "plt.plot([0, 8], [16.0/27.0, 16.0/27.0], 'k')\n",
      "plt.ylim([0, 0.7])\n",
      "plt.xlabel('Tip Speed Ratio (lambda)')\n",
      "plt.ylabel('C_P')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using solver 'mosek'\n",
        "Sweeping 2 variables over 80 passes\n",
        "Solving took 4.39 seconds"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "<matplotlib.text.Text at 0x7f0be748df10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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e/bLY7Db2n9lPzIkYdqbtJOZEDDFpMWQWZgLgarjSvVl3+gX0o3+r/vT7Yz8D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\nwDAMI8r5f6PA5bMYOwkuAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0be75abe50>"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Import CSS for nbviewer\n",
      "\n",
      "If you have a local iPython stylesheet installed, this will add it to the iPython Notebook:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython import utils\n",
      "from IPython.core.display import HTML\n",
      "import os\n",
      "def css_styling():\n",
      "    \"\"\"Load default custom.css file from ipython profile\"\"\"\n",
      "    base = utils.path.get_ipython_dir()\n",
      "    styles = \"<style>\\n%s\\n</style>\" % (open(os.path.join(base,'profile_default/static/custom/custom.css'),'r').read())\n",
      "    return HTML(styles)\n",
      "css_styling()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<style>\n",
        "@import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
        "@import url('http://fonts.googleapis.com/css?family=Roboto');\n",
        "@import url('http://fonts.googleapis.com/css?family=Kameron');\n",
        "@import url('http://fonts.googleapis.com/css?family=Lato:200');\n",
        "@import url('http://fonts.googleapis.com/css?family=Lato:300');\n",
        "@import url('http://fonts.googleapis.com/css?family=Lato:400');\n",
        "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
        "\n",
        "/* Change code font */\n",
        "pre {\n",
        "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
        "}\n",
        "\n",
        "div.input_area {\n",
        "    border-width: 0 0 0 1px;\n",
        "    border-color: rgba(0,0,0,0.10);\n",
        "    background: white;\n",
        "    border-radius: 0;\n",
        "}\n",
        "\n",
        "div.text_cell {\n",
        "    max-width: 105ex; /* instead of 100%, */\n",
        "}\n",
        "\n",
        "div.text_cell_render {\n",
        "    font-family: Roboto;\n",
        "    font-size: 12pt;\n",
        "    line-height: 145%; /* added for some line spacing of text. */\n",
        "}\n",
        "\n",
        "div.text_cell_render h1,\n",
        "div.text_cell_render h2,\n",
        "div.text_cell_render h3,\n",
        "div.text_cell_render h4,\n",
        "div.text_cell_render h5,\n",
        "div.text_cell_render h6 {\n",
        "    font-family: 'Roboto';\n",
        "}\n",
        "\n",
        "div.text_cell_render h1 {\n",
        "    font-size: 24pt;\n",
        "}\n",
        "\n",
        "div.text_cell_render h2 {\n",
        "    font-size: 18pt;\n",
        "}\n",
        "\n",
        "div.text_cell_render h3 {\n",
        "    font-size: 14pt;\n",
        "}\n",
        "\n",
        ".rendered_html pre,\n",
        ".rendered_html code {\n",
        "    font-size: medium;\n",
        "}\n",
        "\n",
        ".rendered_html ol {\n",
        "    list-style:decimal;\n",
        "    margin: 1em 2em;\n",
        "}\n",
        "\n",
        ".prompt {\n",
        "    opacity: 0.6;\n",
        "}\n",
        "\n",
        ".prompt.input_prompt {\n",
        "    color: #668;\n",
        "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
        "}\n",
        "\n",
        ".prompt.out_prompt_overlay {\n",
        "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
        "}\n",
        "\n",
        ".cell.command_mode.selected {\n",
        "    border-color: rgba(0,0,0,0.1);\n",
        "}\n",
        "\n",
        "div.cell.selected {\n",
        "    border-width: 0 0 0 1px;\n",
        "    border-color: rgba(0,0,0,0.1);\n",
        "    border-radius: 0;\n",
        "}\n",
        "\n",
        "div.output_scroll {\n",
        "    -webkit-box-shadow: inset 0 2px 8px rgba(0,0,0,0.1);\n",
        "    box-shadow: inset 0 2px 8px rgba(0,0,0,0.1);\n",
        "    border-radious: 2px;\n",
        "}\n",
        "\n",
        "#menubar .navbar-inner {\n",
        "    background: #fff;\n",
        "    -webkit-box-shadow: none;\n",
        "    box-shadow: none;\n",
        "    border-radius: 0;\n",
        "    border: none;\n",
        "    font-family: Roboto;\n",
        "    font-weight: 400;\n",
        "}\n",
        "\n",
        ".navbar-fixed-top .navbar-inner,\n",
        ".navbar-static-top .navbar-inner {\n",
        "    box-shadow: none;\n",
        "    -webkit-box-shadow: none;\n",
        "    border: none;\n",
        "}\n",
        "\n",
        "div#notebook_panel {\n",
        "    box-shadow: none;\n",
        "    -webkit-box-shadow: none;\n",
        "    border-top: none;\n",
        "}\n",
        "\n",
        "div#notebook {\n",
        "    border-top: 1px solid rgba(0,0,0,0.15);\n",
        "}\n",
        "\n",
        "#menubar .navbar .navbar-inner,\n",
        ".toolbar-inner {\n",
        "    padding-left: 0;\n",
        "    padding-right: 0;\n",
        "}\n",
        "\n",
        "#checkpoint_status,\n",
        "#autosave_status {\n",
        "    color: rgba(0,0,0,0.5);\n",
        "}\n",
        "\n",
        "#header {\n",
        "    font-family: Roboto;\n",
        "}\n",
        "\n",
        "#notebook_name {\n",
        "    font-weight: 200;\n",
        "}\n",
        "\n",
        "/*\n",
        "    This is a lazy fix, we *should* fix the\n",
        "    background for each Bootstrap button type\n",
        "*/\n",
        "#site * .btn {\n",
        "    background: #fafafa;\n",
        "    -webkit-box-shadow: none;\n",
        "    box-shadow: none;\n",
        "}\n",
        "\n",
        "</style>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "<IPython.core.display.HTML at 0x7f0bebafbe90>"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}