diff --git a/examples/02_Analysis_Example.ipynb b/examples/02_Analysis_Example.ipynb new file mode 100644 index 0000000..7a9ca65 --- /dev/null +++ b/examples/02_Analysis_Example.ipynb @@ -0,0 +1,194 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "from wingstructure import data, aero\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# create wing object\n", + "wing = data.Wing()\n", + "\n", + "# add sections to wing\n", + "wing.add_section(data.Point(0.0, 0.0, 0.0), 1.0, 0.0)\n", + "wing.add_section(data.Point(0.05, 4.25, 0.0), 0.7, 0.0)\n", + "wing.add_section(data.Point(0.1, 7.75, 0.0), 0.35, 0.0)\n", + "\n", + "# set fuselage with (=root of wing) to zero\n", + "wing.set_root_pos(0.0)\n", + "\n", + "# define spoiler position\n", + "wing.set_spoiler(1.5, 2.9)\n", + "\n", + "# define control-surfaces\n", + "wing.set_flap('flap', 1, 2.8,[0.7,0.7])\n", + "wing.set_flap('flap2', 4.25, 7, [0.7,0.8])\n", + "\n", + "# display simple wing\n", + "plt.figure(figsize=(8,5))\n", + "wing.plot()\n", + "plt.savefig('wing.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.01871572770370315\n", + "0.01871572770370315\n", + "dict_items([])\n", + "dict_items([('flap2', [5, -5])])\n", + "dict_items([])\n" + ] + }, + { + "data": { + "image/png": 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PKncd1ZCzHgoyoXAX9D0eBp2mznUvjGu+rDBAZKpK8o4aIssywWlzF3DchZx+J6gkn70W3j1PrRcaBb3GQep4GH+VSvwdkBSWxOGqwx4+YP/SSd7fpITcTeofOTwO9q+EBX+GiF6QOg7GXKYSsjlMLX/83WpqafFi9XrGE3D642AvgzL31XBNaeNVbdFu2PofqC5qXDdtElz/X/Vz4S6ISgdTz+ntStN6st0lu7E77QyPG+75jUsJtZUQYoM6O7x5BuRuBFedmm+0QEikSvKRqTDjqcbax4heqmBjMKplE4fwy5RXmTZtWuv7ShkDl32kaj7zNqukv/Q5GO5O/NlroeSAqhkwh7a6icSwRHIrcz37O/AzneT9xeWCLZ/Cz8/C4S0qMR9zKww5C+6adnSlayHUlWxoFCQOaT7vrGfVVF0MBVnqH7+pt2epC4DeU9QVcr8T1Jen/oumaVpQyavKI8IcwYj4EZ7ZYGk27P0Z9ixRU/JIuPQDMIVA/EDImAbpkyBxGESlNZ5bzKEw+aau7zcsFgad3vyz2iq1X4B178DauRASBeOuhKl3qYJVE0lhSWzI39D1GAKQTvK+JiVkfguLHlNXmwlDYeYzMKy+milSTd5mjVFftPQmgzNICWc8DnuXqS/nDw+pzyfdCGc+rebX1YDZ6v34NE3ziRPSTmDppUsReKDNzke/VzWFoM4xfY+HwWc2zj9/ztHvozMsYY0/z3wWhp0Lv/4frHxZJfwT74Pj7mhYJDEskRJ7CXannRBjiG9j9RKd5H1NCFg1BxxVcP7rMOL8wCklCwHDzlETQMVhdUUe01e9z90Ib86AoWfBiAuh/0nuNgGapnVnXboXX5AFm+ZD1ndwzbfq4n/QDEifrJJ70ggwBNADXAajOmf1PwlOvBcWPQ6u5v3zp0Wk0TuiN6X2UhLDEv0UqGfpJO8r+5ZDbH+ISFJXs9aYwE+QtkQYcUHje3M4jLxAXalv/BCssTDqEjjhf35T7aVpWuCzO+1c+c2V3DDqBk7tc2oHViiHjR+pqu9D6wEBfaeqAkFMHxhzqddj9oiEwXDx26p2EtRTSTnrmHnS35iZMdO/sXlYAF1mBSkpYdlsmHsW/Piw+syWGPgJvjXxA2DWC6ql/+8+UPfrN33UeM+rYCc4avwbo6ZpHVZqLyXWGkuosfWGaA3q7Oo1fwd8fZdqOHf643DXVrj6K5Xgu6P6BskHflGN9N49Fyry/RuTh+mSvLf99BQsfhyGzlIt34OBKQSGnKkmR7WqppMSPrwCKvLUEwHH3g4Ryf6OVNO0I0gMS+Tf0//d+kyXU5XaV72qGs/NekE98XPTEkgeFVz9bpz+GCSNwPHVndzy0amcOfluzht+pb+j8ghdkvemlf9WCX7M5XDR275pUOdrTRvhzXhSle5/+TfMHqvuednL/RebpmlH5HC1Mma8lLDje/j3VPj8ZlU7lzpBzRMCUkYHV4KvN+ZSzJd9iMtRBStfUQWYIOCzJC+EeFMIcVgIsbmN+UIIMVsIsVMIsVEIMa615boNRw2sfk09Enf27MBqgOINQqhHYy5+G25brR5l+emf6kkCTdMC0iVfXcLDKx5u/uGy5+H9i1SSu+htuGWF6lCmJ8g4kTdOeJbz+p4BpnZuYXQTvqyunwu8CLzTxvwZwED3NBl4xf3aPZlD4ZoFEBIBxh52VyQ2Ay6aC1PvhKSR6rMN88ASri56grEUoGndTJWjil0lu5jeezoU71O9x8UPhJEXqhq68df0zE6xhp6lJlAd+VjC/RvPUfJZ8VJKuQQoOsIi5wDvSGUlEC2ESPFNdB5UWwWL/6n+OWwJbfas1COkjFY1GFLCmrfUPfu5Z6ke/jRN86uthVtxSRcjDm6CFyfCgvvVjKg01SlNT0zwwEvrX+KCLy6A3M3w/GjI+q+/QzoqgVSHnAo0HRngoPuz7mXx42rKXufvSAKHEHD116rTn8Nb4dUT4Ms/qsEoNE3zPZeLLZv+D4Bh6+bB8HPh7Of9HFRgcEkXO0t2UhfbTz0m/OUfoabM32F1mZD1zwn6YmdC9AW+klL+pv9EIcTXwBNSyqXu9z8C90op17ay7I3AjQBJSUnj582b1+lYKioqsNlsnV7vSCLKdjBu3X0cSjmVHYNv8ei22+ON4/EGk6OCvnvn0SvnGzaMfpjS6Na70uwux9MZwXZMwXY8EHzH1Nbx9Mr+ltdLPmadNZyn4m6jLGqwH6LrPF/8fZaWL+XDog95JPUR0qvyGLfuz149p3flmE466aS1UsoJHVpYSumzCegLbG5j3qvApU3eZwIp7W1z/PjxsisWLVrUpfXa5LBL+eJkKZ8ZKmV1iWe33QEePx5vK81u/Hnpv6TcubDZ7G53PB0QbMcUbMcjZfAdU7PjKT8sZc569bO9Qs54/3j5p4V/9EtcXeWLv8/i/YvliLkj5IbDG9QHC/4i5YORUu5e4pX9deWYgDWyg3k3kKrrvwB+725lPwUolVIe8ndQHbbpY8jfpvp4D43ydzSBr34Anrpa1Zf0u+fC/OugPM+/cWlasHG5YM2b8OJ4+OQGcLkolXUcqC1muKcGpQki9d3ZNgw5e9JfIao3ZH7jx6i6zmfNvoUQHwDTgHghxEHgQcAMIKX8N/ANcCawE6gCrvFVbB7Rawwce0fzwRi09pkscNPPqreppc+qRi6nPAAyw9+RaVq3ZyvfDW88AtlrVH/yM58Fg4EthVsAvDO8bDeXFJ4EqNH5ADXIzU0/qVHuuiGfJXkp5RE7NXZXQdzqo3A8L2k4nPaIv6PonsyhcNL9MPIi+OZu+O4vhE54wd9RaVr3dmA149ferZLTeXNg1MUNj69uKXAn+Xid5FuKCYnBbDA3JnloTPBVRd0u2QdSdX33JCUsegLyM/0dSfcXPwCu/Bxu/Ikaq7tL3D1LGgeR0DStfU53L3ap49mdcQXcvgZGX9Ksf4pxSeO4dcytRFqCsBfOoySEIDEskbzKFrcOd3wHzwzudo8A6yR/tPb+DD89CfuW+TuS4CAEJA1TP+/8Ed4+G764Td271zTtyHZ8Dy9OgKI9YDBwoPcFasTLFsYnjefm0Tf7IcDuISksqXlJHtQQusYQdWuxG9FJ/mgtmw3hiTD6Mn9HEnwyToIT7lUN8/7vfFVVpmnab0mpxsr44BIIiWwcGbIVFbUVbCnYgsPZSr/1GgDHpR7HqPhRzT+0RsOEa2DLZ1Cy3z+BdYFO8kejshB2LYSxV/Tsnu28xWCAk/8K57+mhoJ8fboazlbTtEbOOvj6blhwn2r4e+2CxqdXWrE2by2/+/p3bCroXtXOvnTjqBu5a8Jdv50x8TqQLpXouwmd5I/G9q9AOlVvUZr3jLoYrvoS7GWQp09MmtbMsn/BmjfguD/Cxe+229f6qIRR/O+J/8vQuKE+CrB7ko19tjSK6QspY2DL536JqSt62MgpHuaoVkMwJo9qf1nt6PSeArevaxyuNwgGjtA0j5jyBzWwzLBzOrR4TGgMp/c93ctBdW9Ls5dy1+K7eHfGuwyObdEb4MxnulULe12SPxpTboYbftSjqvlKfYLP/Bb+NVJX3Ws9l5Sw5H+hIl9d7HYwwUsp+SjzI/aV7fNygN1bmi2NCwddSLi5lYJE2gQ10mY3oZN8V1WX6Ee7/CVljLov9un1jY8LaVpPsvp1WPgIbPuiU6sdqjzEIysfYUXOCi8FFhz6RvXl3on3khaR1voCuxbBDw/5NKau0km+q+ZfC+/M8ncUPVNkihoxK+dXWPyEv6PRNN/Kz4Tv/wYDpsOEazu16uaCzQCM0N3ZtqumroZSe2nrM3N+VY/SlRxofX4A0Um+K6qKYM9P0GusvyPpuYadA2OugJ+fhX3L/R2NpvlGXS18cr2qoj/n5U7fKtxcuBmTwcSgmEFeCjB4zPx0Jk+vfrr1mfW3R7b+x3cBdZFO8l2xdym46nQ/9f4240mI7Qf7V/o7Ek3zjaXPQe5GmPUCRCR1alUpJWty1zAoZhAWo8VLAQaP/tH92VG8o/WZcf0hYQjsXuTboLpAJ/muOPALmEJ1Sd7fQiLU4DbHt/I8q6YFo4nXwVnPwZCZnV71+33fs6lgE+cNOM8LgQWf4fHDySrJwu60t75A72PgwCpwOX0bWCfpJN8V+1dA6vgj9iql+UiITb3u/0W1ute0YGSvUJ3ehMd3+j48QJWjiqdXP82Q2CFcNOgiLwQYfIbHDafOVUdWcVbrC/Q5FiJSoOKwbwPrJP2cfFdMvRMMZn9HodWTUrV0zdsCf1gG0en+jkjTPEdK+PIOKDsEV38FBmOnN/HaptfIq8rj6ROfxtiF9XuiYXFqDI0tBVtab6g48iLVUVeA0yX5rhh6Ngw+w99RaPWEgHNfUr0PfnYzuFz+jkjTPGfTfNj8CQw4pUsJfm/pXuZumcus/rMYm6hvMXZUSngKMSExbCnc0voC3aR/FJ3kO+vgGshe5+8otJZiM+C0R2HfUtixwN/RaJpn1NXCDw+q24NT7+zSJmKtsVwx9AruHN+19XsqIQTD4oe1neQBlv4LXjnOd0F1gU7ynbXoMfjidn9HobVm7JUQ3QeWPK07KtKCw4YPoCwbTvpLl0rxAJGWSO6ecDfx1ngPBxf8hscNZ1fJLqrrqltfwBIOeZsD+nl5neQ7w1mnWlP2nuLvSLTWGE1wyt/VvbIAb/GqaR2y+RP1FE//Uzq9anVdNa8efpWN+Ru9EFjPMDxuOE7pJLMos/UF0ier1/2B24OgbnjXGXmbobZCPTqhBaaRF/o7gu6teK8aE6AiT03h8dDvBDX6VkfYK9QJL3ejGtfclgSp4yAqTdWudJP7mAHjik/U36ELv7fs8mxyanOoddZ6IbCeYWziWB4+9mHSI9pozJs0HCwR6n8+QBvh6STfGdlr1WvaRP/GoR2Z0wEbP4SEoZA23t/RBK5DG2DB/aqb1Du3gDkUVr4Cv/y7+XLmMLhvH5gscGijStz1HbHU2cFeri4GinbDixNVR1FNnT0bxl8FOetg7tlq3Yk3wOSbwaArE1vlcoGzVv1NotroP70dA2IG8EDqA0xInuDh4HqOmNAYzht4hH4FDEZ1jqnPDQFIJ/nOKNgBFhtE9/Z3JNqROB3w3wchZTRc+am/owk8zjo1BvniJ9WQmcNmNSaUiTfA8PPBlgjhCVB6EAp3qgQP8J9bVSk9YQijHWZYmqVuj5zzIsT0gxP+R1Vhpo4HR5UqhUb0UutaY2D81eri4rv7IfMbOPcV/chja7Z/Cd/8D1z9tRpGthNq6mr4ds+3nJlxJiahT/FH62D5QbYXbWd6n+mtLzD8PPU9CVD6P6Azpt0PY6/QVY6BzhIGx96mnp3PXqeqizXFUQ3vnKN6bRx+Hsx8tvnY2PEDgAGN7xOHqKnerBfUuA27f8KUtwfGXwODZ6h5QsC0PzcuGxoJEcmN72Mz4IzHVbX9r++qWoR3ZsFta7rcqCwo1Q8ja7F1ekjTvaV7ueene8gszsRkMBFBhJeC7Dm+3PUlr2x4hZWXrSTMHPbbBcZf7fOYOkMn+c6wRqtJC3wTrlP9fP/8DPzuPX9HEzjMVnW7aeINqv1CZy9Ye41R03F/ZO3ixUybNq3zMQgB436v7vWXHFAJ3uWEmtLmFxw9VdZ/VW3JOS916uLn2z3f8tDyhzAbzbx0ykuckHYCiw8s9l6cPcR5A8/j9L6nE2oKbXshR426TVXfA2cA0TfEOqqmDBY+Boe3+zsSrSNCI2HyH2D7V6onvJ6sPBc+uFTdTwc4/TEYdZH/a6Ri+kK/49XPy1+AlyZDZg/v40BKWPIURKXDqEs6tEpNXQ0Pr3iYe5fcy6CYQcw/ez4npJ3g5UB7juTwZDKiMzCINtJlTSk8ngJr3vRtYB2kk3xH5WeqL1/xHn9HonXU5JsgfYpq8d1T5W6Gl6fArkWqTUmgGjBdtQP44BJY9IS/o/Gfg2vg4GqY+icwtt919t7SvVzxzRV8vONjrhlxDW+e8SbJ4cntrqd1zsL9C3l7y9utzwyNUu1X8tt4zM7POl1dL4R4C6gA1gGrgS1S9oCeR/LdJfiEwf6NQ+u4sFi47jt/R+E/tZUw/xowWuDa7yEhgMcQTx4BNyyEL+6An54llanyAAAgAElEQVRUtxQGttHQKZilTYDff9H4/HUbpJS8uvFV3tr8VrPqec07lmYv5Zs933DZ0MswtzZuSfygxhwRYDpckhdCTBBChEsprwHuBXYA04FXvRVcQMnfroaXje7j70i0zqou6Zkj1C34MxRkwflzAjvB1zOFwNn/gsRh8MVt6j5nT1Lfj0DGiepJh1Y4nA5Adbm6pXALk1Mm6+p5Hzi217FUOirZlL+p9QUShqiasgAs73amuv4dwAkgpawGMoGdUsobvRFYwMnPhLiBuhVwd7TkafjwCijZ7+9IfMdZp6apd0LGNH9H03FmK1z4Flz0dpuJLmjNvwYW/7PN2SsPrWT6/OnsL1P/x8+e+CyzT56tq+d9YFLKJAzCwPKc5a0vkDAY7GVQfsi3gXVAZ5J8jZSy4dJaSlkAPOz5kAJU6QFdVd9dTbkFEPBLz6h0AlQXv+e9Aic/4O9IOi9xCPR2V1f3lAuzgp2w5TNo0rhLSklmUSZbClTD0QHRAxiTMAanVF02mztwz17zjEhLJCPiR7Aip43ua/udCKc/rmp7A0xnkvxuIcSMFp9ZPBlMQPvDClWVqHU/UalqvIF9bVyFBxOnAz6/BQ5vU++7c49yG+bB7HGqMVqw27cUADn8PDKLMnnh1xeY9fksLvzyQmb/OhuAeGs8z5/8PP2i+vkz0h7r2F7HsrlwM6X20t/OTBgEx9wakI+AduYMcDvwhBDifSHEHUKIV4FdXoor8BgMEKI7lui2UsdB7qbgv8+76HFY/x4c3urvSI7eoNNVZzrzr1WPKQUph8vBun0/MjshiVk//4kLv7yQ1ze9TlJYEg9MeYDHpz7u7xA1VJJ3SRercle1vkDJ/oB8XLfDreullIeEEOOBc4ExwEbgLm8FFlD2LoX178P0f4Atwd/RaF2ROgFcDsjfpkb1Cka7F6sOgMZeCSMu8Hc0R88aAxe8AW/NgK/uggte9/+z/R4gpUS4j2POxjm8sekNquqqMNhCmBiWzJXDfs8pvU8hzhrn50i1pkbEj8BmtrE8Zzmn9jn1twt8fI0aevaqL3wf3BF0qi5PSumUUn4ipXxASvmSlLLSW4EFlJxfVelI3wPrvgZMh//ZHbwJviIfPr1R9XM+o+3GW91O78lw0v2web76DnZDNXU12J12AJZnL2f6x9PJq8wDID0inbMzzuK5yHEsGXY7r5/+OhcPvlgn+ABkNpiZmDyRFTkraPWp8ahUKMvxfWDt0N3adkRptupHOjTK35FoXWUJU1OwWvqcelTwik9VaSKYTL0LDqwOyMeTWnK4HOwp3cPWwq1sLtjMxvyNZBVn8cTxT3BGvzOIs8YxLmkcNU5122hGvxnM6NeyqZMWqI5PO56imiIqHBVEWFrcvo1MU10SB9iQyjrJd0RZNkSmBtQfrqNcLkmt04XD6aLlKVIAZqMBs9GA0dD9jq3Ttn2pvoSzZvs7Es+b/iAMPVt1KhNsDEa47MOA/P5V1Fbw1e6v2F60nW1F29hZvJNalxq/3Wa2MSJ+BNeMuIaMaDXQzODYwTx94tPNN1JZqAoQxuA/HUtZfz6SuFq5aDMZBBajAZMxMBuMXjToIi4adFHrM6NS1ciL1cUB1QCvw/9VQoh/Sinva++zoFSWrf6AflbndHGguJqckmpyS2vILashr6yGwopa9uZU89zmpZTV1FFW7aDG4Wz4MnWE0SAwG9UXzGoxEm4xERZiJMxiItxiJDzERJTVTJTVTHSYueHn2PAQ4m0W4iNCiAgxNdxrDEiFu2Dd23DKgxAeZNWhphDoc4y/o/AeIaCqCHZ8pxrk+fAk6nA5KK8tJzY0Fikl9/x0D+OSxnH50MsBeOyXx4gKiWJI7BAuG3oZQ2KHMDRuKH0j+7bd33lTX94BRXvglsB++sNe56SwopaCCjuFFbWUVNdSUuWgtLpxKq+po6q2jkq7s9lrbZ2rU+cjgwCLyUCIyYgtxESk1UxkqInayhq+zt9AUmQoSVGhpESGkhwVSp+4MCJCfXc71elyYmzZZ0qkO0eUZXfPJA+cCrRM6DNa+Sz4GMydHvLxaJXXOFi7r5hf95ew83AFOw9XsKegklqnq9lyUVYz8TYL1EFamIXeceFEhJqwmo1YTAYsRkPDa8v8KyU4XC4cdZJapxOHU1Jb56K61kllbV3Da2FlLfsKqyitdlBS7cDpav2LajEZSLCppJ8SZSU1xkqvaCup0aH0iraSEmUlNtziv1qD1PHqNXstDDrNPzF4mssJc06ESTfBuCv9HY13Fe6Ez2+Gi+aqYXI9zOlyku/IZ+H+hews2cnOkp3sKtnFntI9TEiawJzT5iCEoMbZeI/dZrHxw4U/kBiW2PUL3Oy10Pd4Dx5J50gpqax1kltaQ05JdcOUXaLe55XXUFBup6ymrs1tRLgTcUSoqSEpp0SFEmYxYbWoZG0xqVrDEJMBs1FgaOX3VedS56D6iwK7w0m5vY5yd+GloFpyKKuA/Ar7b85DyZGhDEyy0T/BxvBekUzqF0vv2DCPFzxe3fAq87Pm8/0F3zffdu8pcPE7EJXm0f0drXaTvBDiD8AtQIYQYmOTWRHAMm8FFlB80P95ndPFit2F/LjtMKv2FLE9twyX+9ZO79gwBibamDYkgQEJNtJiwkiOCiU5MhSrRV1NLl68mGnTJnk9zvoTQmm1g5KqWooq1ZV9Qbl6za+wk19uZ2d+BT/tyKfa4Wy2vkHQUPpPiAgh3hZClNVMjfvLXFFTR4W9jrzCKuTKhTicLpwuiVNKnE5Jnfvn+m0ZhDpZCKFqI4xCYK6/sGlxkRNlcvEqBv77/Vcs3pxCmMWI1WzEajE2nJxsISZsoSYiQsyEhxiJCFU1FhZTYFYfkrtRPRpoCvF3JN7XaxxYImD3T0eV5KWUFNUUNTRum71uNstylrG7ZLe6V+5uO5VqS6V/dH+mpk5lVPyohvVfOuWlZttLCk9q+Nnlkioh1TiosKv/5Yqauob/7Up7XcMFdLXDibnyEA+UH+L97AS+nLMSe527Bq5OVWvX1rmw17moc7lwuSRSgktKXO5XKdXTvSaDAYMAk9GAQQhMBkGYxYisrWZO1som/9cmap2S/HK7+t66pxpH88KDQaikmRJtZWhyJPEDLMTZ1Pe1vuYuJsxClLuE7avq9cXu4Y2dLklBhZ1DpTXkllazu6CSnXkV7Myv4KM1B6iqVeedxIgQJvaN5bgB8Zw+PIk429F/TwbHDmZG3xnUOGuwmqyNMyKSYdg5R719T+tISf594FvgCeDPTT4vl1IWdWZnQogzgOcBI/C6lPLJFvN7A28D0e5l/iyl/KYz++hOXC7Jit2FfLUxh++25FFUWYvVbGRcn2huP3kgk/rFMiY9mvCQwLlXJ4RoSIap0dYjLiulpKTKQba7ZHCotKbhpJLvvijYU1BJabWDMIvRfSIyExFiIincQJ/UWEJMjScto8GA0QAGg0AgkFI2nPCcLomU6iLA4T45OpwSu7tEUFvnJN9uYp+hN9FFG/lvSR41DlWV2EbFRDNWs7HhFkWU1Uyk1UxMmJnYcAsx4RZiwy3EhlmItanXOJsFmy9uX+z+Sb326wF9lxtN0HeqelSwA6SUFNYUsqNoB7tKd3HF0CsQQvCPFf9gycElLLx4IQBltWVEh0Rz8eCLqcur46zJZ5ERnYFZWCmstFNUqS5m/7M+m2L3z0VVtRRXNq+qLq12UFbj6FD7wFCzAavZyJnG1QCsrcugzuUizGIiusnFaf0Fq/r/F+6LWvd3QNDwPahzSXUx7FI/1zldVDuc7M+pobbOxf7KKsrdF9Bmo6HhIrtffLhK2rYQEiNDSI0Oo1e0KkAE6n1xUBf0SZGhJEWGQnp0s3kul2RnfgWr9hSxZm8Rq/cW8/WmQ/zt800c0z+OM0emcNbIXkSFda16f1r6NKalT2t95p6fVX8qvcZ0adve0G72kFKWAqXApUezIyGEEXgJVe1/EFgthPhCStm0146/AR9JKV8RQgwDvgH6Hs1+j1rOr/DDQ3D6E5A0zCObdDhdfLE+h1eX7GJHXgXhFiOnDE3izJEpTBucQKg5OPrHF0IQ406CI1I792SCumL3whflm9PpV7yHNZerEc6kVBcCle5SV/2JsL5Gobym+Um8/h7kweIqNmc7KKqqpbbO1equQkwGEiJC1GQLobbczq+OHSRGhpASFUpypJXkqFBiwsxdvxjYvRgShqpSRE+QcSLs+BaK90FM42BRNXU17CrZxY7iHewo3kFWcRZZJVkU1TSWQ07tcyrJ4cmcnH4aSSEDWL6zgLzyGuLtlyLtdg7stJN1IJeFO4rIrzhESZWj1RCEgGirmZhwS8Ptsv4J4c0uAOvvIdtCzNia1BKFh6j2Lob6W1bf/wwrzTxzx5VeqY1R36NjPb7dQGYwCAYlRTAoKYIrpvRBSsm2Q+V8s+kQ32w6xF8/28zjX2/j0km9ue74fqREHbmw0ppaZy0Hyg/QP7p/8xmf3awuRM8PnC60O9PwLgS4AJV0G9aTUna0//pJqAFtdru3Nw84B2ia5CUQ6f45ioaKMz8q2KlOpB1pQNMOl0vywer9vLRwJzmlNQxOiuDZi0dz5siUoEnsAe/Mp5q9FUIQajYSajZ2qSpPSklVrbOhtFdUVUtRRS2FlXZ3lWgt+eV29hVWkVNcx08Hs35T0rOYDCS7GxD1igolLSaMtBhrw2tKdCghplb+Pxw1sH8ljL+q03F3W/1OpEoINm37iGETbibCEsH8HfN5dOWjDX26hxpD6RXWl4ywSQyzpuGyJ1FZkcDVr+0gt2yjO3nHAL80bDbMYiQhIgSLC/on2JiSEaeqpiMsxIVbiA0PITZcNTSNspo9165k2LmqvU9PuN3iJ0IIhvWKZFivSO4+bRCbs8t4Y+lu3lq+l7nL93Lu2FTuPX0wiZEd73f+8V8e57/7/svPv/u5eePKqFTV8C6AdKYe+D+oEv1awN6FfaUCB5q8Pwi0HDT5IeB7IcTtQDhqKFv/qv+DHWXr+qy8cv786SbW7itmQp8YHjtvJNMGJwR2a3QNAJd0UeWoosJRQUVtBRWOCkYljMIgDGQWZ3Kg/ACn9jmV9NgwVh1aRYHYR0wEJKSYMBqMGIURo8HI9q1ZjBg2ihqHgV4ho8krrWF30WEOl9dQXG7hUGk1q/cW88WGnGa3EISApIhQ0mOt9I0Lp19COP3iwhlgs9NvxEWYBp/pv1+Ol1U5qthSuIVNBZuYnDyZ4YnD2HzZ+1y//D5uNQ0j3DWCdYdCSXTNoLI8iYKiWMqrY8hv0s9XdJiZ1GgLaTFWJvSNISXK2nBRlRylqnxt7ltiquQ73ncHmDZeTZpPCCEYmRbFv343lrtPG8wbS/fw/qr9fLcll7+eOZRLJqZ36Jw8Pmk8n2R9QmZRJkPjhjbOiExVtb8BRLTac09rCwqxWUrZ5YdwhRAXAadLKa93v78SmCSlvL3JMne5Y3pGCHEM8AYwQkrparGtG4EbAZKSksbPmzev0/FUVFRgs9naXW5A1hyScxez9Pj3O70PUI1jvtzl4MtdDkJMcNkQC8f28vy92o4eT3fhzeMZteFBKmx92d3/Gqpd1RyqPUTfEPW40/rK9Wyo2kCps5QSZwkVzgpqZA2yRS8DT6U/hdVg5dOiT1lWsYxnej8DwNz8uaytWnvE/YeIEP639/82LL+/dj9/T/07AHMOzyG7NodQEYZRhiFcYTjrrDjqwqixh1FeHUFFVQyumsYWvLGhguRwQarNQJrNQGqEgVSbAavJuxeQnv4buaSLXEcue+x72Fe7j732veQ6cht+9wlVM6ktPIG8ajuu0L04q3uDKxSLEZLCDCSGCRKsgnirgXj3a5xVdOr34MvvkcVeRHjlPkqjhuEyeqckr88L7cutdPHWZjuZxS6GxBq4bkQICWFHrrktqSvhgewHOC/mPE6OPLnh84xdb5F28GuWnPBxh/t16MoxnXTSSWullBM6smxnSvLLhRAjpZSbOhVNo4NAepP3afy2Ov464AwAKeUKIUQoEA8cbrqQlHIOMAdgwoQJctq0aZ0Opr6VZrsOzYHY3h1btoUah5M7PviV73fmMWt0L/5+9jDiPdC6szUdPp5uwtPH45IuDpYfJLM4k/f3Ormwdg/Tpk3jvW3v8dyq51h40UISwhLYtWkXuTtySbQlkhGWQVxoHBGWCGxmGzaLezLbmJw8GbPRzIjqEZTXljeMDDamZkzD41Uu6aJO1uF0OXFKJytXrWTM+DG4XC5GJowEwHrISnFNMdP6qWPds3kPmcWZlNpLKbOXUVpbRIW9hDJ7GTJEQiSMjhrIIxPf4vDeLfzvgVdx1UViLrmEpTkVOMKWgcuKqzaW5LBeDElKZHByBKNSoxiVHk2vqFCPXWB66m9kd9q5a9E9rM1bR2VdGQAGVziO6jTqqobhrO6Ny55GXFQCQ9JtXB5ewMz8ZRROmkLKgLEkRYYE3DF1yNq5sOIhuH0dxPVvb+ku0eeFjrl4huTDNQd4/Jtt/HOdkzevHseotOgjrvPap69RYitpHk/odjjwOdMmjYTw+A7t29t/o84k+anANUKI3ajqegFIKeWoI6/WYDUwUAjRD8gGfgdc1mKZ/cApwFwhxFAgFMjvRIyeF5HUpY5Tiipruf7t1fx6oIQHzx7GNcfp4SF97XDVYZZlL2N5znJWHFrRMESkQcCYkiKSHTWcnH4y6RHpDV1UXjfyOq4beV2H9xFvjSfe2vhljg5t+8RwwHKA4XHDm302OaX5HatrRlzT6rou6aLEXkJ+VT51rjqG24DXZ7J19AyiBk3iymFTcTpdTH7/b9hd6iKjHFjjCueXfTE4MxNx2pOJMKQzMmEI49P6MDo9mtFpUUSH+XbEaIfTxd0L/0ZRpZ1edb9nw4FSDoTsx+UYSF1VBrGGQQxJ6MeQvpEMSopgcFIEAxJtDY+LUnIA/vVfeteeDFHduAOgg2vUIDw+7oND+y2DQXDppN5M7BvL1W+t4pJXV/Ly5eM4aUhim+uMThjN8pzlzQYcYtgsSJsAIZFtrudrnUnyR9XBspSyTghxG/Ad6vG4N6WUW4QQDwNrpJRfAHcDrwkh7kQ1wrtadvR+grec9VynV8krq+F3c1aSXVLNy5eNY8bIFC8EprWmqKaIuZvnsixnGTuKdwAqEZ+YdiJjE8cyJHYI/XMzsc6/FnI3kZI+kRRb4P99DMJAbGgssaHunrS2fwPSxW1jblGteQGj0cDCSxaSXZ7NwYqDHCxX096yfewo2klJ7TocwLaa41jyw9lI6SIk6StSTFOY1mcSxw2IZ3JGLJEe7DnM7rTzS84q/rPjBzblbyex8k7W7C3GFV0OgK3qMKPSojgj7WnGpEczKi2q/QaQ0ekQ2181iD3mFo/F6nPZa1UHTbpdTsAYkGjj01uO5dq5q7n+nTU8feEozh/Xeuc2oxJG8eXuL8muyCYtwr1MZC81BZDOJPn9wOVAhpTyYfcz7cnAvo5uwP3M+zctPvt7k5+3Asd1IqaAU+NwcuM7a8grq+G96yczsW/gdG8YrBwuB/lV+fSy9cJsMDMvcx4j40fyp3F/YmrqVAbFDGpenWtx18xkr4H0if4J+mjtXgwmK6Q1jz/SEklkXGTzxkBuxTXF7CzZSaQlkpSwDH7etZO/r11PeN1g5q3ezztrVhGS+C2JliFMTB7LzMGTOLZ/cust+4+g1F7KB1u+5rvdC9ld+SsuapEuM87KATgdJfxuYm/G97mfMenRpMVYu1bVnjENNn4ITkf3HB3SXg6Ht8HQWf6ORGshMSKUD288hhveWcN9n2wkLSaMSf1+ex4fnTAagI35GxuTvKNGjZiYMhqSR/oy7DZ1Jsm/DLiAk4GHUbWBnwDd9CzZAVVF8OYZaqjLDvSwJaXk/k83seFgKa9eOV4neB+5/cfbKawp5KOzPiLCEsHPv/uZkCM1ZIpMgTGXQ3Rv3wXpabsXQ59jO/XoVUxoDBOTG7+uM4cPZcawFThdTlzSwAcbFvHathKKnJ/xXfFnLFhhRCzqy4jo47l27CxOGTig8fnuFqSU/LB7Ja/++j6ZFctAOHA5ogmpncS42GOYOWgqJwzo1anHlI4o40RY84YqDfee4plt+lLOekCqql0t4ISHmHjlivGc99Iy/vB/a/nPbceRFtN8FMuBMQOxmqxsyN/AmRn1T7hI+M+tcMrfu2WSnyylHCeE+BVASlkshPDtzTxfqyqEgkxVWuiAOUt289mv2dx96iBOH95DOifxg5q6Gj7f+Tmz+s8izBzG5UMvb9b6/YgJvt65L3sxQi8rO6T+L8deftSbMggDBnfPZlePn87V46dTVFPEyux1LNi5gl/ylrLZPpc7V7yNaUl/JsSfyN3HXMHQZNUOodJex4LNuby7fhG7TE8hnSFEuY5hVr9zuWT0MfSJC/fOY6J9j4fkUVBb6flt+0LvKXDTEnXbQQtIUVYzr101gXNfWsYN76zlkz8cQ5ilMWWaDCaGxw1nY36T3t7NVjCHqQJigOhMkne4e62TAEKIBFTJPnjV/6Gs7ZfINxwo4ckF25k5MoXbTh7g5cB6rq2FW7lr8V1kV2QTbg7n7P5nc3xaFwf3qCpyfyk73+OVX1mj4dIPPdYDY0uxobGc2X86Z/afjpSSTYczmbPuM1bmLWJl8f8x4/m+jEqLp8j8FsXrP6Uy93TSYlM4YfAfuXXSOQxLSfBKXM2ExcLNP3t/P95iNKsqXS2g9U+w8cKlY7l27moe/Xobj5/XvHR+z4R7CDW1qJ0Ki+u2SX428BmQKIR4DLgQ1Q1t8KoqVK/tDBvockn+/sUW4m0hPHnBSN3BjZd8lvUZj658lJjQGF477TWmpBxFNe2BVfDGqXDZR2ro0u7EbIXBZ/hkV0IIRiUN4cUZ9wP3s/XwQZZut/PpumxKnGYGJ8Vw/7nHMKFPjH/+7+tqVcO17nZf/od/qP+77niroYeZNjiRa47rx5vL9nDpxN6MTGvsont4/PDfrmCNacwdAaDDfbVKKd8D7kUNVHMIOFdK+bG3AgsI1e6rsXaS/Py1B9lwoIT7Zwzx6ZjGPYXdaeeh5Q/x9+V/Z1zSOD46+6OjS/AAicNUV8UH13gmSF+REpbNhvxMv+x+WGIaN57QnwV/OoEXR13Bp5f+g4l9Y/2T4HPWwz/7dHjAmoBRmg1Ln4VDG/wdidZBf5w+kLjwEB78YjOuJt1RSin5LOszlmcvb1w4LK4xdwSATg1vJqXcDmz3UiyBJywO+p0IYW13alBa7eCfC7YzoU8M5409uq5vtd/KqcjhzsV3srVwKzeMvIFbx9yK0eCBfv5DbGpgl+xuluQLd8J/H3DHP9jf0fhXwmBwOVWSH3iqv6PpuPr/uVTdnW13ERlq5r4zBvM/8zfy2a/ZXDBetaYXQjBn4xxGxI/g2FT3QECzZoMxcJqrdWQ8+aVSyqlCiHJo1rdnfWc4gfPUv6cNnqGmI3j+hyyKqmp5e9YkXU3vYcuyl3Hfz/fhdDl5/qTnObn3ye2v1Blp42Hrf1TpuLv87epLrRnT/BhEgDBbIX1S43C73cXBNSoJBEjra61jLhiXxvur9vPEt9s5fURyw3gH7818j+iQJp1gBdhTO+1W10spp7pfI6SUkU2miKBO8B1QUlXL+6v2ceG4tE4PpaodmVM6mZc5j8SwROadNc/zCR4gdQLUlELhLs9v21t2L4ao3hCje1AE1MVO3iao8G/HmJ2SvVYleD3yXLdiMAj+NnMYBRV25q9pHGstNjS2+Uh02Wvhp6fBWeeHKH+rw/fkhRBvCyGim7yPEUK86Z2wAsSnN8J7F7U5++M1B6lxuLh2qj7heppRGHnh5Bf48KwP6RPZp/0VuqL/yXD28+22uQgYLifs/Vk9I95dah68LWOaet27xJ9RdJyUUJGnq+q7qfF9YhiTHs07K/Y13Js/UHaAh5Y/xK4Sd2Hh4BpY9CjUlPgx0kadGSR9lJSyIWopZTEw1vMhBZCS/eCobnWW0yV5d+U+JvWLZWhKj67Q8Lj1h9eT71AlM7PBiw0Zo9Nh/NXdJ8kX7YY6u66qbyplDEz7CyR1k6pvIeD2tXDaY/6OROuiq47tw+6CSpbtKgCgTtbxSdYnbCpwj91W/8h1gDxG15kkbxBCxNS/EULE0smGe91OVWGbCWBx5mH2F1Vx1TF9fRtTkJNS8sjKR3ir4C18MmxB8T7I/Nb7+/GE+IFw3z4Ycpa/IwkcRhNMuw8SBvk7ks4xBU7DLK1zzhyZQly4hbeXqx7d0yPSsRgsjSX5+pwRII/RdSbJP4MabvYR96Ayy4GnvBNWgKgqarMjnLdX7CMpMoTThif5OKjgJoTglemvcFncZb5pyLjmTfjwStXndHdgDlWT1shRDVk/QMXh9pf1tx8fga/u9HcU2lEIMRm5dFJvftyex4GiKkwGExnRGWSVZKkF6pN8gDxG15nn5N9BdYCThxr+9Xwp5bveCszvXC71Rwr77TCz+wurWLIjn8sn98Fs7Mx1knYktc5apJQkhiWSZml95CePS5sALgfkbfHN/rrKUQNvnAY7vvN3JIGnZD+8dwFkftP+sv624ztVe6R1a5dN7o1BCN5ftR+A/tH9m5Tk3TmjG5bkkVJukVK+KKV8wT1iXPBy1sKoSyB13G9mfb81F0A/F+9hz659luu/v546lw9bpdY/7lKe47t9dsWBlXDgF39HEZjiB0FESvfoFKcsG2K81JBU85le0VaO7R/H91tULhgQPYDcylzKa8shMhXu2QmjL/NzlEq7SV4IsdT9Wi6EKGsylQshyrwfop+YQ+G8f8OQmb+Z9cO2PAYnRZAeG9bKilpX7C3dy4fbP6R3ZG9MBh829ajv6KiywHf77Io9S8BgUiPPac0JoTqt2rNEtV4PVC4nVBcfsXMtrfs4eUgiu/Ir2VtQydH+C4MAACAASURBVMDogQCqNG8wgi1BtRcJAPo5+bZI2eoJo7Taweq9xZwyNNEPQQWvZ9c+i8Vo4dYxt/p2x+HdJMkX7oKYvhAS4e9IAlPScFU9ag/gckdVESAb/+e0bu2UIao91g/b8hgQowYl21myU81c/gJsmOev0JrpSEn+XffrH70fTgDZ8R08lvyb/qV/2pGP0yV1kveg1bmrWXRgEdePvJ54q49PgKYQuGYBjLvSt/vtrMp8CNf/c22yuRvABnKnOE676oAppq+/I9E8oHdcGAMTbSzcfpiU8BSsJmvjffkNH6reNANAR+oTxgsh+gDXCiHeQXVn20BKGRhNCD2tpgTqasBia/bxwm15xIZbGJMe08aKWme4pIunVz9NcngyVw7zU6Ltc4x/9tsZcQPUELNa6waeCresDLguRZuJSoMbfvR3FJoHnTI0idd/3k2F3cnI+JHUOmvVjLCYgHlOviNJ/hVgAdAPWEvzJC+BDC/E5X/2cvUa0nhHos7pYlFmPtOHJmE06B7HPGFp9lK2FW3j8amP/3ZcZl/Z+SPUVsKwWf7Zf0fMmu3vCAJbWGz36dRICxrThyby75928VNmPm+c/kbjDEsEVAXGUxQdaV0/SUo5FEBKmSGl7NdkCs4ED02SfGNJft3+EkqrHbqq3oPW5a3DZDBxWt/T/BfEqtfgp+Du8iHoOR3wyxw4sNrfkbRt/QfwylSoDozuTrWjN7Z3DDFhZn7cltd8RoitMYf4WUeSfH11faa7v/rYppO3A/Sb2goQRmhSuly1Rz33eNwA3XDGUzYVbGJwzGBCjH4crCM8DqoCuOFd6UF4djhs/9rfkQQuYYQFf4YdC/wdSduK96rBdFrcAtS6L6NBcOyAeFbvLWZ5znJ+/+3vKaguAEt4m12i+1pHquv/jaquzwDWtZgXvNX1qeNh8k3NBgLZcLCUjIRwoqxe7E+9B5FSkleVxzEpfr4nHp6gWtf/f3t3Hh9VdTd+/POdyWRfCIQESJAkCsguuxVZVFQEBXet1eLyaOturfZn66MPtbV9KmpbH60U60alte4rCm5BXEAU2UFkJ2whAUJmsk1mzu+PO8EBAskkM3PuTM6b17ySzNy593vJ8r3n3HPO164lZ6t2w4FSK5EZTXM4rO+je3fz2+pSXQ4p2baZWmWEx0kFHXh3+U48tUk4xUmNtwbOmQ6THtUdGtCCJK+Uegx4TESeVErdGIWY7OHESYfMkVdKsXTbfkabVnzYiAhvn/829f56vYGk5lir3tVW2nNwW2PiSje3iY4pvbM1C8GuPOVmjnwcGtTd+pvhqDuBZyc8qzmaI4Wy4t3NInKliNwHICLHiciICMWln7fGWrwiYNeBWvZU1R38hhrhISJ6u+rhh3nLNlmG8giewJrsJskfW1quvVvynnIzRz4O9c/PxCGwbFvQWIvNn8GbN9vivnwoSf4J4EdA41p9VYHn4tOLV8DTZx78ctm2SgAGFmTpiijuPLn0SX6/8Pe6w4BeE+C2b+07/aqx8EpaZ71x2F16nr3nyXcdCD1G6Y7CCLPUxAR65WWwrLSSqe9N5X+/+l+rLPS3L9hikGUoN4dGKqWGiMi3YNWTF5H4rZdY5z5kgMyy0v24nGJqx4eRx+vB4/XoDsPqordjN32j7CLof5G1cI9xdGf93lpS1K4m/FF3BEaEDCrowNzVu+iTV8/G/RuhW6D0cb1bb2CEluS9IuLEGmyHiHQG/BGJyg7q3Yd0jy7btp8+XTNJdh35R2TD/g0UZRXhEFORLhR3Db9LdwiW+mpY/JTVyioYpjuaIw28xHoYx5Z2ZMVIw4iGQd078J+vt5HtymOrZ/0Py0/X6U/yoWSlx4DXgTwReRD4DPhDRKKyg6CWvN+vWFFa2WRXfWVdJRe+dSHj/jOO3yz4DXtr7bHKkd35lY2uD0Xgg/vtW8XMzkVX7KT8e/jgf+CADSsKeirgoWLbrGduhNfB3NDQkR3uHfhdgeJl9TF0T14pNRv4FVZi3wGcr5R6OVKBaVfvPrgQzsZyD1V1DQwqsLp0lVK8su4VvH4vic5E/nDqHxiVP4r3Nr3HP1b8Q2fUMWPWqllMeHUC1d5q3aGAKwVcafYdePfECHi7fZWOaJUDO+Dzv1jFfOzGs8f6+YpmhUUjanp3ySApwYGnOhOv38sefFYj0efVHVpI3fUASfywrG383o8HOPkma6AMsGanVdmqXzfrau2rXV/x2y9/S5IzifOOP49JxZOYVDyJel89b214i9uH3K5/xLjNrShfAUCqyybletNy7FuJ7sBOSEjRHYX9Nd5es+MI+8bFllLNLYV45HI6OLFrJnv3p4MLdqRmkPeb7brDAkJoyQeq0M0GOgO5wAsicmukAtNu7N3Q62wANpdbg8OKctIAGNl1JLMnzmZi0cRD3nJJ70uorKtk3uZ50Y01Bq0oX8GAnAG6w/hBWo49V72rr7a6/NLNyPpmNVais+Nc+cYLSDNDIm4VdUqlbJ91Mb7DbZ9bRqHck78Oa4T9/yil7gdOBq6PTFia+X1QtQsa6gDYVOGhS2YyKYlOVOD+6MDOA3EeNpJ3RJcRHJdxHK+seyXqIceS8ppydnp22ivJp9q0JX9wjnye3jhiQXIHqzvczi15M08+bhXmpLF7n9XBXVG9B165Fla9rjmq0JK8AL6gr30cVnY2blSWwiO9YYU15GBzuYfCHKtb+c6SO3lsSdMVwRzi4OJeF7OkbAnr962PWrixZvme5QAM6GyjJH/h3+E6G/bAHJwjbxbCaZbDYf0/2aTE5yE6FEK/C013fRwrykkDfwoJkkBF7T6rnvzO5brDCinJPwssEpFpIjINWAg8fey3xKjGuY2B0fWbK6opyknj+33f8+HWD49ZEnXKCVNwOVy8+v2r0Yg0Jq0sX4lTnPTp2Ed3KD9IybYG4NlNSjYMuw5yTtAdSWy4bYk9y/L2HA+XPAtOU/ciXlm3c4VB2adRmFVo5Q8bzJMPZXT9o8A1wF5gH3CNUuovkQpMq8a5jUnpVNZ42eupp7BTGvO2zMMhDi7pdfQ5yx2TO3LdgOvo26lvlIKNPcvLl9Mru5e++vFN2bYY3rvHqitvJzk94dxHoWN81oEKOzteqAH4bTRl1IiIwsCYrZMzb+aCnhdYc+VtME8+pNH1SqklHFmJLv40zm1MzDg46K4wJ41ZW76gf05/spOzj/n2m0+6OdIRxiy/8rOqfNURgxa1q/geFj0JI2+wV0Kt94AzyVQua6mVr8HWL2HidN2RHOqf51ur8V2l/x6tERmZyS46pSWyudyDUgqJtZa8iDwvIh2Cvs4WkWciE5ZmQS35zRVWks/N8rGyfCWndDul2bcrpdjh3kFZdVkko4xJmys34/a67XU/Hn6oDuax2Vz5ef9tjQ8xWmbXCvj6Gfu1nD3lYJfpokbEFOak8eX+Z5j0+iSrFkbQ0ui6hHJPfqBS6uBq+0qpfcDg8IdkA7l9YfxvIaMrm8o9iMDOupX4lb9FSR5g8huTmbVqVoQDjT3JCcn814D/YmjeUN2hHKpxSVS7Tb9yl5lpV6FIzwV/A9Ts0x3JoTx7zKC7dqCwUxruygImFU+Cn7wEFzypO6SQkrxDRA72U4tIR0Ls7heRCSLynYisF5F7jrLNpSKyWkRWici/Qtl/2HTuBafeAakd2VzuoVtWCovLFpLuSqd/Tv9m3y4i/H7U7znv+POiEGxs6ZbejduH3E73jO66QzlUY0vebnPl3WVmjnwoGhfE8dioF83vt1a7M9Pn4l5RTioVZX24pu/PdIdyUChJ+hHgCxF5BatIzaXAgy19c6C4zRPAmUApsFhE3lJKrQ7apifwa2BUoMqdnnlD7j3WffnsIjZVVFOYk8qXO75keJfhuBwtGx07oWhChIOMTasrVlOUVUSK3VZwa/wDbIP6z4fwlEHBcN1RxI60oFXvcm0ye6N2PyjfDxeSRtwqykkHFCt37mLwjjdwbV8CF+u9qx3K6PpZwEXAbmAPcKFS6p8hHGsEsF4ptVEpVQ+8CEw5bJvrgScCtwJQSum5HF/4BDw+AkTYUuEhJ7uK7e7tLe6qB9jl2cVHWz86uHiOAbUNtfzk3Z8wc/lM3aEcKTEN7quAH9ls0KS7zMyRD0V6HiRlWisF2oUI/OgWyLfZLSoj7ApzUnGmree6knNYvnsJbPhYd0ghj65fDaxudsOm5QPbgr4uBUYetk0vABH5HHAC05RS77fyeK3XUAeuFGq9PvZXe8nP7MxdJ97F2IKxLd7Fh1s+5E+L/8Snl33a7Gj89sIhDv5y2l8oyCjQHUrT7DaC3e+D0b80ySEUnXvBr7c1v100pWTD2S3u9DRiWNesFFSDNZWuQtTBVVN1iuZftaZWxzu8mZsA9ATGAQXAAhHpHzzgD0BEbgBuAMjLy6OkpCTkYNxu91Hf13PrRjr7hbc+mA9A3e5Kerh68N2e7/iO71q0/33V1sCfN+e/SWFSYcjxhepY52M32wL/jkXH+XTf+hqiGtja49KI7L915zTMujTeFur7Ii+WfuZaKhLn5PDVIcqPz5lsteqjKN6+R3Y/H6UU4rNqya/fW86Z3lrmf/LJMb/vkT6naCb5UiB4tFUBVsnaw7dZqJTyAptE5DuspL84eCOl1ExgJsCwYcPUuHHjQg6mpKSEo75v33/Ak0lx35Pg0y/p0TeZPr17kZfW8vXDC/YVMPOtmeT2zGVccejxheqY52MTn2z9hE4pnRjYeWCz22o5nxceB88eiiN03JDPqc5t3c9N72K/XgZs/DM351eQ0QVG3xnyWyNyTl8/A+/8An6xGrLyw7vvZtj2e9RKsXA+eYs+oApBZWciW/yMG3PqMVc6jPQ5hTJP/k8tee4YFgM9RaRIRBKBy4G3DtvmDeC0wL5zsLrvN4ZwjPBoqIWEZMqqrK6W577/HY98/UhIu8jPsH6ZS92lYQ8vVv1p8Z94btVzusM4urTO9qopv+Ej+HM/KGvtHbJ2qnQxbP5MdxQ/aFx7wYyubxdyM1JxqjQqnAK5/cBXrzWeUKbQndnEc+e09M1KqQbgFmAusAZ4SSm1SkQeEJHJgc3mAhUishr4BLhbKRX9v7pDp8Jpv6bsQC0A94/8HVf3vzqkXaQkpJCTkkNplUnyABU1FWx3b2dgTvOteG3SOtmrEp3bVKBrlfRce02hqy63BgMmJOmOxIiC3IwkxJfB3vQcuOkLa1CvRs32AYrIjcBNQLGIBJfUyQA+D+VgSqk5wJzDnrs/6HMF3Bl46FM8DoDd768lwSGMOW4EDkfo99IK0gtMSz7gm93fALRonQFtUnOgocZaSlbzLyYQWJhHzCIqoUrPhR3f6o7iB55y8z1sR3Izk2jYncbeWntUQ2xJS34icC7WaPfzgh5DlVJXRjA2fcrWQsUGyg7U0bFjGfO2zsXr94a8m+4Z3U1LHmu9+qdWPEV+ej6DOg/SHc7RZXSxWs21lbojsbh3W128Nrwfb2tpuVZitcvStp49pqu+HcnNSMZbn8SBqp3wj/Gwb4vWeFqS5I8PfPwOOABUBR6Nq97Fnzdvgvd+RVlVLYlZS/nvz/4bR0h3NiwFGQXs8uzC6wv9AiGevL/pfdbuXcstg2/BZedSm4Muh7vWQWY33ZFY3HvMHPnWyC6EnF4/FJrS7aSfWOWCjXYhNyMJ/Mkc8FZb40M0L7DVkibCDOB9oAj4hkOnwinARiW7wqShDhKS2VNeB9m7KM4qxulwhrybgowCFIodnh30yOwRgUDtz+vz8ti3j9E7u7f9Ks/Z3bBrtP+BiElDrrIedjHoMt0RGFGUm5lEQ1VfJhyfDay0BnJr1GzzVCn1mFKqD/CsUqpYKVUU9Ii/BA+B0fVJlFXVUSc76Znds1W7OaXbKTw/4XnyUtvvwKmX1r3Edvd27hh6Bw4JvTckqjwV8K/LYd1c3ZFYep4J/S/UHYXRFkpB+Xpb1BU3oiM3I5kGdz/GpY6xnrB7km+klLoxkoHYircWnyOJvdXV1Kq9rS6mkpOSw5C8ISQnJIc5wNjg8XqYuXwmI7qMYFS3UbrDaV5CIqx7D8rW6I7ESg7bvrJf6dtYULULnpkAa+c0v22k1VbC40Phm+d0R2JESW5mEkgDG6rdNID9k7yIfBb4WCUiB4IeVSJyIPIhatBQS41KQBKsAVhd0rq0elcfbPmAhTsXhiuymOIQB5efeDm/GPoLJMorfbVKYjo4k+xRia7uADx9JiydrTuS2ONKga1fQsV63ZH8MCXTDLxrNzqlJZGYtZQ/VDzEroIh4LL5FDql1KmBjxmRD8cmzvsL26szcKxcAdCm7vbHv32c4qxiTu56criiixkpCSncOCiGOoBErD/Gdmg9uwN17c0c+dAlZUJCsj3myjdeMJoKdO2G0yFkSk9OSLyWzB9fB4mZWuMxc3Oa0uc8Nq/ahbisVbNCWc72cDPGzyArKStckcWMZ1Y+Q4/MHpxx3Bm6QwlNWo49WvKNCcrUkg+diDVX3m2DJG9a8u1Sl9TuJNeeQKbmBA8t666vCuqerzrs6/jrrvf7YcMn1JZvweGyuuvb0pLvmt6VVFdquKKLCV6/lzkb5/D59pDWSrKH3H72WLjEvdv6aFryrZNmkyRfbZJ8e5Sd5qDSs5R9fzsZVryiNZaWdNe3n256sFY8++f55BXfhiRUku5KJ60N91S+2/sd72x8h+sHXm+Lq7pocDlcvHjui9T59JdZDNkFT+qOwNLYXW/mybdO9xHaBzxZcZwM50y36iIY7UZSchVLnf/HZ3vKOU/zxabprj9coP6v2+/CV3EWz0+9q0272+nZyXOrnuOsHmcxoPOAcERoax9t/YhBnQeRk5JDgsP8eLVaz/GQ8g9Ijc/1piJuwh91R2DJPdF6GO1Kx+RM8MIBh0P7xabNJy5r4K0BoKrBSWZSFr069mrT7grSCwDYfGBzWyOzvVmrZnHHJ3cwY9kM3aG03srX4O9job5abxwdi2HgJdCKRZgMGylba82TN9qVTqlWr63b4TjYcNTFJPnDBa66DjQ4cXWcz5LdS9q0ux5ZPchJyWHuZpsssBIBfuVn+uLpTP96Omf2OJO7h9+tO6TWq3fDzqX6B99tXQg7luqNIZatfhP+MkD/ffn374HXf6Y3BiPqslNTUH4XB5wu6xawRibJHy5w1bWvQahOe4tFuxa1aXcuh4sLTriABdsXsNO9MxwR2kq9r557Pr2HWatn8eMTf8z0MdNJcsZwSc3GqU66S86+/2v46AG9McS6/Vt/GMCoS3W5GXTXDmWluFC+ZCoz8yG7SGssoYyuP/wRn6PrswrgipdZ6u/LIP8Mpvad2uZdXtzrYpRSvPr9q2EI0D6q6qv4+Yc/573N7/GLob/g1yN+3ao1/m2l8Q9ytea58p491jQwo3UaZyXoTvKeCjNHvh3KTHah/Mnszhti1aDQqCVr12copTKbeGQopeJvuHhyJvQ6iy31GXRITg7L9Ldu6d0YlT+K179/nQZ/QxiC1G/ZnmVcOedKvi37lj+O/iPX9r82Nla1a07j9DnPHn0xKGUlJ5PkW69xNLtb8/fRswfSbDAl04iqrBQX+JOprNPfDg6pu15EskVkhIiMaXxEKjBtqnbD2nep933LVmZTWRee2uKX9rqUspoy5pfOD8v+dFpatpSr5lyF2+tmxvgZnFt8ru6QwietMxSMgCSNM0drK8FXb+bIt4UdWvJ1B8DvNS35digzxYXypaAqlsHbt2uNpcVznETkv4DbgQJgKXAy8CVwemRC02THEnjxClxpP2aT93P86r/DstvRBaPJTc3l5e9ejr1V4AJ2uHfQLb0bgzoP4v+N+H+cf8L5bVpDwJaSM+G/PtAbQ+NgMTNHvvWS0uHEc6FD64pLhYUzCS57ATr30ReDoUVWitVd76HBajhqFEpL/nZgOLBFKXUaMBjQ2BcWIYHR9XXiBSAjMTwtugRHAjcMuIERXUeglArLPqPp78v+zkVvXURZdRkiwk/6/CT+ErxdZOXD1e9C8VjdkcS2y2dD/4v0Hd+VDH3Og5wT9MVgaJGZ4sK7byTXVKdonycfymoltUqpWhFBRJKUUmtFpHfEItMlMLre66wn2ZEW1gVdLjvxsrDtK9Ia/A3M3zafgowCenfszcSiiSQ6E8lOztYdWuS9fLVV4OQCTfP9E9Og8FQ9x443Sllr2etQuR32rIHjToHE9rW0dXuXkZSAv+Z4RtanaU/yobTkS0WkA/AG8IGIvAnsiExYGgUWw2lw1pOaEJlxhc+tfI6Zy2faskVfVl3GjGUzOPvVs7mj5A5eXvcyAN0zu3NN/2twOVyaI4yCmv1Qvk7f8Xcut9a79sXHIE1t3rwF/q5x2NCGj+CFi/SvuWBEncMhpKd6WJrgR2meJ9/iZqpS6oLAp9NE5BMgC3g/IlHpFGjJ+5x1ZERgrXmlFOv2raPeX49CIegfkb7Ls4sPt3zIB1s+4Nuyb1EoTul2CveOvJcxBfE3trJZaTmwd6O+4696Hb54DPpdqC+GeOB0wYHt+o7vMWVm27Pk7CXcm1HBhC7nkqgxjlb1RSulYn+I+NH0ncLiugJ8a/5GZmL4p76ICA+e+iAN/gYc4mCXZxcuh4tOKdGfZjNn4xxeWPMCK8pXANAzuyc3DrqRicUT6ZHZI+rx2EZaZ73z5D1lVgwOs1ZVm6TnWd9Hn9dK+NFWXQGuNNNV3051ZATp6kQcZ16pNY5QRtc/D9yulNof+DobeEQpdW2kgtMisytb0k9CHDVkJ0emDryI4HK6aPA3cNNHN7H1wFYmHz+Zqf2mhj25+pUfpRROh5PPtn/GU8uf4snxT5LqSmX9/vX4lI/bh9zO+OPGU5hVGNZjx6zUTtbytt5aa/BUtLnLzBz5cGicK+8ph8yu0T++p9zMkW/HOiV1o66mi/ZCXaEcfWBjggdQSu0TkcERiEmvXSvI2TgfnNV0SukQ0UMlOBJ4ZOwjPL/qed5c/yavrHuFMQVjGNh5IMd3OJ4TOpxAQXpBs6vI1fnqKPeWs2T3Espqytjt2c2myk2s27eO9fvX83+n/x8ju47EgQOFYm/tXlJdqdx80s3cNuS2iJ5jTOoyAPpOsQbMaEvyZo58mwXPldeR5KvLTVd9O5ac7Kbvnj/jfmIP6Td/pS2OUJK8Q0SylVL7AESkY4jvjw1r3mbMqj8hPYrJSY38SPKirCKmnTKNWwbfwr/X/pt3N757yII5CZLALYNv4boB11FRU8GdJXdyw8AbGJU/iqVlS/nZBz+juiFQMS1oGGSHpA70yu7FhT0vpGOyVa70lPxTOCX/lIPbxPwStJHS62zroYu7DPL66zt+vMjpBUOv0bew0Vm/B6/maoaGNr7ELfyn01Yu2VuPzmlooSTpR4AvROSVwNeXAA+GPyTNfF6UJKA2/YGbpp4ZtcPmpORw6+BbuXXwrXi8Hjbu38j6/evZWrWV/jnWH/wGfwMJjgR8ygdAbmouF/W6iKzELPZt28fowaPpnNqZvNQ8MhMz42OZ2fbo2vdAzAVYm3XuBef9Rd/xc80iOO1ZmisFaqFW6Z0lE8ro+lki8jU/rHB3oVJqdWTC0sjvxScJpCQmaLuXkuZKY0DnAQzoPOCQ5/PS8nj67KcPft0tvRu/Gv4rAEr2lTAqf1RU44xb+7daU6/OeQgGXhr942cXRv+Y8crvs5YIdqVE97hKwdLZkD/UJPt2KjXwM1ej/FrjCHX4rgsOzvmKzwnTPi+bEhJROf9h436N06gMfZIyoWafniI1Vbvg87/Cvi3RP3Y8eqgYPvxt9I9b74E3b4Z1c6N/bMMW0gJJvjbQ86pLi5O8iNwOzAZygFzgBRG5NVKBaePzUu5MoCHxOzxej+5oDB2Ss8Dh0pPk93wHH9xv9SYYbZfa0ZqSGG2NPzumlny7lZFkJXn3cac0s2VkhdIffR0wUinlARCRP2EVqPm/SASmzZi7eKd0JIU1BUd0lxvthIg1jc6jYaWyxuRgptCFR3reDwV/oqlxnQUzur7dSg+UKd9zosb6CYTWXS9AcL+DD2ywXFu4ZXZjPd1JdZmBT+1aWo6eBXEaS6OaJB8eaZ31JPnGC0TTkm+3MpKsJO+u0zvDIpSW/LPAIhF5HSu5nw88E5GodFr/IdkNs1mdXo9SI80I9faq/4Xg0rBSmbsMnImQHNk1GtqN9DzY9Gn0j1ttknx7l5VsVelMX/DfMOgSbSsfhjK6/lERKQFGYSX5qUqppZEKTJtvX6CLWswiSTEJvj0b/Us9x3WXWXXkzc9eePSaYCX6aFej6zMZugyEzPzoHdOwlczAPflaB+D3aouj2SQvIlVAcLk0CXpNKaUiU6pNF5+XanGQgIaVzgx70bGs7bl/htrK6B4znvUcbz2iLTkTug6M/nEN20hLSmRU6UDOVe9prSjZ7D15pVSGUioz6JER9IivBA/gb8AjgsuRpDsSQ6cFj8KDeQerEkaNKxkyzJK2YeP3wYEdUB/l+6Jr34XlL0X3mIatpLicdKrOpXuDT2tL3pS5OpzPS42AyxHlxTMMe2msWxDtwXcf/Q7WzYvuMePZjqXwaB/YvCC6x/36Wfjyiege07CV1EQnO1L3siwp0aqEqIlJ8odRPi81IiQ6THd9u9Y49Sma0+j8fvjsz7BtUfSOGe/SA5XoGmctREt1uRl0186lJjpZlrueGV0HQGKatjiimuRFZIKIfCci60XknmNsd7GIKBEZFs34AKrPe5K1dCHJJPn2rfEPdHUUk3zNXlA+M30unNIC/5fRnkbnqTBz5Nu55EQn+7ZdT6/j/mQtyqRJ1JK8iDiBJ4BzgL7Aj0WkbxPbZQC3AVqaM57EHLwOP8kJeqY7GDahoyXf2NpsrINutJ0rGZKyop/kTUu+3Ut1OZH6TiQ1JFljQzSJZkt+BLBeKbVRKVUPvAhMaWK73wEPAbVR+aBEuAAAH2FJREFUjO0gx9LZpDmqSHGae/LtWkYXOOU2yOkZvWM2JiJTSz680jtHt7u+3mOVmE3tFL1jGraT4HQwLOtdCpedDbtW6IsjisfKB7YFfV0KjAzeQEQGA92VUu+IyF1RjO2g9KVPkZdYR8dk05pq15Iz4azfRfeYNfusj6a7PrzG/Cq63aWuVLh7Azjjs4aX0XK7O2ziOWcmEzW25EUp1fxW4TiQyCXA2Uqp/wp8fRUwQil1a+BrB/AxcLVSanNg4Z27lFJfN7GvG4AbAPLy8oa++OKLIcfjdrtJT08/4vlBX97MF9Xd2DDw/zGws55Ss61xtPOJVXY4H2dDNQ5/A97E8MwUbck5ib8BJQ4Q+4+JtcP3KNzi7ZzM+eg1bdV0shI38D+5t3Mgq+mSw605p9NOO+0bpVSLxqxFM4uVAt2Dvi4AdgR9nQH0B0oCK811Ad4SkcmHJ3ql1ExgJsCwYcPUuHHjQg6mpKSEpt5X800CvmonI4cOZmRx7HS3He18YpUtzudvP4LsIvjxv8KyO1ucUxjFzPlU74V9m6za7s0IyzntXg2rXoMRN2jvlYmZ71ELxdr5uNb+hQaBIQP7Q9HoJreJ9DlFs7mwGOgpIkUikghcDrzV+KJSqlIplaOUKlRKFQILgSMSfKTV+ht4qls5K/ZHeV6tYT+pnaI7un7RTPjkj9E7Xnvx9TPw1OnWCobRsHMpfDod6t3ROZ5hWw5x4RVpH4vhKKUagFuAucAa4CWl1CoReUBEJkcrjuZ4/V58IricZu3wdi8tJ7qj61e+AhtLone89qJx/fh9m6JzvMafGTOFrt3zOdKpcqRAhx7aYojqTWel1BxgzmHP3X+UbcdFI6bDfX7qq2x7ey3jLz9Lx+ENO0nNiV5Lvq4Ktn9jjeg3wqvHj6yPmz6F3Kbvi4ZVdblVSTApI/LHMmzNl5BJtSRCp+O1xWD/0T1RVkk6VaSSnGD+a9q9rAKrWIx7T+SPteUL8DdA8bjIH6u9yS60HtHqJanYYPUemEqC7V6iwwl4wVujLQaTyQ6T8f0fySl+kHWVK3WHYujWeyKcMz06U6E2lkBCMnQf2eymRisUj4PNn0W+GphSUL4OCk+N7HGMmJCpanFQZxUs0iR25ohFSd7ud6nr0hGHRGdqoWFjnXtZj2gQh1X7PNqlbduLk2+GYddGfmqiCNz8lbUgjtHuOR2JNChBNdSjq1/HJPnD1IgfgMwkfQUFDBtx74GtX0DfphZnDKOzH4zs/tu7aF2sgZXok2JnLrcROb0TxvCHTf+E/u1gdH1M8PupDVxupbrM2vUG1nznl34K+7ZE7hgay1C2Kxvnwxf/F9ljvHETlPwpsscwYkaaK5tibwOi2sfa9fbn91LjsP5LUk2BGgOgaKz1cdP8yB3j7TvgaTObI+LWfwgfPRC5rnRvLax81aomaBhApZTxfGYGtdFao6EJJskH83mpDYyITUpI0hyMYQude0N6F6sVGAlKWYPuTFGayCseC7562PplZPZf+hU01P5wYWi0e7sp5eFO2bi7HFFwNWpMkg+WmMY7Xa63PnUkag7GsAURKBpjzbGORJ2HvRvhQKmZOhcNx/3Imr8eqQu2jfNBnFA4KjL7N2JOr8yzqfpuGtk9ml7SNhpMkg8mQr2yBt65HKaClBFQPBY8ZdbUqHBrnLtdPC78+zYOlZhmTVGM1Hz5TfMhfwgkZ0Vm/0bMSXUm0kPtx+fepy0Gk+SD1R6guOoLUILT4dQdjWEXfc6DO1ZaXffhtrEEsrpDx+Lw79s4UtFYq2BNuBcnUQq6DID+F4d3v0ZMO9CwmcldHqB8UYQHfB6DmUIXzFvNyZ7VrHJEcbqNYX/JWZFrnfW7AE44w6yOFi2jbocxd4X//1sEzv1zePdpxDwPe/h3h0zG13vopikGk+SD+X1cWuVmt/8U3ZEYdrP5c/jmOTj/SXCG8dem/4Xh25fRvIQIjbVxl0FaZ3OxZhwi0WkN4K73R3ilxWMw3fXBAnMZxWGufYzDuHfDipdgx5Lw7XPncmudcyO6Fs6AZ84J7z5nTbHWUzCMIEkJ1tiuOo1rYZgkH0z5eTS7A+9kfaY7EsNuisZYH8M5MvuD++A/V4Vvf0bLiFirGO7bHJ79ucugbDV0Gxye/RlxI8lp9RzVRbpmwjGYJB9M+clr8NPJ30F3JIbdpOVA3oDwLYrjrYEtX1oj943oKh5nfQzXBdumTwP7Nd9L41BJgdtDlbknaYvBJPlgHYv5d+K/SEq8R3ckhh0Vj4Vti6C+uu372rYIfHVm6pwOOb0CCxyVhGd/G0usgZld9f0hN+wpOZDkD6Qfpy0Gk+QP421QJDrNf4vRhOLTIKc3VO1s+742zgdHAvQwgzyjTsS6uNo0H/z+tu9v03woHA1m2q1xmOTAPfkGz25tMZgRZsEO7CAz5W42+vKAt3RHY9hNz/HWIxw2lkD+MEjKCM/+jND0nWJVivN62vY9UArOecgsgGM0qbEln7plLpx6rZYYTJIPVr2XVNmLR5lfWOMY/L62t9p+8gp49hz1Za/XS2lpKbW1+gpbNCcrK4s1a9boDuOYkpOTKSgowOU6bAXLEydaj7YSgd5hHqlvxI2UQA2Ueo1V6EySD6b81IuQIOa/xTiKZf+BOXfBHcshJbv1+0nrZD2OorS0lIyMDAoLCxGbzr2uqqoiI8O+PRFKKSoqKigtLaWoqOjIDfw+a4R9p+Nbf5DVb0GH46CbuR9vHKlrehfe3rwfio7+ux5p5uZzMOXDi0nyxjF06A51B2BzG6ZZLv4HfPXUMTepra2lU6dOtk3wsUBE6NSp09F7Qz64H2acCg11rTuA3w/v/AIWPtn6II24lpTgIs0vOCJR3KqFTJIPZlryRnPyh4ErtW3TrxY+Cd/Pa3Yzk+Db7pj/h8f9CLzVULq4dTsvWw3V5WbqnHFUCi8zOiazzG8K1NiDOKgVB04xFeiMo0hItEbEt3a+fGUpVKyPyalz06ZN4+GHH9YdRvgUngriaP0FW+PPgKkfbxxFYoLwSmYqX3Vowy2hNjJJPojqehLbyaYuuavuUAw7KxprlZ09sCP09zYmlOJx4YzIaI2UDtBtSOvny2+cD51OgKz8sIZlxI/0xFQqv3uQPt1u0haDSfJBvD4F4iPR1JI3jqXXBBjzK5AQR9grP6x5yypkkts3MrGF0axZsxg4cCCDBg3iqqsOXX53w4YNXHDBBQwdOpTRo0ezdu1aAN5++21GjhzJ4MGDGT9+PLt3W/ODp02bxrXXXsu4ceMoLi7msccei/r5NKl4HOxeFfp9eb8ftn9jWvHGMSUmOBgoG0itXK8tBnPzOUjDrlWkOKpJ94VhRTMjfnXuBaffa82RDpUjAYZeE1K1st++vYrVOw6Efqxj6Nstk/85r99RX1+1ahUPPvggn3/+OTk5Oezdu/eQxHzDDTfw8MMPM3jwYBYtWsRNN93Exx9/zKmnnsrChQsREf7xj3/w0EMP8cgjjwCwdu1aPvnkE6qqqujduzc33njjkVPbom3AxZDbBwJTnVrM4YBfrIJ6T2TiMuKCyykclz+Dys09gNO1xGCSfBDfgd1M8FSRkpGjOxQjFnz2KFRshLN+B6kdj75d+XoriYgDLn4WnPbvKfr444+5+OKLycmxfhc6dvzh/NxuN1988QVTp07F4bA6A+vqrJZwaWkpl112GTt37qS+vv6QqWuTJk0iKSmJpKQkcnNz2b17NwUFBVE8qybk9rEeAEv/TbftS4FxR99eKVj2b+jcG/KHgis5GlEaMcrldLAg3UGOt0ZbDCbJB/H7ffy+fC/vF5s5r0YL+Lyw/EVY9x6c9SAMuvzIFvq6efDqdVAwDLrf3qp65sdqcUeKUuqoI9P9fj8dOnTg888/P2Ke/K233sqdd97J5MmTKSkpYdq0aQdfS0r6obXsdDppaNBXmesISsHad+j1/Tvwdi2cM/3I71X599aUuc0LYMhPrSRvGMfgdAgOwK/CsHxyK5l78kH8vsZ68mYNaqMFxt0DP/sUOh4Pb/wcZk3+oT68UrDgEfjXpZBdCOf9VWuooTrjjDN46aWXqKioAGDv3r0HX8vMzKSoqIjXX38dsC4Ili1bBkBlZSX5+dZAtOeffz7KUbeBCFw6iy3HXQzfPAfPnwtVu6zXGuqg5H/hyVNg53I4989wbmx9Pw09nCI4FPgxSd4Wqrwehvco4POaRbpDMWJFXj+4dq71h3/XCqjdb92nfflq+OgB6H+R9XoHfVWoWqNfv37ce++9jB07lkGDBnHnnXce8vrs2bOZNWsWgwYNol+/frz55puANcDukksuYfTo0Qe7+mOGw8mm4qusWyq7VsDM06BmPyyZBSV/hD7nwS2LYdi11j15w2iGwyE4EPzoWwzHdNcHcyYz8YCPvLzuuiMxYonDYf3hH3gZJKZBXZXVoj/zd3DKrSENsrOTqVOnMnXq1CZfa2zJH95dP2XKFKZMmXLE9sHd9gArV64MW5xh1/9CyOkJGz6xptkNmWrdgy8aozsyIwbVKxfb0/poO75J8kH83U/n+Z2P8OjoQbpDMWJRYpr1MSkDrv+4VfffDZvoMsB6gPV9NAneaCUfLqpcHbQd3/Q5BfH6feCoBdFXMciIEybBG4YBJOAnpa5c2/FNkg9Suek9MnpPY82uV3WHYhiGYcSBdFVLXvVqbcc3ST6ICtT3TsS05A3DMIy2S1aCU9+4O3NPPliD35q363Ca/xbDMAyj7R4qTaVDmr5Fk0xLPojP7wUwpWYNwzCMsPCLA8w8eXvw+a1ueqdpyRtGkyZOnMj+/fubfC09Pb1N+467UraGAfwnu453k/Uta2uSfJB6h9Wl4nSGWKzCMNqJOXPm0KHDodOBlFL4/S1rqdhqKVvDiIIvkrrwWeZAbcePapIXkQki8p2IrBeRe5p4/U4RWS0iy0XkIxHpEc349uaNsD5JibGVugwjAs4//3yGDh1Kv379mDlzJgCFhYWUl5ezefNmhg0bxk033cSQIUPYtm0bAL/85S8ZMmQIZ5xxBnv2WANZx40bx29+8xvGjh3LX//616OWow321FNPcc4551BTU8OGDRuYMGHCEWVtX375Zfr378+gQYMYM8bMYzfsyVl5F12TbtF2/Kj1S4uIE3gCOBMoBRaLyFtKqeC5Bd8Cw5RS1SJyI/AQcFm0YmxQVisjwWG66w2beXbSkc/1Ox9GXA/11TD7kiNfP+kKGPwT8FTASz899LVr3m32kM888wwdO3akpqaG4cOHc9FFFx3y+vfff8/zzz/P3/72NwA8Hg9DhgzhkUce4YEHHuC3v/0tjz/+OAD79+9n/vz5AOzbt++o5WgBHn/8cebNm8cbb7xBUlISN9xwAzNmzKBnz56HlLV94IEHmDt3Lvn5+Ue9hWAYug1Wq+npTgAGazl+NLPZCGC9UmojgIi8CEwBDiZ5pdQnQdsvBK6MYnyk7vgCgCRfbTQPaxi29Nhjjx0sQrNt2za+//77Q14/7rjjOPnkkw9+7XA4uOwy65r8yiuv5MILLzz4WuPzcOxytP/85z8pKCjgjTfewOVyHSxre8klP1zENJa1HTVqFFdffTWXXnrpIccyDDtpSHue8tp64Gdajh/NJJ8PbAv6uhQYeYztrwPea+oFEbkBuAEgLy+PkpKSkINxu91HvM+3bQtX1x5g/+YKSmpC36dOTZ1PLIu384HQzikrK4uqqqofnrj4xaY3bNzmmK8nHvl68L6bsGDBAubOncu8efNITU1l4sSJ7N27F6UUbrcbt9tNSkrKoTECVVVVJCQk4Ha7UUpRVVWFL1DdsXHbm266iVtuuYWJEyeyYMEC/vjHP1JVVUVdXR29e/dmxYoVrF27lsLCQg4cOEBWVhYLFiw44jjTp09n8eLFzJ07l0GDBvHZZ5/RqVOnI86ltra2xf/v8fZzZ85Hvy2JXtx+31HjjvQ5RTPJN1Wlo8klAkTkSmAYMLap15VSM4GZAMOGDVPjxo0LOZiSkhIOf9+6A4u5eMl+Vk4cT/+eRU2/0aaaOp9YFm/nA6Gd05o1a44o/hJNXq+XnJwc8vLyWLt2LYsXLyY1NRUROTiKXkQOidHv9zN37lwuv/xy3nrrLcaMGUNGRgZOp5O0tLSD27rdbk444QQyMjJ4+eWXcTqdZGRkkJSUxIgRI7jtttu44oorDnbFFxcX8/7773PJJZeglGL58uUMGjSIDRs2cPrpp3P66aczb9489u/fT2Fh4RHnkpyczODBLesqjbefO3M++j26TlDCUeOO9DlFc+BdKRBc3q0A2HH4RiIyHrgXmKyUqotSbADUN3hxi5g5B0a7N2HCBBoaGhg4cCD33XffId3yR5OWlsaqVasYOnQoH3/8Mffff3+T2zVXjvbUU0/l4YcfZtKkSZSXlzN79myefvrpI8ra3n333QwYMID+/fszZswYBg0yhaUM+3EgGgvNRrclvxjoKSJFwHbgcuCK4A1EZDDwd2CCUqosirEBsKJhOz8u7M6Dno30J7Za8oYRTklJSbz33pF3yzZv3gxATk4OixYtOuQ1t9sNwO9+97tDnj+8K7Il5WjPPvtszj777IPHev/994/Y/rXXXmv2PAxDN9315KPWZlVKNQC3AHOBNcBLSqlVIvKAiEwObDYdSAdeFpGlIvJWtOID6JRUyI/LneSld4vmYQ3DMIw4VebsyjbXcdqOH9W5YkqpOcCcw567P+jz8dGM53A1Pa5g5pd9uTS9e/MbG4ZhGEYzvJKCknptxzd3n4NUN7hxJJbhV17doRiGYRhxQHCgNFY2NUk+yHcHvibt+EcpqzliPKBhGIZhhMwhDpTSV6DGLO0WpLHUrCvBqTkSwzAMIx4k0hGf0teeNkk+iC9QZCPRLGtrGIZhhEEhP2VvjbknbwsH1653mpa8YTz22GP06dOH/Px8brlFX4ENw4hlCQ7B59c3hc40WYM0Lr+Z6HRpjsQw9Pvb3/7Ge++9x/z58/n66691h2MYMWkHb1OeXAqM1nJ805IP0qCsJO9ymJa80b79/Oc/Z+PGjUyePJl9+/YdfD64TOzkyZMPlomdNm0aV111Faeffjo9e/bkqaeeAmDnzp2MGTOGk046if79+x+xBr1hxLtU6UJCg75p2aYlH8QfGAHpMt31hs1c8/41zW4ztmAsV/e/+uD2U06YwvknnM++2n3cWXLnIds+O+HZY+5rxowZvP/++3zyySe88847B58/9dRTD5aJffzxxw8pE7t8+XIWLlyIx+Nh8ODBTJo0iX//+9+cffbZ3Hvvvfh8Pqqrq0M8c8OIbXkJJ7O35oC245skH8QXGF1vuusNo2nBZWJra2s5/vjjD742ZcoUUlJSSElJ4bTTTuOrr75i+PDhXHvttXi9Xs4//3xOOukkjdEbRvQlOAS/uSdvD327ZfDZPkh1mSRv2EtzLe9jbZ+dnB3y+4/m1ltv5c4772Ty5MnMmTOHhx566OBrIocWmhQRxowZw6effsq7777LVVddxd13381Pf/rTsMRiGLHgtjN6Ul1nFsOxhdMLf8Rdw+4i2ZWkOxTDsKXKykry8/MB+Ne//nXIa2+++Sa1tbVUVFRQUlLC8OHD2bJlC7m5uVx//fVcd911LFmyREfYhqHN8Z3TGVCQpe34piUfpF9OP/rl9NMdhmHYVmOZ2Pz8fIYMGUJpaenB10aMGMGkSZPYunUr9913H926deP5559n+vTpuFwu0tPTmTVrlsboDaP9MUneMIwmNZaVvfrqq7n66quBQ8vEVlVVkZGRcXD7Xr16MXPmzEP2MXXqVKZOnRqVeA3DOJLprjcMwzCMOGVa8oZhtNm0adN0h2AYRhNMS94wDMMw4pRJ8oZhU0rpm1sbL8z/odHemSRvGDaUnJxMRUWFSVJtoJSioqKC5ORk3aEYhjbmnrxh2FBBQQGlpaXs2bNHdyhHVVtba/sEmpycTEFBge4wDEMbk+QNw4ZcLhdFRUW6wzimkpISBg8erDsMwzCOwXTXG4ZhGEacMkneMAzDMOKUSfKGYRiGEack1kfvisgeYEsr3poDlIc5HJ3M+dhfvJ1TvJ0PxN85mfOxv9acUw+lVOeWbBjzSb61RORrpdQw3XGEizkf+4u3c4q384H4OydzPvYX6XMy3fWGYRiGEadMkjcMwzCMONWek/zM5jeJKeZ87C/ezinezgfi75zM+dhfRM+p3d6TNwzDMIx4155b8oZhGIYR19ptkheRk0RkoYgsFZGvRWSE7pjCQURuFZHvRGSViDykO55wEJG7RESJSI7uWNpKRKaLyFoRWS4ir4tIB90xtYaITAj8nK0XkXt0x9MWItJdRD4RkTWB35vbdccUDiLiFJFvReQd3bGEg4h0EJFXAr8/a0TkR7pjagsR+UXg522liPxbRCJSCKLdJnngIeC3SqmTgPsDX8c0ETkNmAIMVEr1Ax7WHFKbiUh34Exgq+5YwuQDoL9SaiCwDvi15nhCJiJO4AngHKAv8GMR6as3qjZpAH6plOoDnAzcHOPn0+h2YI3uIMLor8D7SqkTgUHE8LmJSD5wGzBMKdUfcAKXR+JY7TnJKyAz8HkWsENjLOFyI/C/Sqk6AKVUmeZ4wuHPwK+wvl8xTyk1TynVEPhyIRCLJdJGAOuVUhuVUvXAi1gXlzFJKbVTKbUk8HkVVvLI1xtV24hIATAJ+IfuWMJBRDKBMcDTAEqpeqXUfr1RtVkCkCIiCUAqEcpB7TnJ3wFMF5FtWC3emGtRNaEXMFpEFonIfBEZrjugthCRycB2pdQy3bFEyLXAe7qDaIV8YFvQ16XEeFJsJCKFwGBgkd5I2uwvWBfHft2BhEkxsAd4NnAL4h8ikqY7qNZSSm3HyjtbgZ1ApVJqXiSOFdelZkXkQ6BLEy/dC5wB/EIp9aqIXIp1hTg+mvG1RjPnlABkY3U5DgdeEpFiZeMpFM2cz2+As6IbUdsd65yUUm8GtrkXq5t4djRjCxNp4jnb/oy1lIikA68CdyilDuiOp7VE5FygTCn1jYiM0x1PmCQAQ4BblVKLROSvwD3AfXrDah0Rycbq/SoC9gMvi8iVSqkXwn2suE7ySqmjJm0RmYV1zwrgZWKkW6uZc7oReC2Q1L8SET/Wush7ohVfqI52PiIyAOsXYJmIgNWtvURERiildkUxxJAd63sEICJTgXOBM+x8AXYMpUD3oK8LiPHbXSLiwkrws5VSr+mOp41GAZNFZCKQDGSKyAtKqSs1x9UWpUCpUqqxh+UVrCQfq8YDm5RSewBE5DXgFCDsSb49d9fvAMYGPj8d+F5jLOHyBta5ICK9gERitJiDUmqFUipXKVWolCrE+iUfYvcE3xwRmQD8P2CyUqpadzyttBjoKSJFIpKINWDoLc0xtZpYV5FPA2uUUo/qjqetlFK/VkoVBH5vLgc+jvEET+D3fpuI9A48dQawWmNIbbUVOFlEUgM/f2cQoYGEcd2Sb8b1wF8Dgx5qgRs0xxMOzwDPiMhKoB6YGqMtxXj2OJAEfBDooViolPq53pBCo5RqEJFbgLlYo4KfUUqt0hxWW4wCrgJWiMjSwHO/UUrN0RiTcaRbgdmBC8uNwDWa42m1wC2HV4AlWLftviVCK9+ZFe8MwzAMI0615+56wzAMw4hrJskbhmEYRpwySd4wDMMw4pRJ8oZhGIYRp0ySNwzDMIw4ZZK8YcQBEZkTixXtROQL3TEYRjwzU+gMwzAMI06Zlrxh2JyI/EpEbgt8/mcR+Tjw+Rki8kLg880ikiMihYFa208FalXPE5GUwDbDA3XsvwzUtV/ZxLG6isinIrI0UOd6dOB5t4g8IiJLROQjEekceP56EVksIstE5FURSQ08/5yIPCYiX4jIRhG5+Cjn5g58HCciJUH1wmcHVgI7fPuSwP/Bp4HzHC4ir4nI9yLy+3D8fxtGPDFJ3jDs71NgdODzYUB6YK31U4EFTWzfE3hCKdUPq/jFRYHnnwV+rpT6EeA7yrGuAOYqpU7CqtnduAJcGrBEKTUEmA/8T+D515RSw5VSjfW9rwvaV9dAjOcC/9uC8xyMVR2yL1bVsVFH2a5eKTUGmAG8CdwM9AeuFpFOLTiOYbQbJskbhv19AwwVkQygDvgSK9mPpukkv0kptTTovYWB+/UZSqnGe+D/OsqxFgPXiMg0YECgvjpYJUv/E/j8BazkDdBfRBaIyArgJ0C/oH29oZTyK6VWA3ktOM+vlFKlSik/1sVF4VG2a1wnfwWwKlAPvg5rqdPuR3mPYbRLJskbhs0ppbzAZqy1ur/ASuynAcfTdFGLuqDPfVg1KpoqD9vUsT4FxgDbgX+KyE+Ptmng43PALUqpAcBvsaqeNRVHS47fVNzH2s5/2Hv8x3iPYbRLJskbRmz4FLgr8HEB8HNgaUsLECml9gFVInJy4KnLm9pORHpg1SJ/Cqsy25DASw6g8b76FcBngc8zgJ2B2wc/CemMDMOIOHPVaxixYQFwL/ClUsojIrU03VV/LNcBT4mIBygBKpvYZhxwt4h4ATfQ2JL3AP1E5JvA+y4LPH8fsAjYgtV9nhFiTIZhRJCZQmcY7YSIpCulGkez3wN0VUrd3sL3upVS6REN0DCMsDMtecNoPyaJyK+xfu+3AFfrDccwjEgzLXnDMAzDiFNm4J1hGIZhxCmT5A3DMAwjTpkkbxiGYRhxyiR5wzAMw4hTJskbhmEYRpwySd4wDMMw4tT/B74JcyF4qa9LAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "liftana = aero.LiftAnalysis.generate(wing)\n", + "\n", + "span_pos = liftana.ys\n", + "\n", + "α, distribution, C_Dib = liftana.calculate(C_L=0.8)\n", + "α_qr, distribution_q, C_Dia = liftana.calculate(C_L=0.8, \n", + " controls={'flap2': [5, -5]})\n", + "α_ab, distribution_ab, C_Di = liftana.calculate(C_L=0.8, airbrake=True)\n", + "\n", + "plt.figure(figsize=(8,5))\n", + "plt.plot(span_pos, distribution, label='clean')\n", + "plt.plot(span_pos, distribution_ab, '--', label='airbrakes')\n", + "plt.plot(span_pos, distribution_q, '-.', label='flaps')\n", + "plt.xlabel('wing span in m')\n", + "plt.ylabel('local lift coefficient $c_l$')\n", + "plt.title('Lift distribution for $C_L = 0,8$')\n", + "plt.grid()\n", + "plt.legend()\n", + "plt.savefig('Liftdistribution.png')\n", + "plt.savefig('Liftdistribution.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'c_ls': array([0.17526834, 0.3342843 , 0.47155578, 0.58651597, 0.68078389,\n", + " 0.75697514, 0.81793964, 0.86639283, 0.90472905, 0.93496854,\n", + " 0.95876624, 0.97745008, 0.99207443, 1.00346589, 1.0122734 ,\n", + " 1.01899972, 1.02403923, 1.02769404, 1.03020185, 1.03173995,\n", + " 1.03244711, 1.03241758, 1.03171982, 1.03037879, 1.02838935,\n", + " 1.02566923, 1.02200214, 1.01913148, 1.02090594, 1.02139576,\n", + " 1.02110045, 1.02017507, 1.01872381, 1.01677718, 1.01435829,\n", + " 1.01144383, 1.00801175, 1.00398572, 0.99929012, 0.99375449,\n", + " 0.98719265, 0.97917582, 0.96914853, 0.95520178, 0.96914853,\n", + " 0.97917582, 0.98719265, 0.99375449, 0.99929012, 1.00398572,\n", + " 1.00801175, 1.01144383, 1.01435829, 1.01677718, 1.01872381,\n", + " 1.02017507, 1.02110045, 1.02139576, 1.02090594, 1.01913148,\n", + " 1.02200214, 1.02566923, 1.02838935, 1.03037879, 1.03171982,\n", + " 1.03241758, 1.03244711, 1.03173995, 1.03020185, 1.02769404,\n", + " 1.02403923, 1.01899972, 1.0122734 , 1.00346589, 0.99207443,\n", + " 0.97745008, 0.95876624, 0.93496854, 0.90472905, 0.86639283,\n", + " 0.81793964, 0.75697514, 0.68078389, 0.58651597, 0.47155578,\n", + " 0.3342843 , 0.17526834]),\n", + " 'a_is': array([0.14662398, 0.1213158 , 0.09946837, 0.08117189, 0.06616868,\n", + " 0.05404247, 0.04433967, 0.0366281 , 0.0305267 , 0.02571394,\n", + " 0.02192642, 0.01895279, 0.01662525, 0.01481225, 0.01341049,\n", + " 0.01233996, 0.0115379 , 0.01095622, 0.01055709, 0.01031229,\n", + " 0.01019974, 0.01020444, 0.01031549, 0.01052893, 0.01084555,\n", + " 0.01127848, 0.01186211, 0.01231899, 0.01203658, 0.01195862,\n", + " 0.01200562, 0.0121529 , 0.01238387, 0.01269369, 0.01307867,\n", + " 0.01354252, 0.01408875, 0.01472951, 0.01547684, 0.01635786,\n", + " 0.01740221, 0.01867813, 0.02027402, 0.02249372, 0.02027402,\n", + " 0.01867813, 0.01740221, 0.01635786, 0.01547684, 0.01472951,\n", + " 0.01408875, 0.01354252, 0.01307867, 0.01269369, 0.01238387,\n", + " 0.0121529 , 0.01200562, 0.01195862, 0.01203658, 0.01231899,\n", + " 0.01186211, 0.01127848, 0.01084555, 0.01052893, 0.01031549,\n", + " 0.01020444, 0.01019974, 0.01031229, 0.01055709, 0.01095622,\n", + " 0.0115379 , 0.01233996, 0.01341049, 0.01481225, 0.01662525,\n", + " 0.01895279, 0.02192642, 0.02571394, 0.0305267 , 0.0366281 ,\n", + " 0.04433967, 0.05404247, 0.06616868, 0.08117189, 0.09946837,\n", + " 0.1213158 , 0.14662398]),\n", + " 'C_L': 1.0,\n", + " 'alpha': 0.17451880288157148,\n", + " 'C_Mx': -1.3595426780354487e-16}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aero.calculate(wing, C_L=1.0, calc_cmx=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "aero" + ] + } + ], + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/Analysis_Example.ipynb b/examples/Analysis_Example.ipynb index e4396fd..f8ced68 100644 --- a/examples/Analysis_Example.ipynb +++ b/examples/Analysis_Example.ipynb @@ -14,7 +14,7 @@ "outputs": [], "source": [ "from matplotlib import pyplot as plt\n", - "from wingstructure import data, analysis\n", + "from wingstructure import data, aero\n", "import numpy as np" ] }, @@ -75,20 +75,23 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "/home/jonathan/Programmieren/wingstructure/wingstructure/analysis.py:33: UserWarning: No airfoil database defined, using default airfoil.\n", - " warn('No airfoil database defined, using default airfoil.')\n" + "0.01871572770370315\n", + "0.01871572770370315\n", + "dict_items([])\n", + "dict_items([('flap2', [5, -5])])\n", + "dict_items([])\n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -98,14 +101,14 @@ } ], "source": [ - "liftana = analysis.LiftAnalysis(wing)\n", + "liftana = aero.LiftAnalysis.generate(wing)\n", "\n", - "span_pos = liftana.calc_ys\n", + "span_pos = liftana.ys\n", "\n", - "α, distribution, C_Dib = liftana.calculate(C_L=0.8, return_C_Di=True)\n", + "α, distribution, C_Dib = liftana.calculate(C_L=0.8)\n", "α_qr, distribution_q, C_Dia = liftana.calculate(C_L=0.8, \n", - " flap_deflections={'flap2': [5, -5]}, return_C_Di=True)\n", - "α_ab, distribution_ab, C_Di = liftana.calculate(C_L=0.8, air_brake=True, return_C_Di=True)\n", + " controls={'flap2': [5, -5]})\n", + "α_ab, distribution_ab, C_Di = liftana.calculate(C_L=0.8, airbrake=True)\n", "\n", "plt.figure(figsize=(8,5))\n", "plt.plot(span_pos, distribution, label='clean')\n", @@ -120,6 +123,65 @@ "plt.savefig('Liftdistribution.pdf')" ] }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from wingstructure.aero import multhop" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0935781315507477\n", + "-0.28072172161146763\n" + ] + } + ], + "source": [ + "res = multhop.calc_multhopalpha(wing, 0.8, controls={'flap2': [5, -15]})" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,10))\n", + "plt.plot(res['ys'], res['c_ls'])\n", + "plt.plot(liftana.ys, distribution_q, '+')" + ] + }, { "cell_type": "code", "execution_count": null, @@ -144,7 +206,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/examples/Liftdistribution.pdf b/examples/Liftdistribution.pdf index 6b5d698..26cc1f6 100644 Binary files a/examples/Liftdistribution.pdf and b/examples/Liftdistribution.pdf differ diff --git a/examples/Liftdistribution.png b/examples/Liftdistribution.png index 673ace5..868f671 100644 Binary files a/examples/Liftdistribution.png and b/examples/Liftdistribution.png differ diff --git a/tests/test_liftingline.py b/tests/test_liftingline.py deleted file mode 100644 index e7f36d7..0000000 --- a/tests/test_liftingline.py +++ /dev/null @@ -1,86 +0,0 @@ -import numpy as np -from wingstructure.aero.liftingline import multhopp - -def test_multhopp_schlichting(): - Λ = 6 # aspect ratio of wing - - b = 15 # m span width - cs = np.array([b/Λ]*2) # depth of wing - ys = np.array([0,b/2]) # section positions - - αs = np.array([1]*2) # angle of attack - dcls = np.array([2*np.pi]*2) - - # reference result - ηs_ref = np.array([0,0.3827,0.7071,0.9239,1]) - γs_ref = np.array([0.4320,0.4192,0.3710,0.2485,0]) - - # coarse calculation - M = 7 - - res = multhopp(αs, cs, ys, dcls, M = M, mode='gamma') - - assert np.isclose(res[0][M//2:]/b*2, ηs_ref[:-1], atol=1e-4).all() - assert np.isclose(res[1][M//2:], γs_ref[:-1], atol=1e-4).all() - -def test_multhopp_dcls_nan(): - Λ = 6 # aspect ratio of wing - - b = 15 # m span width - cs = np.array([b/Λ]*2) # depth of wing - ys = np.array([0,b/2]) # section positions - - αs = np.array([1]*2) # angle of attack - dcls = np.nan - - # reference result - ηs_ref = np.array([0,0.3827,0.7071,0.9239,1]) - γs_ref = np.array([0.4320,0.4192,0.3710,0.2485,0]) - - # coarse calculation - M = 7 - - res = multhopp(αs, cs, ys, dcls, M = M, mode='gamma') - - assert np.isclose(res[0][M//2:]/b*2, ηs_ref[:-1], atol=1e-4).all() - assert np.isclose(res[1][M//2:], γs_ref[:-1], atol=1e-4).all() - -def test_multhopp_coefficients(): - - Λ = 6 # aspect ratio of wing - - b = 15 # m span width - cs = [b/Λ]*2 # depth of wing - ys = [0,b/2] # section positions - - αs = [np.radians(1)]*2 # angle of attack - dcls = [2*np.pi]*2 - - res = multhopp(αs, cs, ys, dcls, M = 91) - - A = b * cs[0] - - C_L = np.trapz(res.c_ls, res.ys)/b - - # ensure lift coefficient matches local lift coefficents - assert np.isclose(C_L, res.C_L, rtol=1e-3) - -def test_multhopp_coefficients2(): - - Λ = 6 # aspect ratio of wing - - b = 15 # m span width - cs = [b/Λ]*3 # depth of wing - ys = [-b/2, 0, b/2] # section positions - - αs = [np.radians(1)]*3 # angle of attack - dcls = [2*np.pi]*3 - - res = multhopp(αs, cs, ys, dcls, M = 91) - - A = b * cs[0] - - C_L = np.trapz(res.c_ls, res.ys)/b - - # ensure lift coefficient matches local lift coefficents - assert np.isclose(C_L, res.C_L, rtol=1e-3) diff --git a/tests/test_multhop.py b/tests/test_multhop.py new file mode 100644 index 0000000..86fc876 --- /dev/null +++ b/tests/test_multhop.py @@ -0,0 +1,38 @@ +import numpy as np + +from wingstructure.aero import multhop + + +# test low level functions + +def test_multhop_solve(): + """test multhop_solve with numeric data from schlichting book + """ + + + Λ = 6 # aspect ratio of wing + + b = 15 # m span width + S = b**2/Λ + + cs = np.array([b/Λ]*2) # depth of wing + ys = np.array([0,b/2]) # section positions + + αs = np.array([1]*2) # angle of attack + dcls = np.array([2*np.pi]*2) + + # reference result from Schlichting + ηs_ref = np.array([0,0.3827,0.7071,0.9239,1]) + γs_ref = np.array([0.4320,0.4192,0.3710,0.2485,0]) + + # coarse calculation + M = 7 + + solverinput = multhop._prepare_multhop(ys, αs, cs, dcls, S, b, M) + + calc_ys = solverinput[0] + + γs = multhop._multhop_solve(*solverinput[1:], b) + + assert np.isclose(calc_ys[M//2:]/b*2, ηs_ref[:-1], atol=1e-4).all() + assert np.isclose(γs[M//2:], γs_ref[:-1], atol=1e-4).all() diff --git a/wingstructure/aero/__init__.py b/wingstructure/aero/__init__.py index e973423..cc02b45 100644 --- a/wingstructure/aero/__init__.py +++ b/wingstructure/aero/__init__.py @@ -1,3 +1,3 @@ from . import analysis, liftingline, nnliftingline, polarinterp -from .analysis import AirfoilData, LiftAnalysis, LiftAndMomentAnalysis \ No newline at end of file +from .liftingline import AirfoilData, LiftAnalysis, calculate \ No newline at end of file diff --git a/wingstructure/aero/analysis.py b/wingstructure/aero/analysis.py index 15fad78..08dbf6e 100644 --- a/wingstructure/aero/analysis.py +++ b/wingstructure/aero/analysis.py @@ -79,6 +79,7 @@ def get_α0(airfoil, airfoil_db): self.airbrake_distribution, self.airbrake_lift, self.airbrake_drag = multhopp_(α_ab) ## lift due to angle of attack + #print('\n\naoa') α_aoa = np.ones(M) self.aoa_c_ls, aoa_C_L, self.aoa_C_Di = multhopp_(α_aoa) self.aoa_c_ls /= aoa_C_L @@ -90,7 +91,7 @@ def _calculate_flap_α(self, flap, angle=np.radians(1)): for ii, span_pos in enumerate(self.calc_ys): - if flap.chordpos_at(span_pos) >= 0.0 and span_pos > 0: + if flap.length(span_pos) > 0.0 and span_pos > 0: lambda_k = 1-flap.chordpos_at(span_pos) diff --git a/wingstructure/aero/liftingline.py b/wingstructure/aero/liftingline.py index 488c384..85f0c5d 100644 --- a/wingstructure/aero/liftingline.py +++ b/wingstructure/aero/liftingline.py @@ -1,178 +1,125 @@ # -*- coding: utf-8 -*- """ -Module providing the multhop quadrature method for solving prandtl's lifting line problem. +Module providing objects for calculation of prandtl's lifting line problem. """ import numpy as np -#from numba import jit -from collections import namedtuple +from collections import namedtuple, defaultdict +from .multhop import _calc_gridpoints, Multhop, AirfoilData, _calc_eta_eff π = np.pi +_calculator_dict = { + 'multhop': Multhop, +} -_multhopp_result = namedtuple('multhopp_result', ('ys','c_ls','C_L')) -_ext_multhopp_result = namedtuple('ext_multhopp_result', ('ys', 'c_ls', 'C_L', 'αᵢs','C_Wi')) +class LiftAnalysis: + @classmethod + def generate(cls, wing, airfoil_db=defaultdict(AirfoilData), M=None, method='multhop'): + + ys = _calc_gridpoints(wing, M) + + try: + calculator = _calculator_dict[method](wing, ys, airfoil_db) + except: + raise Exception('{} is not implemented yet!'.format(method)) + + analysis = cls() + + analysis.ys = ys + analysis._base = calculator.baselift() + analysis._airbrake = calculator.airbrakelift() + analysis._aoa = calculator.aoa(1) + analysis._control_surfaces = {name : calculator._controlsurfacelift_n(name) \ + for name in wing.controls.keys()} + analysis.chords = calculator.chords + + return analysis + + def __init__(self): + self.ys = None + self.chords = None + self._base = None + self._airbrake = None + self._aoa = None + self._control_surfaces = {} + + def calculate(self, C_L, controls:dict={}, airbrake:bool=False, airfoil_db:dict=None, options:dict=None): + + c_ls = np.zeros_like(self.ys) + C_Di = 0.0 -#@jit -def _solve_multhopp(αs, θs, chords, b, dcls, return_B = False): - - M = len(θs) - - # create empyt matrix (N_MxN_M) for multhoppcoefficients - Bb = np.zeros((M, M)) - Bd = np.zeros(M) - - # calculation of multhopp coefficients - for v, (θv, c, dcl_v) in enumerate(zip(θs, chords, dcls)): - for n, θn in enumerate(θs): - - # diagonal elements - if v == n: - # prevent division throught zero - if np.isclose(np.sin(θv), 0.0): - θv = 1e-15 - - Bb[v, v] = (M+1)/(4*np.sin(θv)) - Bd[v] = 2*b/(dcl_v*c) - # non diagonal elements - else: - Bb[v, n] = - ((1-(-1.)**(v-n))/2*(np.sin(θn)/ \ - ((M+1)*(np.cos(θn)-np.cos(θv))**2))) - - B = Bb + np.diag(Bd) - - #print(B) - - #print(B.shape, αs.shape) - # calculation of local circulation - γs = np.dot(np.linalg.inv(B), αs) - - #print(γs) - if return_B: - return γs, Bb, Bd - else: - return γs + # collect contributions to lift distribution + contributions = [self._base] + if airbrake: contributions.append(self._airbrake) + + print(controls.items()) + contributions += [self._calc_controlsurface(*cs) for cs in controls.items()] -def _calculate_liftcoefficients(Θs, γs, chords, Λ, b, M): - """Calculates lift coefficients from circulation - - Parameters - ---------- - chords : np.ndarray - chord lengthes - b : float - span width - M : int - number of grid points + for contrib in contributions: + C_Di += contrib.C_Di + c_ls += contrib.c_ls + C_L -= contrib.C_L - Returns - ------- - tuple - c_l (ndarray) - local lift coefficents, C_L (float) - lift coefficient wing - """ + # choose angle of attack to reach C_L + α = C_L / self._aoa.C_L + c_ls += α * self._aoa.c_ls + C_Di += abs(α) * self._aoa.C_Di - # calculate lift coefficient distritbution - c_l = 2*b/(np.array(chords)) * np.array(γs) # TODO: Check this formula + return np.rad2deg(α), c_ls, C_Di - # calculate overall lift coefficient (whole wing) - C_L = π*Λ / (M+1) * np.sum(γs * np.sin(Θs)) + def _calc_controlsurface(self, name, deflections): + # control surface lift distribution is proportional + # to effective deflection not to deflection itself + fac = lambda η: _calc_eta_eff(np.radians(η)) + controllift = self._control_surfaces[name] + return controllift * fac(deflections[0]) \ + + controllift.flip() * fac(deflections[1]) - return c_l, C_L +def calculate(wing, alpha=None, C_L=None, controls={}, airbrake=False, M=None, method='multhop', airfoil_db:dict=defaultdict(AirfoilData), + calc_cmx=False): + + ys = _calc_gridpoints(wing, M) -def _calcgridpoints(b:float, Λ:float, M:int = None): + try: + calculator = _calculator_dict[method](wing, ys, airfoil_db) + except: + raise Exception('{} is not implemented yet!'.format(method)) - # calculate number of gridpoints - if M is None: - M = int(round(Λ)*4-1) # has to be uneven, not more than 4*aspect ratio - elif M%2 == 0: - M += 1 # has to be uneven + base_res = calculator.baselift() - # grid points as angle - θs = np.linspace(np.pi/(M+1), M/(M+1)*np.pi, M) + if airbrake: + base_res += calculator.airbrakelift() - # calculate grid points - calc_ys = -b/2 * np.cos(θs) + for name, (η_r, η_l) in controls.items(): + base_res += calculator.controlsurfacelift(name, η_r) + base_res += calculator.controlsurfacelift(name, η_l).flip() - return θs, calc_ys + if alpha is not None: + base_res += calculator.aoa(alpha) -def multhopp(αs: np.ndarray, chords: np.ndarray, ys: np.ndarray, dcls: np.ndarray or float=np.nan, M:int=None, - mode = 'c_l', interp = True ): - """Use multhopp's quadrature to solve prandtl's lifting line problem - - Parameters - ---------- - αs : np.array - angle of attack regarding zero lift angle - chords : np.array - chord lengths of wing - ys : np.array - corresponding span position - dcls : np.array, optional - lift coefficient slope (the default is np.nan, which means 2pi is used) - M : int, optional - number of evaluation points (the default is None, which let function use recommendation number) - mode : str, optional - (the default is 'c_l', which [default_description]) - interp : bool, optional - calculate grid points or use given span positions for calculation (the default is True) - - Returns - ------- - namedtuple - local lift coefficients, optional circulation distribution - """ - - if np.isnan(dcls).all(): - dcls = np.array([2*π]*len(ys)) - - # calculate wingspan - b = 2*max(ys) - - # calculate wing area - if min(ys) >= 0.0: - S = 2 * np.trapz(y=chords, x=ys) - else: - S = np.trapz(y=chords, x=ys) - - # calculate aspect ratio - Λ = b**2 / S + if C_L is not None: + raise Warning('C_L value is ignored because aoa is defined!') - # interpolate - if interp: - θs, calc_ys = _calcgridpoints(b, Λ, M) - - calc_αs = np.interp(np.abs(calc_ys), ys, αs) - calc_chords = np.interp(np.abs(calc_ys), ys, chords) - calc_dcls = np.interp(np.abs(calc_ys), ys, dcls) - else: - calc_ys = ys - calc_chords = chords - calc_αs = αs - calc_dcls = dcls + elif C_L is not None: + res_aoa = calculator.aoa(1) - θs = np.arccos(-2*calc_ys/b) + C_L -= base_res.C_L - # calculate circulation distribution - γs, Bb, Bd = _solve_multhopp(calc_αs, θs, calc_chords, b, calc_dcls, return_B=True) + alpha = C_L/res_aoa.C_L - B = Bb+np.diag(Bd) + base_res += alpha * res_aoa - # return only ys and gamma - if mode in ('gamma', 'γ'): - return calc_ys, γs - - # calculate coefficients - c_ls, C_L = _calculate_liftcoefficients(θs, γs, calc_chords, Λ, b, M) + additional = {} - if mode == 'c_l': - # return lift coefficients - return _multhopp_result(calc_ys, c_ls, C_L) - elif mode == 'combined': - # return lift coefficients, induced angle and induced drag + if calc_cmx: + # Moment coefficient in flight direction + A = wing.area + b = wing.span + C_Mx = np.trapz(ys*base_res.c_ls*calculator.chords, ys)/ (A*b) - αᵢs = Bb@γs - C_Di = π*Λ/(M+1) * np.sum( γs * αᵢs * np.sin(θs)) + additional['C_Mx'] = C_Mx - return _ext_multhopp_result(calc_ys, c_ls, C_L, αᵢs, C_Di) + return {'c_ls': base_res.c_ls, 'a_is': base_res.α_is, 'C_L': base_res.C_L, 'alpha': alpha, **additional} diff --git a/wingstructure/aero/multhop.py b/wingstructure/aero/multhop.py new file mode 100644 index 0000000..ee0eb0b --- /dev/null +++ b/wingstructure/aero/multhop.py @@ -0,0 +1,354 @@ +"""Module providing multhop quadrature method for solving +prandtl' lifting line problem +""" + +import numpy as np +from numpy import pi as π, sqrt, arctan +from collections import namedtuple, defaultdict + + +# Definition of low level functions +_multhop_result = namedtuple('ext_multhopp_result', + ('c_ls', 'α_is', 'C_L', 'C_Di')) + +class MulthopResult: + + def __init__(self, c_ls, α_is, C_L, C_Di): + self.c_ls = c_ls + self.α_is = α_is + self.C_L = C_L + self.C_Di = C_Di + + def flip(self): + return MulthopResult(self.c_ls[::-1], self.α_is[::-1], + self.C_L, self.C_Di) + + def __mul__(self, factor): + return MulthopResult(self.c_ls * factor, self.α_is * factor, + self.C_L * factor, self.C_Di * abs(factor)) #TODO check C_Di and alpha_i + + def __add__(self, other): + return MulthopResult(self.c_ls+other.c_ls, self.α_is+other.α_is, + self.C_L+other.C_L, self.C_Di+other.C_Di) + + __rmul__ = __mul__ + __radd__ = __add__ + +def _prepare_multhop(ys: np.ndarray, αs: np.ndarray, chords: np.ndarray, + dcls: np.ndarray, S:float, b:float, M=None): + + # calculate aspect ratio + Λ = b**2 / S + + # calculate grid points + if M is None: + M = int(round(Λ)*4-1) # has to be uneven, not more than 4*aspect ratio + elif M%2 == 0: + M += 1 + + θs = np.linspace(np.pi/(M+1), M/(M+1)*np.pi, M) + calc_ys = -b/2 * np.cos(θs) + + # interpolate input values + + αs_int = np.interp(np.abs(calc_ys), ys, αs) + chords_int = np.interp(np.abs(calc_ys), ys, chords) + dcls_int = np.interp(np.abs(calc_ys), ys, dcls) + + return calc_ys, θs, αs_int, chords_int, dcls_int + + +def _multhop_solve(θs, αs, chords, dcls, b, return_αi=False): + + # number of grid points + M = len(θs) + + # create empyt matrix (N_MxN_M) for multhoppcoefficients + Bb = np.zeros((M, M)) + Bd = np.zeros(M) + + # calculation of multhopp coefficients + for v, (θv, c, dcl_v) in enumerate(zip(θs, chords, dcls)): + for n, θn in enumerate(θs): + + # diagonal elements + if v == n: + # prevent division throught zero + if np.isclose(np.sin(θv), 0.0): + θv = 1e-15 + + Bb[v, v] = (M+1)/(4*np.sin(θv)) + Bd[v] = 2*b/(dcl_v*c) + # non diagonal elements + else: + Bb[v, n] = - ((1-(-1.)**(v-n))/2*(np.sin(θn)/ \ + ((M+1)*(np.cos(θn)-np.cos(θv))**2))) + + B = Bb + np.diag(Bd) + + # calculation of local circulation + γs = np.dot(np.linalg.inv(B), αs) + + if not return_αi: + return γs + else: + α_is = Bb@γs + return γs, α_is + + +def multhop(ys: np.ndarray, αs: np.ndarray, chords: np.ndarray, + dcls: np.ndarray, S:float, b:float, M:int=None, do_prep=True): + """Low level function for multhop quadrature calculation + + The parameters except for S and b have to be numpy arrays of the + same length and determine the wing geometry as well calculation + grid points. + + Parameters + ---------- + ys : np.ndarray + span positions at which lift is calculated + chords : np.ndarray + chord lengthes at span positions + αs: np.ndarray + array of angles of attack for chord positions + dcls : np.ndarray + lift coefficient slope regarding angle of attack + S : float + wing area + b : float + span width of wing + M: int + number of grid points + + Returns + ------- + tuple + multhop results - (c_ls, α_is, C_L, C_Di)) + lift coefficients, induced angle of attack, + wing's lift coefficient, induced drag coefficient + """ + + # calculate aspect ratio + Λ = b**2 / S + + if do_prep: + solverinput = _prepare_multhop(ys, αs, chords, dcls, S, b, M) + + γs, α_is = _multhop_solve(*solverinput[1:], b, return_αi=True) + + θs = solverinput[1] + else: + M = len(ys) + + θs = np.arccos(-2* ys/b) + #print(θs) + γs, α_is = _multhop_solve(θs, αs, chords, dcls, b, return_αi=True) + + # calculate lift coefficient distritbution + c_ls = 2*b/(np.array(chords)) * np.array(γs) # TODO: Check this formula + + # calculate overall lift coefficient (whole wing) + C_L = π*Λ / (M+1) * np.sum(γs * np.sin(θs)) + + # calculate induced drag + C_Di = π*Λ/(M+1) * np.sum( γs * α_is * np.sin(θs)) + + return MulthopResult(c_ls, α_is, C_L, C_Di) + + +# Helper functions for high level interface + +class AirfoilData(object): + def __init__(self, alpha0: float = 0, dif_ca_alpha: float = 2*np.pi, cm0: float = 0): + self.alpha0 = alpha0 + self.dif_ca_alpha = dif_ca_alpha + self.cm0 = cm0 + +def _calc_gridpoints(wing, M:int): + + if M is None: + M = int(round(wing.aspect_ratio)*4-1) + + b = wing.span + + θs = np.linspace(np.pi/(M+1), M/(M+1)*np.pi, M) + calc_ys = -b/2 * np.cos(θs) + + return calc_ys + +def _calc_base_α(wing, ys, airfoil_db): + """calculates of geometric and aerodynamic twist""" + + def get_α0(airfoil): + return np.radians(airfoil_db[airfoil].alpha0) + + αs = wing.twists # geometric twist + + αs -= np.array([get_α0(airfoil) for airfoil in wing.airfoils]) + + return np.interp(np.abs(ys), wing.ys, αs) + + +def _calc_eta_eff(η_k): + return 22.743 * arctan(0.04715 * η_k) + + +def _calc_flap_Δα(controlsurface, ys, η): + + λ_k = controlsurface.length(ys) + + λ_k[ys<0.0] = 0.0 # only consider one side (symmetric wing) + + η_k = np.full_like(ys, η) + + η_keff = _calc_eta_eff(η_k) + + k = -2/π * (sqrt(λ_k * (1-λ_k)) + arctan(sqrt(λ_k))) + + αs = -(0.75 * k - 0.25 * λ_k) * η_keff + + print(η_keff[0]) + return αs#/η_keff + + +# Definition of high level functions and analysis object + +class Multhop: + def __init__(self, wing, ys, airfoil_db): + self.wing = wing + self.ys = ys + self.chords = np.interp(np.abs(ys), wing.ys, wing.chords) + self.dcls = np.full_like(self.chords, 2*π) #TODO make adaptable + self.airfoil_db = airfoil_db + + def _multhop(self, αs): + A = self.wing.area + b = self.wing.span + return multhop(self.ys, αs, self.chords, self.dcls, A, b, do_prep=False) + + def baselift(self): + # geometric and aerodynamic twist + αs = _calc_base_α(self.wing, self.ys, self.airfoil_db) + return self._multhop(αs) + + def airbrakelift(self): + α_ab = np.radians(-12.0) + αs = np.where(self.wing.within_airbrake(self.ys), α_ab, 0.0) + return self._multhop(αs) + + def controlsurfacelift(self, name, η): + try: + control_surf = self.wing.controls[name] + except: + raise Exception('control surface "{}" is not set in wing definition!'.format(name)) + + αs = _calc_flap_Δα(control_surf, self.ys, np.radians(η)) + + return self._multhop(αs) + + def _controlsurfacelift_n(self, name): + try: + control_surf = self.wing.controls[name] + except: + raise Exception('control surface "{}" is not set in wing definition!'.format(name)) + + η = 1.0 + + αs = _calc_flap_Δα(control_surf, self.ys, np.radians(η))/_calc_eta_eff(np.radians(η)) + + return self._multhop(αs) + + def aoa(self, α): + αs = np.full_like(self.ys, α) + return self._multhop(αs) + + +def calc_multhoplift(wing, α, controls:dict={}, airbrake:bool=False, airfoil_db:dict=defaultdict(AirfoilData), options:dict=None): + + # calculate grid points + ys = _calc_gridpoints(wing, 87) + + # sum up aoa parts + + # geometric and aerodynamic twist + αs = _calc_base_α(wing, ys, airfoil_db) + + # control surface parts + for name, (η_r, η_l) in controls.items(): + try: + control_surf = wing.controls[name] + except: + raise Exception('control surface "{}" is not set in wing definition!'.format(name)) + + αs += _calc_flap_Δα(control_surf, ys, np.radians(η_r)) + αs += _calc_flap_Δα(control_surf, ys, np.radians(η_l))[::-1] + + # - airbrake + if airbrake: + α_ab = np.radians(-12.0) + αs += np.where(wing.within_airbrake(ys), α_ab, 0.0) + + # add aoa + αs += α + + # interpolate chord lengthes + chords = np.interp(np.abs(ys), wing.ys, wing.chords) + + # determine lift slope + dcls = np.full_like(chords, 2*π) #TODO: make adaptable + + res = multhop(ys, αs, chords, dcls, wing.area, wing.span, do_prep=False) + + # alpha_i + return {'ys': ys, 'c_ls':res.c_ls, 'C_L': res.C_L, 'C_Di': res.C_Di} + + +def calc_multhopalpha(wing, C_L:float, controls:dict={}, airbrake:bool=False, airfoil_db:dict=None, options:dict=None): + + # calculate grid points + ys = _calc_gridpoints(wing, 87) + + # interpolate chord lengthes + chords = np.interp(np.abs(ys), wing.ys, wing.chords) + + # determine lift slope + dcls = np.full_like(chords, 2*π) #TODO: make adaptable + + # use default airfoil if no database is set + if airfoil_db is None: + #warn('No airfoil database defined, using default airfoil.') + airfoil_db = defaultdict(AirfoilData) + + # geometric and aerodynamic twist + αs = _calc_base_α(wing, ys, airfoil_db) + + # control surface parts + for name, (η_r, η_l) in controls.items(): + try: + control_surf = wing.controls[name] + except: + raise Exception('control surface "{}" not defined in wing!'.format(name)) + + αs += _calc_flap_Δα(control_surf, ys, np.radians(η_r)) + αs += _calc_flap_Δα(control_surf, ys, np.radians(η_l))[::-1] + + # - airbrake + if airbrake: + α_ab = np.radians(-12.0) + αs += np.where(wing.within_airbrake(ys), α_ab, 0.0) + + multhop_ = lambda αs: multhop(ys, αs, chords, dcls, wing.area, wing.span, do_prep=False) + + baseres = multhop_(αs) + + # add aoa + αs_ = np.ones_like(ys) + aoares = multhop_(αs_) + + fac = (C_L-baseres.C_L)/aoares.C_L + c_ls = baseres.c_ls + fac * aoares.c_ls + α = C_L/aoares.C_L + C_Di = baseres.C_Di + fac * aoares.C_Di + + return {'ys': ys, 'c_ls':c_ls, 'α': α, 'C_Di':C_Di} diff --git a/wingstructure/data/geometry.py b/wingstructure/data/geometry.py index 0c1d20e..6c225fe 100644 --- a/wingstructure/data/geometry.py +++ b/wingstructure/data/geometry.py @@ -372,7 +372,12 @@ def chordpos_at(self, span_pos: float): """ return np.interp(span_pos, [self.y_start, self.y_end], [self.chord_start, self.chord_end], - right=-1.0, left=-1.0) + right=0.0, left=0.0) + + def length(self, span_pos): + + return np.interp(span_pos, [self.y_start, self.y_end], [self.chord_start, self.chord_end], + right=0.0, left=0.0) def __lt__(self, other) -> bool: return self.y_start < other.y_start @@ -390,7 +395,7 @@ class Wing(_BaseWing): """ def __init__(self, pos:Point=origin, rot:Point=origin): super(Wing, self).__init__(pos, rot) - self.flaps = dict() + self.controls = dict() self.airbrake = None @classmethod @@ -423,10 +428,10 @@ def set_flap(self, name, span_pos_start, span_pos_end, depth): flap = ControlSurface(span_pos_start, span_pos_end, depth) - self.flaps[name] = flap + self.controls[name] = flap def get_flap_depth(self, span_pos: float)->float: - for flap in self.flaps.values(): + for flap in self.controls.values(): if flap.y_start <= span_pos <= flap.y_end: return flap.depth_at(span_pos)/self.chord_at(span_pos) return 0.0 @@ -451,7 +456,14 @@ def is_airbrake_pos(self, span_pos: float) -> bool: return True else: return False - + + def within_airbrake(self, span_pos): + if self.airbrake == None: + return np.full_like(span_pos, False) + + return (np.abs(span_pos)>=self.airbrake['start']) \ + & (np.abs(span_pos)<=self.airbrake['end']) + def plot(self): import matplotlib.pyplot as plt @@ -481,7 +493,7 @@ def plot(self): plt.plot(y_positions, -1*np.array(x_positions)-np.array(chord_lengths), 'b') # draw flaps - for name, aflap in self.flaps.items(): + for name, aflap in self.controls.items(): y_pos = np.array([aflap.y_start, aflap.y_end]) y_pos = np.concatenate([y_pos, y_positions[(y_positions>aflap.y_start) & diff --git a/wingstructure/structure/internalreactions.py b/wingstructure/structure/internalreactions.py index 538eef1..2e9f85e 100644 --- a/wingstructure/structure/internalreactions.py +++ b/wingstructure/structure/internalreactions.py @@ -69,7 +69,6 @@ def calc_moments(coords, discrete_loads): coords_Qs = discrete_loads[:,:3] Qs = discrete_loads[:,3:] - # iterate over grid points j = 0 for i in range(1, n):