diff --git a/docs/notebooks/EngineeringWindFarmModels.ipynb b/docs/notebooks/EngineeringWindFarmModels.ipynb index f221acd4be57793e060907a07269ff0b241ab1df..e1edf493ab66361d2ca8db9f865bf21c364c56d7 100644 --- a/docs/notebooks/EngineeringWindFarmModels.ipynb +++ b/docs/notebooks/EngineeringWindFarmModels.ipynb @@ -88,6 +88,8 @@ " - [CGIRotorAVG](#CGIRotorAvg): Circular Gauss Integration\n", "- `DeflectionModel`: Calculate deflected downwind and crosswind distances due to yaw misalignment, shear etc. Available models are:\n", " - [JimenezWakeDeflection](#JimenezWakeDeflection)\n", + " - [FugaDeflection](#FugaDeflection)\n", + " - [GCLHillDeflection](#GCLHillDeflection)\n", "- `TurbulenceModel`: Calculate added turbulence in the wake from one wind turbine to downstream wind turbines or sites in the wind farm. Available models are:\n", " - [STF2005TurbulenceModel](#STF2005TurbulenceModel): Steen Frandsen, from IEC 2005 standard\n", " - [STF2017TurbulenceModel](#STF2017TurbulenceModel): Steen Frandsen, from IEC 2017 standard\n", @@ -239,7 +241,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "119 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)\n" + "86.6 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)\n" ] } ], @@ -288,7 +290,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "1.14 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n" + "798 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n" ] } ], @@ -731,7 +733,7 @@ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x229f1ba8250>" + "<matplotlib.legend.Legend at 0x178dfd87250>" ] }, "execution_count": 19, @@ -777,7 +779,7 @@ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x229f1f4c220>" + "<matplotlib.legend.Legend at 0x178e5da44f0>" ] }, "execution_count": 20, @@ -892,7 +894,7 @@ { "data": { "text/plain": [ - "<matplotlib.contour.QuadContourSet at 0x229f2096460>" + "<matplotlib.contour.QuadContourSet at 0x178e5b7b400>" ] }, "execution_count": 22, @@ -933,7 +935,7 @@ { "data": { "text/plain": [ - "<matplotlib.contour.QuadContourSet at 0x229f24526d0>" + "<matplotlib.contour.QuadContourSet at 0x178e46c8820>" ] }, "execution_count": 23, @@ -975,7 +977,7 @@ { "data": { "text/plain": [ - "<matplotlib.contour.QuadContourSet at 0x229f2583c10>" + "<matplotlib.contour.QuadContourSet at 0x178e5d0db80>" ] }, "execution_count": 24, @@ -1331,7 +1333,7 @@ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x229f25242b0>" + "<matplotlib.legend.Legend at 0x178e6336e80>" ] }, "execution_count": 34, @@ -1792,7 +1794,7 @@ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x22980d52b50>" + "<matplotlib.legend.Legend at 0x178dfe93f10>" ] }, "execution_count": 45, @@ -1854,7 +1856,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -1868,11 +1870,14 @@ " wfm = IEA37SimpleBastankhahGaussian(site, windTurbines, deflectionModel=deflectionModel)\n", "\n", " yaw = [-20,20,0]\n", + " D = windTurbines.diameter()\n", "\n", " plt.figure(figsize=(14,4))\n", " fm = wfm(x, y, yaw=yaw, wd=270, ws=10).flow_map()\n", - " fm.plot_wake_map()\n", - " fm.min_WS_eff().plot(color='k', ls='--')" + " fm.plot_wake_map(normalize_with=D)\n", + " center_line = fm.min_WS_eff()\n", + " plt.plot(center_line.x/D, center_line/D,'--k')\n", + " plt.grid()" ] }, { @@ -1891,12 +1896,12 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 64, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 1008x288 with 2 Axes>" ] @@ -1921,12 +1926,12 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 65, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 1008x288 with 2 Axes>" ] @@ -1942,6 +1947,48 @@ "plot_deflection(FugaDeflection())" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### GCLHillDeflection\n", + "\n", + "Deflection model based on Hill's ring vortex theory\n", + "\n", + "Implemented according to\n", + " Larsen, G. C., Ott, S., Liew, J., van der Laan, M. P., Simon, E., R.Thorsen, G., & Jacobs, P. (2020).\n", + " Yaw induced wake deflection - a full-scale validation study.\n", + " Journal of Physics - Conference Series, 1618, [062047].\n", + " https://doi.org/10.1088/1742-6596/1618/6/062047\n", + " \n", + "Note, this model uses the wake centerline deficit magnitude to calculate the deflection. Hence non-gaussian-shaped wake deficit models as well as deficit models with improper near wake fields, e.g. NOJDeficit, BastankhahGaussianDeficit, NiayifarGaussianDeficit, should be avoided.\n", + "\n", + "As default the model uses the wake_deficitModel specified for the WindFarmModel to calculate the magnitude of the deficit, but a separate model can be specified." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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jWj13Bu4GbxiGUc1mVV3pt0BEFgBnASuArcB3ROQ8Vb3CZ92TcUT+hKhp4yAWkVfVm0RkzzjyMmYXnRCFdkl6u+S5nVLeCRnvRuS7UmU91h1k0EW4l5+CdBqri3jp9fpMJKTbRTB8iDFA8RLgYVV9GkBErgGOA6pkXEQOBb4MnKGqW6KkjYuO9ZEXkQuACwCWLl3K2ptviDX/iYmJ2PMcVCYmJlj7qxu7XQzachnv8L1hx/YJbl/7y8jp2lfMDlVAmzYzuWOCe9fd3I1Nzzqmdkxw3/pfdbsYs4KpHRP87o5bGq9ohMLqMz7sPG+aDcAqERnF6R5zKuDuhoOI7A5cA7xJVX8fJW2cdEzkS/2OVgMcedRKXXXCSbHmv/bmG4g7z0Fl7c03cMzxJwLtizjGHUWPM8odZ1533Hozhx59Qmz5tTNK1angcivH1H3rf8WBRx0fW1l6PerXDGF36YE7b2G/w49rb2EGhEGpy04FoX93xy0ccMTsr89OMGh1qSh5bV0wVPVWEbkauB3IA3cAq0XkwtLyy4APA4uAL4gIQF5VVwal9W5DRP42RFG2q+qX6q1go9YYvrQiW3FKei/1L4/L+fqpj3enu46EqZtuyvcs9H7DCE2njv9Bn3U4Tqwum0dVLwEu8Xx9mWv5O3DGig+b1svfA18E6jWRLwRM5I14iUPUW5XreAS/xfShMmj8cmbcrtxu+e6WSBe1f25KvTKWfl1iehHboG/qMiHWr9sweoj/VtV/qreCiIw1yiSu4SevBE7CGcrnceASVf1KHHkb3aNZYW/lhtZa2qaTtiyncY3VHbeEt1u6e0Gq+0GgoPfK6TSKeqtMnWCQZbbXf++EiFPGPmkY9QUDWJcRhp/sKqr6vjjWiWvUmtfHkY/ROwRJfLMXhObTNZWsaYFt9XrnFfFm3qCPW747MpJNp24UEW9Kg3YDMxrTjmOilxpF/dxQKddhb9SkYXQGEbkI+BqwDWcEnCOAD4Qde9661hiBhL0xRb2BNSOWzchts/fV1t4PqE0bteztEO/ZMgFVnMLUiYm/eh2rg2gkEt0uQWN6oUHRSmOiXP5e2I/ZQC81MjuBal8OC/w2Vf2MiLwM2Ak4H0fsTeSN5imqtl3Qowpu1HOz2ZO5lYi4N2nUPt3tuOB2dHbXTsyK2yX5nH03w+jneDfphUhzvWOvm42iXmtgxPIOkzUyY8PqsucpX9zOBL6mqneJhL/gmcgbvngvxGFlNIoEt1vMOxkJb3Vm17iFqtMX7s4JYe/LZ6+Xr4y2oe9sO2W7t+t15rjsRoOj0+d7OxsOzr509zzvhUaj0RwK5Hv6WuHLehFZgzMT7AdFZA4Q+qw2kTd88UptWClux4ud7Rbydvb7r/dYs9eG6eyHbbZDPttBv0TAgsrZrKj1w28Ds6/B0Wnx7Jfju1n65TgOR+8HPwYVEUmpah54O3A48JCq7hCRRTjda0JhIm8E4hboMNeBuMW8XV1SmrmotXLjKhZ7d8Kq0NvsoRt3L5UliHyxSLKPo3q9XMdxRIPjPofCNDD7tfHQ8UaCat802PuBwavLxkM+9xBrSyM9Xgdcp6pbAVR1C7AlbCYm8kYNiiPb5XO/kaCHmsQnbNecNrxgG1VK4hw+U1XJN2lFnZYp7ZOLfT+Us1iEos/YG93sy+zXsOiFuozQFRTo324k/Srb3ep+1cuNyX7D6rI3UdWVIrIHcAZwqYgsB24GfgzcqKrZMPmYyBu+5Iv1W7VhxDyuvuFRLkKdGJowcsMgYP1OSVQfvsEfiNIf+xP02xYKtd9FFdmmSXgamXSuLus9nehkY6KZug57vjcjS73aSOhmw6Co6vRznsX22emndb3QYO8U/dZHXlUfxZkt9jIRSQMvAk4H/llEnlbVlzfKw0Te8KVY1Np+8iEuwPXzDLHdHojIQ+sXvrIgteOGNIvvb43R/tj/SILss247bvTFoifPNtWln5y2u8EQtr56TWjirv9eaxg03SDok/O8Wfye1rULVcgVSi9i99joRkY1qpoDfl76oxShb4iJvOFLUesLaKOLbKvSH2YbZaLenFsbJz5iggY3pF4Ti15H6Y86KxT8y5hIhBMbb0QpFq9XrRLeOOvSHelul4DVk5B2NBSiNqbaUZdRiaPu45S9Zt9HUprvkuhHP7+v0iru89zviaDRfUTkFcBHgT2BJM5wlKqqc8OkN5E3fMkXi5WbQr2LcatR+DA3vyg36W5E38v4lTPuG1IY+sBzgWblVHuqa42fIBRUmQ542dVP8JuRjLANAi/lBoKzyXjqMinSlsaVV2g7Hb2OUjf1uil1o0HQzcZAqw2B8v7HeUj1U1eL+NscvXXNbDd9OiHUpcCrgXu0iQuAibzhS7FYHZFvNqoc5oRqR+S9lRM57muAN78+eqO+a9QT1W5eo703Wd/GmzrvmOQDHp+nPPvWjADG0SBQdfJptlFQKUuM3QSqnhi0OcLdqeh1XI2lKLRad91sCBRKo9bkCv3Rt6bXo/3l89xNq+f8oCAi+wPfcn21F/BhVb3Us95JODKeBjar6oml7y8G3oHT1r8HOF9Vp3w29RhwbzMSDybyRgD5YrHq5K93Mwoj+ZM7tvOxf/w70uk0R616EWe+6rWh8q7OK9RqrnK1L9Ieln66IfUS7mOvWu462xDy3vDCHArFojLt+c2TrnzCSri3IbDxsUe48kv/wfaJ5/mn/7y8Zn3vcRpGMAqqlfK0IiStikF50+2Q3nZKbeXa12Sf7rDR61bqpZnftdk6i+PF7fJwvTvyTj+QuET55p/+iFtvXMPWZzZz1hvezsrjTw6d1tv4dtPp6G8z9eEtY1DXv9mAAvmY9k9VH8AZ3x0RSQJPANe61xGR+cAXgNNVdYOILCl9vxx4N3CQqk6KyLeBc4HLfTb1PuBHInIjUBmpRlU/HaacJvKGL4WCVl6QaTbi7l50/Y9/wMmnv5IXnXoGH3rX+Zz2yj8HwktZsxfLdl5k8yGH3cz2scjXu4F1ioKnb3c7f1PvTbKZRkNBlWyxujNqSv2Nrby9oKi2uwGwdNc9eM9HL+Wf3nN+5Ziqd1N3NwT8fkdvdxD3v6PKQqGgTQtXIiEtP2Vxbzpu6Q0jtUHvGzQS21ai1+1uBHSrAaCqFKGmMRxE2HIeffLpHH3y6Wx7bitf/tRHOHTViaHLV9DOXQcb7U/Qk74g6t2DeuH63kecCvyxNMqMmzcA16jqBgBV3eRalgJGRCQHjAIbA/L+F2ACGAaGohbMRN7wJVfQwEhyI7nxu3E8tfEJ9tznQCfPRIKpfPi3bsIIc5SydJb+7p/YKHLTqcfKeZRUQtoyZGLcjYSC1kbkC+IThU8I+YDNpcSxNL/6V4VC6ZzwawAkfW7O3nycfu3B51ajRoAf3gZXaFqSWWd7zf5sYbpKtUI7I9vtHu6ymbpo9nqgngZlUZVsTG9mes+HK77077zsdW+paWzXxVXX5XMzTqquQXF2VUsI9e5BszEy7x6lJwSLRWSd6/NqVV0dsO65wJU+3+8HpEXkBmAO8BlV/bqqPiEinwI2AJPAGlVdE5D3QlU9LWyhvZjIG77ki8UqkW90UW8k2/OW7MxjGx9n+X4HkSsU6kapCzF0negVeY4SWWo37ZDusCLZKkkR8qVJylpp2JVxy2mrx4q3XvM+XWv8up6kAqJ8SREK1EpGuV6LfhF/l1z4SbuXAooyU856v1mUrjflBkCUSF+zDYCkSNPdrOJuADTbYA/a72YaALNF/lWdv8mQwZ6G3bqK5XyVq/7zXznk2JNYtt/B7MgXSDVx3E17Wp5xXFfjumZ6GxmFQrSnG1Vl6vG+/zGxWVVXNlpJRIaAVwIf9FmcAo7CidiPAL8WkbXA08BZwApgK/AdETlPVa/wyeOnInJaHdGvi4m84ct0vljpo+gljGh7L+hHn3wGl/3rP/DrG9aw8sUvrbqw5LV10Y1D/ttBIobIUlwX+aDob5m4Ik1x9Ll2k0yI63hq/gmHuzzNNgb8BLWm/6kq23MFUknXukWfbjs+9ZNIiO8IGykRtm7ZwpWf+wQP/e5evrX6M5z99ne5uuZUH2PuY8b7u5d/Z/fTjaB3Eqr2y9Voa3RMRj0Gyk9c2k25PM00ANzC6HcINjos/apiNsp/1FFrvHXw4/++lAMPO5LtufrXzcr5FaJ8SRGuv+qr3H3rL9m27XmeePQhTnnNm5iul38LXcUiU2x+e0DgdQDC3YP8zudG94vepS1Pwc8AblfVP/ksexynQbAd2C4iNwGHlZY9rKpPA4jINcBxgJ/IvxN4n4hkgRw2/KQRB9lCkelCsUqy//T7O5meeI7djpzpWxhWoCUzzAUf+ffK5x35fFPl6qdhxAAyQLZ0J2z2Qh3l8Wcr8lxPBpshr603DpIiVceYEq3Rlmwh8u77AmpRmZ6a5Dc/+BrH//lf+YpQoahM5YuQr80j7ZL7LFTLPvgKf0qEaZTMnPm89YMfmymLas35UD7G6r1QW/6dyzd47+8cJP5V+xhC/J1tlYQ+xLEUuZ99xEMrKdKSONeTf7+XsJt5UdpNUFXEcRyXiSr/nRL/YqHAtmywfKaTAiFuIe59P/bst3Ds2W+pfJ5w5Z/2noe43jj0UHPOenHtf5TjudyoiHqfCGr8l3Hfg9y4t9Ope0wf83r8u9UAfB/4nIikcPq3HwP8BzAGrBKRUZyuNacC6/wyUNU5rRTORN6oQYGpfKFKtvOqPHnvrWTmLQqM1Lc6mki/SbqbwIuv6wY/3UTfx6jRnaA6bKYREUtkPdHc05ZKH3HP/oTpI1/V37RJ6ffbdpnEUAbVIhv/+Ft23vvgmvopqLItWyDtyq+c91TplKqIQwPZB3/hT4pAoVbi/Y6x8jHkPTaGqR61pipv9/64GniNIndBDbd+FX8IL87eLkJRr4e9IP6N6jOK+IcdwcZP/IsKWycLJOv8Zn7yXbU8xDHkPS9DbcNn3bDHYaMyQ0ADvw7JBi/iDml115ryvSDs/ch7XPb6fTpiH/mGlET8pcBfur670NmWXqaq94vIdcDdOM24L6vqvaX1rgZuxzlq7gBWe/LeWVWfarD9huuYyBu+ZAuFKmHP75gAYOERJ4fuuxjXEFC9RNAFNuhmOYz6RkMg5MW/Th1GkXP3RbuZxkEzDQFvND1Umop0BjQAtHZZTb/QEDca337jIcqaTEhFUB5afwM7731wzfbyWmQqp0xVpHRmWflGPpXHV/Qry7ziXqgv+wAFn++ChB+cG577Bh8k/PUid1Gl38m/nG+wpYWV9AIa/slRhDZlKtFc9L4VaY4i/n7ncJTi9rr0a6kxXKaeAAf9/vVEvl4Dwb29QMFvcMwFlcmdX12pL60XRwNhTJVsvvRUOBn+uK5sexbex6OgqjuARZ7vLvN8/iTwSZ+0lwCX1Mn+R8CRDYrQcB0TecOXyXyh6tHj1tt/AcBUchTqPPKcbXgvpFFb+nM1uEFTbxixMBGZpocLLEQX83zEaGYqYjeGSvSzgcwkUIpF9fQBb2xoUWU/sJ94qXz7H/9yHvjVD8nlciSSzmW0XKZCcUZCyjfYmcifViRgCq0RiiDRd+dRWR5C9r3HmPtGXm5kBt2wgyJ3iaq615r1yzSKwpelvxXhL28n1PEf5RiOKP1hnhSViRrpd6cpM9ulX4HnJ3O+x0bKx8KD3q8IlPwGDYMpnw7i7vNxyufaHfXpQZXUN9kwKFNveMyiwlTeOc/D3L/KZY06zKXRFIeJyPN1lgtQbzlgIm8EsD1XYPu0KyL/3NMkl+5Vt99iNwnzGDUM3gtmLuTFLOjGUL6I1mynwY3NfcEN8zi2MlpIyEeyUUcKSYUUm6Corm+eEYZ9rIn81hEZ/+4fwTvg2wc8oDzlcsxftgcAj959CyuOeHFVmQqqbM/mSSUTZEs367JoJBNCeVo/9+8aVfSd7QWLvlOO2u447uNqTqmR6Sf75f3w7ndpwcy6ru/rPfUpHw9+DchGXbgaCb+Tf4zCD6GlP8poSuVjIHJENEIabzoIL/1BT+rCbrpe1UbdZ1VlR3bGdKvlvfSOh5/k+3wXtjEQlL6ch990nEHXZu823Q0Dv3uV39O7sNty8g++B6oqU7liw8ZAOf9Cnbdc+6F/vNK7g194UdVkHPmYyBu1KGzLFtiWLcnP838iBWR3O4pctrk+z37EJd8Q7kIYWA7XBTIX4gLgV+6gC4fiH8X3NhDqXaTLF9YwF9FcQUOJv5NhiFUknJiXBa2RNARFc4PyK1MWAaGOYJbXDShDcOTX70WwgL7erm2PzF3Ipj/eWxF5N9l8ceZxdkLIerZflny3OFRkHn/JB3CPIucVfe/v3kj01aeR6Re1C4rsQ3tk35umkmWHhB8INxJKhNGUysIfhlaEv5V0vST8RYVtUznA5+Vvz0XeWx4/Ga9N43POl85TN0HHUjk/V1sj9FMBv4Z8ozRQ3cj3I5fw/wGKCjtyxbrbSyfqd4MslydsYMvoLCbyhi9T+WIl+j7/gRsA2JZP0NLsLd5tNFgeWkhdRBlppXxRbNR69zYO/MQ8qKyqWpGlehfhchnqlT9HSEkP8dAknZSG7zCE6U8ZRvTdUhYkC2FfqPJesBpFzqvW9dl2WLn3k7+9V72Me9dcyeT2bQyNjrvSzkQTUwmZGY2idCAlEkI2X6wShzgk33uj997YvcdOOVLnzst9o/aL0JXr1e+JUdUkUu4nBxFlH2aE308a60X3oXHkvUDtSD21RJP9elHAauEPpnIu9ZDw+9VjJ4RfVdm2PUfSdRzVCLvrwuz7MnjAuoH51cnfmybragCX07kbAY0aAE4e/tutl4f3/G+0PjiHaaFY7/gM7jJa3laYAFevoPRXeePARN7wZVu2yPZsHlRZUCywdemhzucYCHqs6aXRhc5LMtG4D2B114RwYpfztF0aReTdu+d+gz5StLxJ0g3k20/EvOnLBIl+kKR5t1HJJ2BcdDd+cuDbt5fGkVu/cjXq815ZL4LYD8+ZD8BDv7meA046u7KsWFQmp/NV200lE0zni5V9ChJ8oEryvYLvLPeXfKh94TUomg8zkTpwfnf3zc8vQteq6ENwF7Dy71XzOxWCpb2R7Nd7DyRu2VeCz4WwL1FHFfIwwh914rO4ZB/CCX892c+67jfu9cpynwUSnrErw4q/89mVp0jNmPJhI//udI0aDO6ndJXvymUI2QXIe/6XCTpeFf/uneU03vsb1A9yNfPU22gvJvKGL9uzBSanC8x77iEAnh5bAdPh+8fXGxkl67moNBL1fIPl5Ytd0I2t6mbWQNz8uhV4aRQ9zxVnLoROpK46bbMXwjCPPxsJeqMIdpinDV7BD5Iyb95VedQZGaWMWwSCjqcwUu8tU12BC/FkxNmu86Mu3H0/ntnw+5oy7ZjKz0h7MuHIuUccysu8gg+OHAQJPtCy5CcTQpGZvrteyfeL5tcTfb9H7x0RfWdhZNGHEKMxNXr4mKgOyfu/h9H4ZI8i+xDtfZJ+lX1VZft2R5GTnvPA+c7975k69v7W7vVyLulPJoXp3Mx6ftLvHunFb1/8BH66lKZRtN99frvxpnOf82787olBLwGrVD95A9f5GnC/q3ef9BP/XiLu4SfbiYgsrLdcVZ8Jk4+JvOHLVK7AjmyefTffjSLsqCPxYYYzrCfrxQZmWy//VEIoFGvLVjWqiWex+4LnvtH5ztpZDB7eLkyE3f8xX9Cj0fqR8qDHhUHRk6qLb4MGSj35bpQ2brGvK/V1rs9RpT4OoV9+2PE8s+H3bNu8kTmLd6l8n3PNSlkWiLJUBAl+OSroloIwgu8uL1Tf5N3HurvPvCPyMzd4r+S7b9a10l4r4l7J95bJK/pVT8Z83gEp519zrMUo+uDIfhyiXx5NqYYG8yikJBFazBt3AQwn+u48IfxLumU6IfuqMDk5E5EvnzuplL+0B8l8GOFPeo6lRCJBrnQC1BP+6m4yxUDhB39xd5/flTx9jkXv/TGRqH3fwu8+mEwI+QIU08793F1uv/QQfA/pFzHuQ9bj3NUE2B14tvTv+cAGYEWYTEzkDV8mpwtMTjkP/e6bt7KqW029kVGC5CgoAgEhIul1RHw6IGpbT9ALRf/H5vmCfxnzxeanj1etFRo/IXdkqTZ9uTj1ZNpPoiA+sa83BKdfQ6Ys9o3G3A86Vhr1gS4LU70GXlnCCvk8ueksyVSK9FCG6ewUT298nNx0ltx0lkIuR346y+77HsiCxUt4ZtNT3HnLDUxPZ8lPT5PLOeu8+MyzWbb7Ch66/x5+/K3LyU1Pk8tNk5vO8sxjf+T0Lc9w+gUf4rabrueZqSxTU87115EH54ady7ll3pGFsijUk3uoPn/8ju1igKTUE3xNOjM4p1yi7n2qk07WRt7Lku8Vdr9j1dvHtjaN65gMKfnubfiJvt9xV3eUphhEX/BvsDZ6gtZo6NR2iD4+ZQ3KE6KLftT8/dKoKlM7pkhUHdcJpzuN5zuolnE/2U8mZ+TcK/G5HJV1HGbWm1k2k6a8TbfsA1XncaUsEWQfgqS9+gbo15d/2vMbuWVfU1oV2XcLf2XeDtcmogS6ehW/e2kvoqorAETkMuAHqvqj0uczgJeEzcdE3vBlKldgt+fuA+BpmQ+uFn05QhF0gwsS9sD+1AGi7ndxAqoubFUXl4L/9qeLtTfjYDkPugL4P7ZsFMFQtCpy7xelhHoy7s575t9BMh2UT71tu/Pz7UpUR7zrpcsXFFVFNI8Wi6Qzw6gqf9rwELlsSaSnHRleuHQZy/faj9x0ll/+z7erl2enOPjo43nBqhPZ9vzzfOE//opcdopcSbanp6c4/dy3ccLLX8OTGx7iI2991Yyol2YnvuAf/42XvPqNbPjD/fzDm15RU9b3fOzzvOiMs3nikQf5/Ef+tmb5Xge+gGW7r2DrlqdZ98ufkU4PkR4aIj2UAZLkprOoKv/7za+SnjOX3NF/49RPruiSCaFYLEfTnXorFmfE3vnsfO+V+6DfoVhH7t3LnW1UC74mlGyuEBjB9x7fbsmvJ/jQWPL9XqKLKvngiH4UyXf2MZro1xt6tTy/QpImZrxsIBuDLPqoMj3ldF4pi3sy6QyzJa78ysvK508imag8DUt4utJAreS7JT5I9IFSI3xG9N3rFD2jQ7m3631Kk/fM9eC9VzV6eumVdq/o+wW/dNgph1/Qq/zbVXc/9R/9KR9wfzVi4YWqemH5g6r+WEQ+Gjaxibzhy3SuwPLcEzwn40yVZq7wPoKE2peNIPjFRb9Wv99IFwXXitWjXISTdL87ZMpHxMO+uus8onSNUlBHnmpulnWilM1KfdAoOl5x8UZBK/kU8kxtn6QwnSWXnSSXnSKRTLFk973IFZQ/rr+Zbc9uIZedYrq0fMGSZaw87VUkRfjuf36U57ZsYnrKWZbLZtnn8KN5zV+/D4APv+4UJrY+U5FpLRZ50Vnn8tYPfQKA//PaU2smgDnt9W/nDX97CcVika9//ENVy9JDGTKjo7xg1YkAPP7g70gPZUhnMgxlMsyZv5Ch4WEAxubMY9VL/4x0JuOkK/1/n4MPB2Dn3fbk3f/6+ZKED5HJDJMeGmL5nvsAsN+hR/HFH95aSj9EOj1EZni4IhFHnnAKX/vZnVXlU1XuunY1Tz98H/l8jmcee4ThI2aO4fLxWSwmasR+RhD8o/ZOOqeu3BIQRu7Bc6N23Yini0pxyFnuXsd9E/feuKvXq5Zt7zFYs7xBFL+ch1+j1O8pU7OSX5cmRnSuSL76SFsd32k0YVqYaD6Emx8hzMvvEJ/o17zs3oToKzCdLYm8S9JrPuccwS8PPVqWfG/UPp/zl/yZe1rBN5LvFviaWY19JX+mkQ71JT+ZFKaL1deJmnPO0/gs3xtnJnGrXt9P1p3RqQp1g17ldH5Ptf2eaPcyqtqPo9ZsFpH/A1yBc/ifB2wJm9hE3vAnOwHAbXJg5eLnvWBB9UULylFH9/Lqi2DlMWSxdlIPv64t3kflhXyhNkqBN001KY+Ig7Mdv5cp68n5zGPIcFIPgJaifT7dWvxG0EkmhOnJ7Uxt30Zuage5qUl0epJCPs+Kw48F4IG1N/D0hgcpTE9VRDw9PMJL3vZe0klhzZc/xaO/XU8+OyPqC3felQs+fQUAl110Lhvuu7OqPvY46Aje9YWrAfje5/+VJx96oGr5fiuPZ+VprwLg0fvvYmLrMwwNjzA0PEJqKMNQZriy7iHHnkShUHBkeyhDZjjD7vsfDDhTsf/FR/+TVDpNeijD8PAI6aEMC5bsDMBQZphLf7yOdCbDSGaYVHqo6uY3Z+5c/u3qX1T9Zm7mLljE+R/8F+d38JG48XkLOOEMZz/8JC8zPMKS5btVPocZzjSdcAxwwx03scsee/GH677HztkCxWKRRCLhigZW3yzL0XnvudEoau8WgLLcV/WFd8t96Z5brEjQTARP1Wmwl9O7b+ZBcu9sk0oa780+jOD7vTgXRfCddLVjavu9z+I3ZGuQ4Ae98xLUXceN4vMCd4hIvh+9IvnlbZUJOwlbFMmHANF3R+QTiYq0V8l8+clwHcEHKJbfU/Cmh4rgQ7WoV3fVqZXcpOv+kUgIhUKhkq70z6p1/NYr3yf97pEtCz5UAl+qMxH52ifTtYIfLlhmxMzrgUuAa3EuJzeVvguFibxRgwL7PncnAM/nM7j7DJYvRFB70YLa/svuC5ezvDoqUSho1UVzuljwuWi4Hvd7hldMSm203Hvx9JPscpqUp9zusgdJSgIll50kP7Wj9P9JNDfFTnsfTDI9xFN/vI/ND95DbmoHG7NP8shUhtzUDl789g+QSg9x93VX8cBN/0t+apJ8dtLJIzvFX3/j1xSK8NMv/Qv3XP/dqhoYGhnjfdfcAcDdP/8+9934Q2f/02nSmRHmLdmFl7ztvaU6zZNIJBmbv5B0Zph0ZoSFOy+v5HXsWefxghPPIDM86kS1h0cYX7C4svxt//wFZ5uZYdLDI04eQ5nKTeJvv3g1XtwC9Nr3fLjq9/JyzGmvdMruc1MXEeYvXlL3JcNGN5W6LygGbLdmnYbbqL7Z7Xv8y/nDr37Isl33YMfzz5F9/hmSI3MpFoqBwp5Kaemcmrnpl8+x8rlVLQy1Yl8+f9wN43KD2H2zrzSAXUKgqpUX7spy7xe5r+Rbcz4UfOUearuuuSU7qCHrFXxvJN7vSVScgu+sXPtVXSpRfK29LgXNhSBSdz6Feumg8chTcUl+mG1V1gvYZu1swyElXxWmJiCZpphIUswByZQj494IfdEj8qXPRdd9odI9p+iRe5/04G5MJ2ruYVAt937i7pV2oHI+O/kHr1fvHukNgHmDX36BL8UVfPINllVTI/f4P9E24qM0Os1FIjKuqhNR05vIG77MLTzPQ4WdyOVnLhx+0u4WdveFzn3RqsV7E6l+3Fgj4i7pdo+Q4pb6ytjKQYJezIMIeVJMb3+e7U89TH5yuyPjk9spTu9g92NOY3TBEp66/3Ye/MV3yE3uIDflrJOf2s6p7/0s85btwT0/uoLf/Pcna/bqjV+8njk77cKjt9/EbVf+58w+pYdIZUY47ryLSKWH0GIRkQSjC3dyZHp4hPTwCFoskEqnOeSUs9hl/8MYGhklnRlheHSUoeGxSn4vf/dHeeV7Pko6M0IiOdMfoCwmZ/zlB6rK5RWbI1/6qkBRTSeFJbutqPk+SH6DRu1p5oVoqC/hiYQEDfjTMG2j7UK46HvQrKDzdt4dgKGiM81ZdstjDC3dn0QyUYnMFwvF0ucZQYgSrQ8SfndkL4rYV27wrpu7O/pWdUMPXCceuYdaMW8k9zPr+P4kzRHUvaaB4Kt6Rm8KyKcdgl9OG0aSwwh3kOSHEXz3tuptz7vN2jwVclNQKHdiTzsR+JLYAxSLjr4kCg3EvnQOwozcu/vdF4oFJCFVy9zn7Mx37uh5wSP2CYpF/3tlFLkPup965d6NN/jl92S7qm69b4KGkPs4J4JsN0rjbmK9hogcB3wZGAd2F5HDgL9U1b8Ok95E3qghN+2MVrN2++5o0hXtTpUvOO4Lh//FxREP98kfvF75IlsWkfL3APlcDp2eJD81QWpknPToXIqT23j63pvJT05QzG4nPzlBfnI7ux7/Zyze+1C2PnIfd379nylkHQHPT+2gmM9x3Hs+w7JDT+DpB9az9nPvrSnLgj32Z3TBErLbnuXpP9xNeniM9MgomTkLmLt0t4o0Lz3gKI4+7+9ID4+SyoyQyowwPDrG8NwFABxy+hs45NTXkB4e4djhTaxP7FV10zrszDdw2JlvqJHg8jq7H3oMux96DOA/+dTo+HjNd35C7Ze2nsD70QmB72b0vRV5925ndN4i5u7YwXsuvZwr70ygRa2SBD+pnzkvSg3QVPXnMNF6782/WjhmxN4bxVPVykgaVV3WIop97XreF8lnrgM1EcQW5d6hzXIPvmKeFHG91F79pMFvMrV6MyW3IvjQeNK1RhO3xSX4Qdvybq/eNoFyJ3koByqKBUgkHbFPpkvfOf93i31Vg9l1zgGexrTnPCzMdMXxnrNO3t7zlkCxd/e1rxbgMIGtegduuPXcwS9w+ozXa2RGfao9SIjI/sC3XF/tBXxYVS91rSPAZ4AzgR3AW1X19tKyi4F34BzR9wDnq6rfpPb/AbwM+AGAqt4lIi8OW85YRF5ETsfZkSTwZVX9eBz5Gt1h67POHATTRSFRNXD3jGzMnOj1Ly6iOQqTE0xOPk96ZJzM/CXks1Nsvv3HFKa2k59yZLwwtZ1FLziRJYefwtSzT3HXZ/+KwtQEhexkJa/9zvk7dn3xa8g+t4nf/vfMC93JoWFSI+MsPniV8zkzwujiXRgaHSc1PEYqM8rQyBjjS5y+z4v2OYwX/93nSA+PkRoZY3hs3JH2UUeQ9zj6VPY4+tRK/t7HjEv3OZil+zh9vmtmC0wIqTlzZ8omW2puYEECX7WOr4TXfNWSwLdb3luKvNehXtpOiruXfY89nR3XfYO9j3ghes9vZ6JvpTtn0SeqVT9KXy313vPLK/V+y9zp3I/o8xRLUeRiZbn7sXxksaf68bs7oldP7P32q1X8h3JtXkT8JlrzzqCs5XdhxH9StnpyD/VFu56k15sRudUuOuAv2zWjmbQg+IHnq6oj7W5xL+Sd7jV+Ul+iWACSKV/xDsJv3XrnbDDe8zU+qXca8bX3YmdZAu+LuO50M+d5/afaEE7qe504J4RS1QeAwwFEJAk8gdOP3c0ZwL6lv2OALwLHiMhy4N3AQao6KSLfBs4FLg/Y1mNSfT6E7ujXssiXdu7zwEuBx4HbROQHqnpfq3kbnac8mshvk84oHsVi0RlCMJ+lUMwzND6PfL7IxO9/hWafpzA1QXFqG4XJbYwsP4CdVr4CLRb47aXnUZjcRjE30/hcctyfs+cr3okWCzzy/UsBkGSa1PAYyZEx5q44FIDU8DgL9j+a9Mg4qdJfenScuXseAsDYkj044SNXkxoZZ3h8DomkcxiXbwpzlq3guHd9urJd78VnfP4ixucf52wrYHg+qBV47w3MO1SfF+/6jQR+UOU9irh71+yEuIcd+WRs7nwAbrv6/zH5+yxDux8DqcxMPqHloH6U3l8CwiybWV6+8ReLSj5IMNyRO/fNPUDYqwSmwQvoweXzlCE2vOee/1wK3tEu/GZT9hP78qcgWe0VuW+UttF2IXx/eD/BDxu9B0pWXl6xVtobUQypN+6ovZek6yTwnre1XW+qz8FkUsjnZ87h6i425THovd+7u9L5rwPVXe7cTxxql1XXr7frqXfOj9BdVQeTU4E/quqjnu/PAr6ujjytFZH5IrKstCwFjIhIDhgFNgbk/Vipe42KyBBOA+D+sAWLIyJ/NPCgqj4EICJX4eyYiXwfcvu63wBw/Te/yMSTj1DMOqKOFhnd5zh2fuX/IZkUNl3/WQo7ngMcGU+OzCU5Msf5nEgyd79VJNLDJEfmMDQ2h+TIXMZ2XgFAYmiEIz90LanhMdLDM6OdlC9EqZFxDnzDP5S+c0X8ylabHGLuzq6RRcRnHeqLedj16sm7d12/9QWpkuG45L3VbjPtFvdmI+6NuspInW12UtqDtrXTHvvzjW98mG1/+APwRUimSQzPZXiv4xg/+k0kk0meW/vfJNIZEsPzSI7OIzEyh6H5u5Cas1PVY/9yBD2fL/o+BavucuO/rJaZm75q7ZB67ZD6+utV4zfUq/dl9aCx7aF2fPvKOp4hXb0R+1bEvkzgdPcBY4MHrR9F7qvGc/eOmuMekjSi3HvPwzDR9HoT0M1sp7Hcb9myhY1/2kLh6Ueccs7bAxmej04+S3H7U4A4P1ipS01iwV7I0Bg69RzFbU9AIlVaJpBIogtXkMyMk5/YAtv/BCLOvaZU/vTivZFUBrLPkZvYVLoPiTNcJZBZsg+STJPftoXi5LNO+qSzDkBmpxUUgeLUVoqTzztlSiQr+Q/vtLsj9NnnKExtR0SQhCAiIMLIImcggumJrZDPltIJyZQgkoQ5CwHQ6e1oPucqP+QTSTLjzhPg3OQEWixUtp1HSKXL3Y6UQi5LQgQEijkhlUqW9iVFvqAkpfT7iVDw/L75QrFvxo+P2Ed+sYisc31eraqrA9Y9F7jS5/vlwGOuz48Dy1V1nYh8CmeG1klgjaquCcj7QpxeLctL6dcA7wy7E3GIvN9OHONdSUQuAC4AWLp0KWtvviGGTc8wMTERe56DyFNPPUkmk+GQXeawfc6ejI7PYWx8nNHRcXZevhsHH+G8fLTp/36MzPAwo2PjZFxDD8IO5zp0wF9U5TvzyGhb6f8pRLJAFne0zM+j3N9JgGi5v61eJTjvMGkapfP7QlxfjJLl8OIjMwuLten99si3HsKs1Ep+9bOtk6LOkjpe3EiZfZfnpkhsvLeqjqNknm+wivdZZtBWcjXfOGvuunQR73rXu7j1rvt5/vkJtk9sY2Lbc+y258686EUpVJV/uGIN23dsr0p94mmv4DXnvYNcLss/XvQ2xsfnMDZnLsNz5jE2Zw6HrTyWQ45YSX56mrtvv5Wx8TmMDo87/x8bZ3hk1Pflcr/zpfzVoqECb1y+teb7oPWD8pvZ+6B8mjkHvUsD0qrni4J3Han6Lux5EPUcGipm2fX5PwSvGJBnndXrHuGxnqMhV4h6vvqplN95623Y/fCHP+Shhx6qfD7/wos55LADuO/eO/nK56+qSX/h3/4f9j1gN+647Rau+H+1DnbRhz7G7iuWsfamW/nOj75Us/wD//wZlu4ynxvX/JTvXXd5zfKPfHo1CxbtxHXf+xk/vrZ2+5+47BuMjI7xvSuv4ec//l7N8s98/VoSiQRXffVr/OrnP6laNpTJ8B9f/Q4AX/v8f7Lulhurls+dN5+Pf/HrAFz27//C3etvrVq+09Jl/NOlqxGB//joh3jgt3dXLd9tz71Y+OlP8/plz/KvH7yYRx/6Q9XyfQ88mL//v878Hv/4ngv508bHq5a/4MgX8u4PfsSpp78+n63PPMPc+fNr9rGP2ayqKxutVIqSvxL4oN9in+9URBbgBLVXAFuB74jIeap6Rc3KqpuBN0YpeFUBvBOzRM5A5M+Bl6nqO0qf3wQcrarvCkpz5FEr9Vdr1wUtboq1N9/AqhNOijXPQWN6eppLP/Uxjj5mFR+5Y5hNm5xRkLyTZ5Qf9bunvy7jnuzGvcxvtjy/mfd8I/DMRKTCRN/jjrw3irrX6zLzgvzD/G5oL89yamg24t5qtL0XIu31ouA1+/z4vbDrIa58m+vP3nA7TeYD8MtvfY7pYoLVG/ar6p/rnplSCznIbacw+TzFqedJjy8kvXB3KGR59leXU9jhfF+cfI7i1Dbmr3w1C174aooTm/jjF99Ss81lp/0VO616DblnN/Lod/+F1OgckiNzSI3MJT06h4UvOJnRZXtTzE6w48mHSI2Oc+4+yvefWUZ6ZBzxmeq+0bnoVy/NPBFrtC60/mTM9zyJ8d2UvXf8kQ3j+9SuGLSdJrqmdXKEKOjOMK+qymtX7sHKFx7D+uf3hkQKycx1Iu75KTQ3AeX7Run/Mr4USQ2jhWl0+9POC7Iys47M3Y1kZhSd3Ao7NqG4xkVXSC/Zx0k/uYXi8086jVXVyuRSQ8sOJpUZIb91I4XnNwJaatA67jS64mgkmWJ688MUnn8StNzQcpbPPfBERISpJx+g8NyTzveqzjqJBPMPOZVkMsHEI3cy/eyTSCmdqpLKZFh0+GkAbHvwNrLPPjmTVpXk8Bg7H30GAJvvvoHs1k2VZaiSmbuQi89axZVPLuCp3/yI6W1bSsVylo8u2pldjjkTgA03Xk1hx3Ol7rXO8vGlu7PbKmf5g9f9F4XsDlJDw9z3vS+uDyPA3WDJ3ofoa/7t26HWveycg0Pth4icBbxTVU/zWfYl4AZVvbL0+QHgJOAE4HRVfXvp+zcDq/xGohGR/XD61i9V1UNE5FDglar6z2H2I46I/OPAbq7PuxLcD8joYX52/XUApWnnwwu8n6R7l1ULQnW+nZL3OMQ9aj93oVYE2i3t7e4e0xFhr8m31KdT/OU9LlkPmxf4vxcBsPigY9h8360kKudC7dTyyaEMDGVIz5kZvz+RTEByhEUn/1XNOQLO+adzFrHXX6x2urxNbqMwtY1idoLxPQ8DQLVIcnQuhakJss8+RX7yeQqTE4wu24fRZXuz/Ynfc/+X/xaASuxOhIPf/m8sPPBYnvn9HTx63WqSw+OkRsac91SGx9nl+FcxZ8lyJp95iomND5VeEp9TeodljMzIOFJ6Uc7bn9ZvNJugvreN5nDwTsrmN+us3/r1Zlb2m1W50YzKfmPhB8/I7LMdn/IANS8euglK43150U29/Bp1p2n0kqM7vd95X68bUGUdTxeIQi7LEUccwZErj+GO24ZKG09CMo0k08jo/FJmqaplADI0iozMr3S5Ka9TOZfGF8L4Qv8JpADGdyI9d2nVd0n38L4Ll5NeuLxm1tjy+sNL94ale/sGqFKpBOO7HQi7Hei5X86sO3evI4AjqtK515u//zGlbdbebwEWH3qS77bhWQB2WfXyqjy9jeG9Tvnz6m16fq8DXn5+5d/3fe+LDBivx79bDTgjzfxNqVv5McBzqvqkiGwAVonIKE7XmlOBoAj2/wP+HvgSgKreLSLfBDom8rcB+4rICpw3es8F3hBDvkaHueeuO9h52S6Ac7EIK/Bxybv7JlS+iLRD3OtF86JE28NE7xJSnUcYae+ksDcTYW+3rNfL29t3M+o2yvmEIUjUG+W5YO9D2HzfrbxgznPcP+UR9fK/XTfcoO/9hrNLpocYWbJnaXntcT+8eDf2e8snXHkk0GKRcnRwbJd9OfDt/05+aoJVY5v55cYC+antjOw0E4uRZIrp57ewY9Oj5CcnKExOsNNhJ5JftIwt96/lvm/WDkr2og9fyfzle/PYr/+Xh376TacBkBl1Ro0aHuXQ117E0Pg8Nv3hbp5/7PekhkdJjzjLhkfHWbDnASQSSXLT06RSqcoTgkbDU3r77jYSevCfbbZmnYhC732w7Z++um+/Nw93mSCc1HuvAc1KPdQX81alPsz2AZLpDGeeeSa77L4vrL+/IumVYSh9BL7y/wCBr4h7kMD7fOcW+HIDvN767u+c9NUNcGe5v8B769OdNmg9r+gHbdsvbc39r84TNr/1e33kGkVrZnJvhZKIvxT4S9d3FwKo6mXAj3CGnnwQZ/jJ80vLbhWRq4HbcXqQ3QEE9b8fVdXfeLou1h8jwEXLIq+qeRH5G+AnQBL4qqr+ttV8jc6ydavTan/l2efw23vuIpkUUqlE0/LuF1Esr9OKuMcZba8n0I0i7WGj7JKd+XcnhD2u6Ho3Zb0eEmIbcYt62PzKpBMJigovmr+RB55eAkQT90r5UkE38XA39/J6kpg5jxOjc1l4wAsBOH6353josXlVZVi43xEs3O+zVfmXu1+mkgmWHHYy47vsQ35qO1qawyG3YxtjC5z9TI/MYWTRLhSyO8hObGX75ifIT+1AX/tuADbeeSMP/PBrNXX2uq+sJTGU5I6r/oMHfvJNUpmRiuinR8Z55b9ciYjw+59/l6f/eC9DI2OkR8ZID48xPD6Xg17yGgC2PPIA05MTpDOjZEadCdXSw6NkxubUzu7qM0Y9NCf05fzAP0rvN5GVd1tBeUDtaCLe9f3SxCH1fteBdkr95I7tzrCtIo6gJ5PV8g7+Ah8g7+5/14i9z7p+T8+86wd95yfvzjrRzt2wku+dIKpW7puXd780vS7w7UJVdwCLPN9d5vq3EvBiqqpeAlwSYjObRWRvShEXETkHeDJsGWMZR15Vf4TTKjH6lB9cezUA8+cvACCdTpJOJ9si7n5dZaJE21uNtEeJsrfcLUaE4XS4riBh+7D3sqy3KuqNJD0XIg9oPpoex7pJEX62Y39eOvYAmbRQKA35ElbanXX9b+y1y/zl3bue37ogvuetN62IVM6tofF5jM6bX52vq052PvxEdj3q5MC8DnzlX7D/S891ZlSe2k5+cgfF6UmSaac73y6HnsDQ6JzScmdCt2I+X3nJ9tnH/8hj629wlpXmmBiZt4j9Tj6bVDLBbVd9lkfW/aJq+3OX7sqbv7iGQlH58SfexZ8evNdpKAyPkM6MstOe+/LSv/4IAL+55ivseHYzQyOjDA07jYB5S3dl7xeeSDohbPz9PU49DI+QygyTGR5maGS0anveBgLUF3q/Yz6q0NdLA47UBz2pq5dnPaGH2i5RUdN7t3/tVz/H9772OT63+nIYylTLe59H3muXxSvvNe+ZlBvyUv7curxHua4akXgnTrT+ABF5AniYCC+/2syuBgBPPbmRQw8/ovJ5KJ0knU6EFnevtDv/rhb3RlLeSOqDpL0bwh42up6Q6gtmK7LeCVFvJaIex5jujUVZ2hJJjxp1rxedSiaEZ1ILAVg19gS/mV4B1Jd1qH/Td5a3tr5XAkTCicDM5/oiECaCl8kMg2uUK2+aXQ8/nl0PP746X1c+x7z5fRzz5veRTAjFYoH81CSFXLaSz9FvfA8Hn/EG8tlJitlJctlJUumZsfx3OXglmfF55LOT5Kac5YX8zBhED/3mFzz1h3vJT8/Mf7HHYavY+4Unkisq13zsPWx9yj1IG+y36hRe8MH3AfDZt51GdscE6aFh0pkM6cwwBxx7Kqe82Rn74dufeB8gDA0PkxoaJp0ZZo+Dj+TgY09CVVm/5lrSmWHnSUIp/U7LljN/p50pFovseH5rafkwaZeEuvFrSEDtmOFeyhNa+dEpoX9yw8PMmzePZDIFw85wxibu/su8aYPOV6chLk1H3b3X214fhlI10vCTPUFp+PaXiMgYkFDVbY3SuDGRN3j0YWeor1NecjrgdF/IpBKk08nQ4h5F2ht1j2lF2Ot1iWmXrNdbR4CRoXAR+bhEvR3R9M5IeuOIj0j7BL2ZR8d+2afEOXee1TH2TW/inuR+obfn17+1kbD7pWskAH55+d2go8q7X/n8ftNmBAKqj7NEIunM3pycmUl54W77sHC3fQLTHvnKt1Z95z1fz/24MzJcAiVXkv3KugnhrPf+G5PbniM3tYPcdJb89BQLluxSWeegE17mLM86DYx8doq0q+Gy4f67mJ7cTi6bJTc9RT47xXFnv5kDjjmRYi7LVR/7+5pyv/RNf80rLngvO57fyof+bGaAjWR6iKFMhjPfdjEnv/Z8nt30JJf9/dtIDWVIDQ0xlBkmlR7ixFefxwuOO5lnNz3FdV//AumhDEOZIaehMJTh0ONPYZe99mPbs1u4f90tZErpysuX7bEXY3PnMz01xbPbtjKSGSY1lGFoKEPC05goz2sQRF61rsw/9dgjLFiwwDmx0pmG0u73XTul3VmnsZC3W9ohWkM76XqqFpQfhDtXjfgRkUU4XXBOwBm68mbgn1R1S5j0JvIG3/nWNwAYGhqqfDeUTjI8XI6AhJP2qFH2uIS9XnS9VVlvJOpO2YMFX2QmEuJ3A+uEpLfS3aVTgh42Hwm5Xpm45LwR3pvm8HCK2/MHcWrxNuamppmUGZnzG++9Xln9X17zz6PRi25lUskEIv7vq7j3wy9dqPxDRPGakfd6+UVJHzj8Y2VdYWhkjKGRMWf90ve7HXyUZ/3SP6acYMipb/u7uvm/9/LqccRVFS0WSSYESQ/xgW/8nFx2itx0tvTEYIolu+7u5JkZ5jUXXUIuO8X09BS5rNMQWLZiX6fEiQSLdtmN/PQ0ueks2R3bmZh+hmxpzoKJ557htp/+D/npLLnpLMWCM8D+/J12Zpe99mPjw3/gSx/6m5oyX/SpL3PEiadx37pfcenF51ctSySTfODzV3LQymO5/abrueLfP0I6M0wqnSadHiI1NMTbP/QJlq/Yh3tu/SU3fO8q0kNDpNNDleVnvfWvmbdwMQ/+9k42PvpHXnDwQSDC0LBzP+rHvu311vNu37u81Sdj3jSCcz+vWSfGc7SXUKiaRK5PuAq4CXhN6fMbgW8BLwmT2ER+wFFVisUip7709Krvh9NJRksiHyXKHlbYW5H1epH1Toh6lGi6CAx5L8QRJb0dUfQ4XhiNS86jrAfR5bxVMY+2LSGZENLpBLnEOEzBSn7HbcPBQxUHSTkER9KddAENtyDJ9T4tEiGTClq3tZt6q+nbIe8QRuA96weWz/frwPyD8hERUumZLiOLl+8RmF9mZJQXnzMzh4D3GJ2/eCkXfGx1YDl22/cgPn3dHTOzxObz5HPTJFPO9vc86DA+etX1FPO50hODLLlslj0OcOZt2HXv/XnLB/+VYs61fDrL4mXOzKTj8xaw76Ermc5OUcjnyE1PU8hPV+R529ZnePj+u8lPT5MvLc9PZ3nZa9/CvIWL+e1ttzC1Yzu77rqrI5+ZksgPQLcYaL0rm2+eCWcG2XpzOPilK6f1EnYIXyMyC1X1o67P/ywirwqb2ER+wFl/mzNT3JErj676fmQoyY5ssqGw+wm5X3S9FVmvF1VvVdTDRCJbiaQLkAmKnjYh6K10cemknLdLzEWii3krUt4MSREymRTFovJkcReWTW8kk4l2qW30OLveJEDlMjTKVzyfA4/piA2GqPm0Ku1BeUB0cYfm5F3Ef1tBeTUsQ53ft5lJo8pUjRSWSlUkHiAzPMJue+8fmHbxsl156WveFLh8v8NWst9hKwN//+NedhbHvewspxw++/DyN/4FL3vtW3jo+m+WIvLOy631Iuvu7+OKrEdZr1dk3UvVeS7O53Y1pHuRYv9F5H8hIucC5ZmszgF+GDaxifyA8/Of/oRkMlkjLcPpJKOZVI2wh42utyLrYUW9k5Lut164CLpAuQ7bED1vJKmdFvP2R8v9X9qqv43mo0h1guWBpBLCWCZFQZUnhg5m2aaN7MRWdmQWN07cKO8QN9Mwv0E5UjfkE5Gvt416edc71gK7fUUU9np51ZXfiBF3J03gorrb6rS8N0oLzTf8KulDnEOhjrs6+aTSaVLp0uROpa41vdQFplVJh/aIelC6ctqEUHOex9F4NmLlL4G/Bf4bJ8aSALaLyN/ijHA5t15iE/kBZnp6GoBzXlc7ytFYJslULhlZ1lsR9SjR9Ga6u8Qr6OX09eV8WmAoUd0/sZ1i3i0p70SUXEQQoot5MzLuJmpZhxIJRisReEdM9n92PQ/s8fLgRDEQJWKWKN/gffrOVvJrdKw1WF6vPM2IeplmhB3aK+0SkH+z4g7dl3fojMD7lUkExsZm3tlqdQIl73rtjqT7pWnl5dLWoulCJuA8j/qECxofd91GNd4JoTqBqs5pJb2J/ADz0zU/BmCPPVfULJuTSTCVS1VOWr+oeiui3oqkh4mihxX0KC+INhM1F2r7yFdtp0Uxj1vK2y3kzUbHK/ddaU7Mm+1e4yZs2RMJYc5IujIE2jNLDmXhprsZHUpWKjjKy1hxR8TKx2q9PvKVbYdsHIR68tOCoIdK37DhUXdxw+3Xzb/0bkSzeYc5PlsV97DbiUvew+blVyYpvTAOnYugO981uPe0INnteG8kKF93HgkJfifNTbMNZKN1ROR44E5V3S4i5wFHApeq6oYw6U3kB5h7776TnZft4rtsOJVgTiZZufm1IuqtSHozgh6HnAflUe9G6HcBzrpeNIpDytsh5J2Q8Vai4u7yCc1JeSvda7w02hcRnK41JVkvLt0fNt3NoolHmFq8b2W9To2sECQGCXG60DUi6k08bMQubL6NBN3Jq0EeIcrUuCHQumxD65F2iE/aofPiDo3LJgLDw6lZETkPSh9Xd7NGDUgpCMM+DfZ2PL3qBVT7ctSaLwKHichhwPuAr+B0szkxTGIT+QFl67PPAnDW2X9eu1BgTiZZNetgq5IeVdDDRM+jvBAal5iHSecVzyEf8wtzYwzrnr0s4q1Gwv3K2nJUP0YSAQ3LOZlkZTZPABVhfOMdJJcfULVuNycuCbrBe2klGhdGwqu3FSHvkA2GcA2BkHkFbFNcy+KIrpfptLBD/NIOzV2jxkZnutbE1d8cekPKg/Kplxc0FzWXInXP8zgawUbL5FVVReQs4DOq+hUReUvDVCVM5AeU71/7HQDmzZ/vu3zME6lLBYg8VF/Qowp6mH7nUaLmgaN1xCDlfjS66YnURpGa2U51ntEurN3oihJXBNyv7K0IuZ94x0lChGRSmJNJVIl88eBTKN77M8ZTRSQ5c9nNFVoT+Va6gibq3ODbEXVrVgiiNwbCrx+lTPXOCZHgAEc9woh6o21X5Rfh+I7yDky7pL1el8SxTPU8JlXlaYOM1ytPnC9lQ3ve83DS1n6XyOP7ZL25/E3q28Q2EfkgcB7wYhFJUn7BKgQm8gPKn556kkMPPyJw+Uiq9pF71RjxEQU9jJzHIeaB0h+DkDfCfxu1o6xEkdyo0tqKgMch33FGvf2kWyQeGW+n0CdFGBtyzp9CaWp6li7n2Xsh+ejtjB14bGXdKCIfd/Q+mXPehekWrUpBsw2DqOdImO24I/Jh5byZ8rRL1KPmHbUOo/7WIlI1apqbuCS8mbygtXc52v0eh982ElkYTTcetaaZbfUqfTj85OuANwBvV9WnRGR34JNhE5vIDyCPPOzMQnjKS04PXCeTTFJQrZ6xMgY59+2jGKOU17t5Nbr3hJpZNKKEtyMi32x5/IhLvNshxv55SuzbirvLjYgwnvGIPDAxdxHTG//A0sNPACBfUIYbXIGbidi7t9monMPp7om8H3G8lFymFRGJKuMiTppmyh9FoCG6oDezjXaLOtQf8UsE5oz4ByQ7HQWH1gW80fbDbsfZVri6Lm+v0Xke9rfuZ7FvBRGZD3wZOARn4ti3qeqvfdZ7IbAWeJ2qXl367mLgHaV09wDnq+qUN62qPgV82vV5A/D1sGU0kR9AnnryCYZHRhgaGvJdLlRH5BtF28PKua/oB1xEmhXyuGQ8itzVfeTus7ybfbxjl+B2d1WJuWtN6O22sF+JBIyWzp+8S6qXv/BUHvnZtxlOJlCFesHwcrpG80iFlXbfckr9vrO9RlS5DkMcDYeUCIIETvxWj2akvLzNZmhmf5t9clJP1BuRECqN4ajliePlaIhPvJ3txSvLkSbci3Cet+Mc6zRtGH7yM8B1qnqOiAwBo94VSl1hPgH8xPXdcuDdwEGqOiki3wbOBS6Ps3BgIj+QrDruRaw67kV118l43/5vQcyDblbNCnlcIt7OfunuMoSJyNekjVGQ2yXbnRDqyrZK+yC0v/EQtiz1lo+mUuS1iLupPDpvAYe/+i/rdpEpi7l/E7u6YVCPMI+WEyI178JE3U4v0qzkhiFQvKV9fc79aLUB0g05j3a9TTA2VH9bcb7sDN15t6KVfCGceJefFkUtS2UbXb7edhMRmQu8GHgrgKpOA9M+q74L+C7wQs/3KWBERHI4DYCN7SinibzhS3kmuLBiHnSuNyvkcYp4O/ukQ32xC9unu9e7i/huo4sX+G7LfD0SIoykkhS0+keoCLzPb1Mvsp7XmeiSn+D7NgxCbEMkeI4Dv+30s9w3olVZkRB5xNltKI6XDluRcohnf/z24wWvuoDchrsZTSfaJtfQnnclvDQT4W62XoOOv0ZPi5p9KtSrKJGeVC4WkXWuz6tVdbXr817A08DXSkNDrgcuUtXt5RVKkfezgVNwibyqPiEinwI2AJPAGlVd08QuNcRE3vAllUjUHB1BF5ggUW4kk3H2SY8iri11m4ictrZPd7sku9Ny2xWZDtEw6vZ9KZGofqJVual4gt9BYx17b0JDLisPI+1hb2J5pO5kZV7cct/NYTN7kXIXunaP6tGqfHuJq3ER535nkkkKiZkXxt108r2HMq3UUasNxFYlOyVSt8HeiDgbnz3KZlVdWWd5Cmdypnep6q0i8hngA8A/uta5FHi/qhbcviIiC4CzgBXAVuA7InKeql7hWucenLaHL6p6aJidMJE3fHEumO7prv3XazyxRwPpaqOAt9rPOS7a3e+wF6LTnZJnadO24oxKJYrCUMrpBw8zY4h5u7uUew7UE++aZS63CTPpSb28iwLDKecWEFnMQ5S9nbifUrRCXGKcFyGTbDy5VlTaLVLtaHi03O1HnOBHUB/5sMTRJSTO60JcXVQij8kfkGY2DyWZb3FYXxePA4+r6q2lz1fjiLyblcBVJddZDJwpInmcS//Dqvo0gIhcAxwHXOFK+4rS/99Z+v9/l/7/RmBH2EKayBu+uPt1tyrjUUQzsqy3eB9u92grYbvWtLa9tmbfMnE/uu31R8EJEVKJBOqV3FK5vfKbxPnez4mDGgEQriFQxm+drMjMk4OSMzUzI2LcMh+mUZGU+KW5FQo0L7DdFKp2NxTqjTATREGbiyK3qy93u+qoXb+7t3EaRyNzACLzvpSGgnxMRPZX1QeAU4H7POusKP9bRC4H/ldVvycixwCrRGQUp2vNqcA6T9pHS+mOV9XjXYs+ICK/Av4pTDlN5A1fUokEMBP1CvOCX1ji7ofeyTyC8/b/vl0R5Jrt97jcRiWwG1edZb1CUbT0REt8JTeJUAwIKNfIf5mEf17l/Jy0wWVKU9sYEGpnHR5KRBPzgiopWvs9vI2HZl4O7za5iN2UeoFmJLsVwp63SRESEGkUoE43huLu4hREHNe6Ro3M2RaZVyXuUWveBXyjNGLNQ8D5InKhsy29LLgcequIXA3cDuSBO4DVAauPicgJqnozgIgcB4yFLaCJvOGL00Xeb6a8kOmbHWIxJknr1LUpjEB3U7J7XXq7SbuqJpGQioxK0X8jKkGyHCzsQXlV8mwg4AVPcpFaaVaFREgxLxa1qeOr5olETOLerW4+UBoZpA+EqFeuB/XKkUoI0kIUuWf2sUeu+/3YyOwlVPVOnO4zbnwFXlXf6vl8CXBJiM28HfiqiMwrfd4KvC1sGU3kDV9SiQTFQNmYobUXR5tO6p9fFy6cDd8RCLFOL9HzRZXeL2Oy1LWmqErCx0WKqhAgy8VicBeBYPkvS2xwxfjlK0jpyVspf9V6WdRsL4qAux07bEPBS6MhNdt5njVqJPTSed4r5YiC+9qdVCci3wn57PW6iqNxGGcjs9frq19R1fXAYaXhLkVVn4uS3kTe8MU578Xnuybza5Nk99qFpaY4fSCevUK4Y0R6vhtRsagkErXCWu5O49f4LZZEMVj8oRn5B0fQ/YKbIjOjgDRqCLi3BeH6I1c9IYjwkwV2IWoich9XkL5R48OpS4t6xkFSyhH5/q3PXrovhW1k9lKZW0HRUPNo9BIikgFeA+wJpMrvJaqq9ZE3mieREL+hqEPTqYtCv197el1KjeYoR7qLLpP0SnrVMh9RDCP+fvl6l9ez6PINo1FDoEy93g61Ah6+YeAm7EuLjboShSyCL8100+n3a1GvkExKbE84Zouctoo1Mnue7wPP4YxTn42a2ETe8CWVkKaiWd28bvamFPd+BLlMPxSzUy8Pt0RCKmUsS7jv8O+ek6XoOeGaFX+/vP3yEPEfGtVbjnpPAtyEbQy4adQNur5Qh+3LH2q1KqKPgCImjTFiTzjqE+VQk0F7KqxQiG/4yU6xq6qe3mxiE3mjBsGR4mYeT5m0VtMX4mnESkJqzwO3kgSdV/Wk373cWad2pXriP1MOd7lqJyvzbifK9uqt26j/fhDNNA68tNZYCIffi8P1aKZxMWjMdvnsXMPPGpl9wC0i8gJVvaeZxCbyRiBxSLlJbHR6YYKnXqbX66eo9UdzSSaDR6aBaNLvbC8gnwbiL9RG5P0lPLi/f6PtNsoXwnQNCqK5xoGXRtH3MA2GqF1BwgzI0s1ReLqN90Vso3mE8DOkzwYUKPZfS/kE4K0i8jBO1xoB1GZ2NVqiWwLe65LWDLNxn4xgEiJIgxGfUhKu61oj6YcZ8a/3BM1X/KX22Gwk/5X1QkT/q8sYviHgLUfYbVSlb7px4Ee4rkVxy1Lg6EWDIPgS7+zaA43VZT9wRiuJTeQNX0w+48fqNAZ85LN5QWsfjZ5mFYsauutAMmRf8DDSDzPi36jbV5iov/+6IcrgEu2wv1/UxkBN+piPG/c+lN836MyxWP/9iNlAXC+7GgNYl33UR15E5qrq88C2VvIxkTd8SUj0m5KJqg8+4mnES6/Vb1E1UJDLQhyl21qUd1XCSj/MvAsThkSEcjTq6x+0vpOmuah7MwLdasOgGud9g2b3pVXKddGLjdpmGaTuIO3G6rJn+SbwCpzRarxjACuwV5hMTOSNQHpNkAyjH6gnc2H93S3BYWU7SpS/TLR+3eEi/mWiyH8lTcRGQG265kW2VRn2dl8oNxQ6GS1vJgDTa1TmVLDuILExSHXZT+PIq+orSv9f0Uo+JvKGL4lEuBvQIF0gmsGJena7FLOHMPXZS90MwjaGmxV+J20pTdQof8Rh6VSbeETf7LHf5I3YEdkmt1mVR7RMhODfOpHscKS82L8BGOdpliAD9iSz3xtfRmuIyNeBXwK/VNXfRU1vIm8EYgJq9CPdPm6LxSZGbmlS+J20IctVE+UPN8dBOboV1auaEv8yrfyGsUTjIpa7R8SzqNr14z+IUIGhSh36D43abdol3O3c10FrFPUpl+OMXPNZEdkLuBO4SVU/EyZxSyIvIn8OfAQ4EDhaVde1kp9hGEa/E/Q0qxVZjyMPr7PXe9m1ma49lbRNij9QGcmn2QZAQbXlYXOLxeB3HIJoVJftFqlKd5QuClvDkYsiNjDa3SBp5sldfwpxbzaK2oZCodBDj2VDoKo/F5EbgRcCJwMXAgcD7Rd54F7g1cCXWszH6DEG6i33NtOtuhzkcai7TVlC2ikLcQh/UDeSZrr2VNI2Kf7Q/CRArTYAYOZ8abYh4E1X3q92DOVbU+cdvMYEzjUQYxnqdVWqR6SXpXv0yUU7GKR9jRsReQRnVJkCkFfVlZ7l84ArgN1xnPpTqvq10rKLgXfgvLh6D3C+qk75bONnwBjwa5wuNi9U1U1hy9iSyKvq/aVCtJKNYRhtYLY2xrz71csNlrA30G4Jf5R8gvKKKqqtij/UvkTbagMAWjtfykP8uY/FOGe59u5vJ+b5CDPRWNvwdAcJPUTpLL3mNcL62M+g7Rl+8mRV3Ryw7J3Afar6ZyKyE/CAiHwD2Al4N3CQqk6KyLeBc3G60Xi5GzgKOAR4DtgqIr9W1ckwhetYH3kRuQC4AGDp0qWsvfmGWPOfmJiIPc9BZWJigrW/urHbxZgVbLe6jBWrTye0Ewc7tk9w+62/jD/jmIm3WDHm5spqcscE9667uR1ZDxTl/Z7aMcEDd97S1bI0TY/9eNnJCf5419puF2M2o8AccSLa48AzQL60LAWMiEgOGAU2+magejGAiIwD5wNfA3YGMmEK0FDkReSnpQy9fEhVvx9mI6WCrgZWAxx51EpddcJJYZOGYu3NNxB3noPK2ptv4JjjT+x2MWYFa391I6usLmOjX+qzF54SNIryr1t7EytXvTh6vm3YtzjzbMfIc42Gs7tn3c28YOUJkfPtxGHS7WOxmaEAf3fHLRxwxHFtKE0wfTJiYWQeuPMW9j+8s3XZbSIcc4tFxP1u5+qSq7pRYI0403V/yWf554Af4Ej6HOB1qloEnhCRTwEbgElgjaqu8SuEiPwN8CKcqPyjwFdxutiEoqHIq+pLwmZmzB5EZDCmAjeMNhCmm0a7BatRt55mOyG02hWn3Xk22+2k3r2/cTeZ4BGA6klFHD1BGlVxu7rYhT1+43rnoBGtjh3eie5K7WC2NkA6xGZvn3cfjlfVjSKyBLheRH6nqje5lr8MZ5SZU4C9S+v8EkgCZwErgK3Ad0TkPFW9wmcbI8CngfWqmvdZXhcbftIIZFDffbAGjNEJoghWu6S/nX34ofvSHy3fSNmW8g6Rbwyj6NQjzst0lMOs197BifOdhFbo9GREjXa73ohKsxFVjXXUGlXdWPr/JhG5FjgacIv8+cDH1RGHB0XkYeAAYA/gYVV9GkBErgGOw3kx1ruNT7ZSxlaHnzwb+CxOp/4fisidqvqyVvI0jG4TZwNGYs7PizU6BoOw0tRt4Yfekv6o+UbNvyxIzchSWN+LQ1DDymU7LlXNHJLll4c7QdznTK80KNw0KlO/zITaaURkDEio6rbSv08D/smz2gbgVOCXIrIU2B94COcwXiUiozhda04F2jJEe6uj1lwLXBtTWQzDiEivPDWxBkVv0G3hh/ZH+aF9Uflm82+GVnwvqnfFJZfNCF9TVSmN08V1CHf7yUK332GA3mx8tEKMDZOlwLWl+2wK+KaqXiciFwKo6mXAR4HLReQeHHl/f2mEm80icjVwO87Lr3dQek80bqxrjWEYLdOpBoX3CYc1IJqjLC+NJKbfhb+yrTZF+5vdRqvba9W7mvWcdg6pGZVO+Xe7LzHtbki0+nSjFxoa3UJVHwIO8/n+Mte/N+JE6v3SXwJc0rYCljCRNwyjb+n0E4lBazj0U4QfOi/9cdDJBsDMNptO6tp+a+nrNwqCXx6uRzu6iHQjYN9Ll5luP7GIiirk8/01s2urmMgbhmGEpJmGwyDIfy8IP3Re+itIZ6L+blppcMSz/ZazAOIddaWdXUQ62Y88VncO0U2pHgNw+ep7TOQNwzDaSLNPDWZjA6AXhuUs0zXpp70v4LZr+27iLUvtd82OtNJO1+5UP/Jee/G0zwLyA4mJvGEYRg8StQEwW8S/V6L7bsKMye9eJ27xh+50wQmilxoCbtrh2p326vgbDI27KfVa46EVFKVQmD37EwYTecMwjFlAFPGfDdLfi8JfJkq0H9oj/mV6qQFQJkyZGq3TzvJVl6M9+faSO8+2UWsGDRN5wzCMAUNEQs1xMEjCD90boaOXxL9Mt/vgNyLOl5I71Shw0wl37qXGQsdQKHbiBOkhTOQNwzAMX8JG+WeD8ENvR/nd9KL4u+n1RoCXfm8UBJGQwZvZdRAxkTcMwzBaIozwzxbZB3/hD2oEdFv6Ibr4Q+flv0xCJPIoQG66LdLtGL602/vUbxQLFpE3DMMwjFgZNNkv0w9de/xoRv6hew2AMnGIdK+Jc6v7FJS+1/bTaA4TecMwDKMnGLSuPF76Vfrd9FP0P4i4ouq9LsqdnvysE6iq9ZE3DMMwjF5mUKP7bmaD9Jfxyr93OM969LKzDUqDwOguJvKGYRjGrMNkf4Yo0g+9L/5u+rULUBRaahB43jcYhEaB9ZE3DMMwjAFg0LvyBDGbxb9Msw2AMv3UEHAzG7vTDDom8oZhGIZRh0bCP+hqNAji72VQGwKDhog8AmwDCkBeVVd6lr8ReH/p4wTwV6p6V2nZxcA7AAXuAc5X1am4y2gibxiGYRgtYtH98IQRf+86s0H+3VhDoE0oaPwzYZ2sqpsDlj0MnKiqz4rIGcBq4BgRWQ68GzhIVSdF5NvAucDlcRfORN4wDMMwOoT13W+OQYz61yNsQ6Dei8PWGGgdVb3F9XEtsKvrcwoYEZEcMApsbEcZTOQNwzAMo4ew6H7rRBX/MrO9AeCm1acCvYiiFAqFsKsvFpF1rs+rVXV1TZawRkQU+JLPcjdvB34MoKpPiMingA3AJLBGVdeELVgUTOQNwzAMow8x4Y+fZhoAgyT/s4zN3j7vPhyvqhtFZAlwvYj8TlVv8q4kIifjiPwJpc8LgLOAFcBW4Dsicp6qXhHrHmAibxiGYRizGhP+9mLR/x5C4x1+UlU3lv6/SUSuBY4GqkReRA4FvgycoapbSl+/BHhYVZ8urXMNcBwQu8jPwgcrhmEYhmFERURC/RnxkBRp6s/oDCIyJiJzyv8GTgPu9ayzO3AN8CZV/b1r0QZglYiMinPSnArc345yWkTeMAzDMIzQRJF5i/LHT1SZd68/258CKEoxvrd4lwLXlo73FPBNVb1ORC4EUNXLgA8Di4AvlNbLq+pKVb1VRK4GbgfywB04I9rEjom8YRiGYRhtwbr19BbWDSg8qvoQcJjP95e5/v0OnLHi/dJfAlzStgKWMJE3DMMwDKOruIVfqN8AMOnvPH3TpSfmPvL9gIm8YRiGYRh9g0X5DWMGE3nDMAzDMGYd1pffGARM5A3DMAzDGGiijsZj4t+bxPyya19gIm8YhmEYhhEBi/YbvYKJvGEYhmEYRpsw6e8g9rKrYRiGYRiG0Q2si48RFRN5wzAMwzCMPqSR+HuH8pz14q9qEXnDMAzDMAxj9mER/9lHSyIvIp8E/gyYBv4InK+qW2Mol2EYhmEYhtFFoop/t1EYuFFrEi2mvx44RFUPBX4PfLD1IhmGYRiGYRiG0YiWIvKqusb1cS1wTmvFMQzDMAzDMIwmUIVCvtul6CgSV/8nEfkf4FuqekXA8guACwCWLl161JVXXhXLdstMTEwwPj4ea56DitVlfFhdxovVZ3xYXcaH1WW8WH3GRzvq8pRTTl6vqitjzTQmEvP30MyJ/xBq3akfXNiz+xGFhhF5EfkpsLPPog+p6vdL63wIyAPfCMpHVVcDqwGOPGqlrjrhpGbKG8jam28g7jwHFavL+LC6jBerz/iwuowPq8t4sfqMD6vL1hCRJLAOeEJVX+Gz/CTgUiANbFbVE0vfXwy8A6fb/j0475BOtaOMDUVeVV9Sb7mIvAV4BXCq2uvNhmEYhmEYRrcoFuLM7SLgfmCud4GIzAe+AJyuqhtEZEnp++XAu4GDVHVSRL4NnAtcHmfByrT0squInA68H3ilqu6Ip0iGYRiGYRiG0T1EZFfg5cCXA1Z5A3CNqm4AUNVNrmUpYEREUsAosLFd5Wx11JrPAXOA60XkThG5LIYyGYZhGIZhGEY0VKGQC/cHi0VknevvAk9ulwLvA4LGs9wPWCAiN4jIehF5s1MEfQL4FLABeBJ4zjM4TKy0OmrNPnEVxDAMwzAMwzA6xOagl11F5BXAJlVdX+oH70cKOAo4FRgBfi0ia4GngbOAFcBW4Dsicl7QYDCtYjO7GoZhGIZhGLMALUfbW+V44JUiciYwDMwVkStU9TzXOo/jNAa2A9tF5CbgsNKyh1X1aQARuQY4DmiLyLfatcYwDMMwDMMwZg2q+kFV3VVV98R5UfXnHokH+D7wIhFJicgocAzOi7EbgFUiMirO1Linlr5vCxaRNwzDMAzDMPof1bhHralCRC50NqOXqer9InIdcDdOP/ovq+q9pfWuBm7HGZr9DkrDr7cDE3nDMAzDMAzD8EFVbwBuKP37Ms+yTwKf9ElzCXBJB4pnIm8YhmEYhmHMBhQK+W4XoqNYH3nDMAzDMAzD6ENM5A3DMAzDMAyjD7GuNYZhGIZhGEb/o0ChfS+79iIWkTcMwzAMwzCMPsQi8oZhGIZhGEb/o7FNCNU3WETeMAzDMAzDMPoQi8gbhmEYhmEYs4D2TgjVi1hE3jAMwzAMwzD6EIvIG4ZhGIZhGP2P9ZE3DMMwDMMwDKMfsIi8YRiGYRiGMQtQKOS7XYiO0hWRTwiMpOPNU9qQ56BidRkfVpfxYvUZH1aX8WF1GS9Wn/FhdTn7sa41hmEYhmEYhuFBRJIicoeI/K/PMhGR/xSRB0XkbhE50rXsYhH5rYjcKyJXishwu8poIm8YhmEYhmH0P4oz/GSYv3BcBNwfsOwMYN/S3wXAFwFEZDnwbmClqh4CJIFzW9irupjIG4ZhGIZhGIYLEdkVeDnw5YBVzgK+rg5rgfkisqy0LAWMiEgKGAU2tqucXekjv379+s0i8mjM2S4GNsec56BidRkfVpfxYvUZH1aX8WF1GS9Wn/HRjrrcI+b8YkMnN/1k6o7PLg65+rCIrHN9Xq2qq12fLwXeB8wJSL8ceMz1+XFguaquE5FPARuASWCNqq4JWabIdEXkVXWnuPMUkXWqujLufAcRq8v4sLqMF6vP+LC6jA+ry3ix+oyPQatLVT09jnxE5BXAJlVdLyInBa3mVwQRWYATrV8BbAW+IyLnqeoVcZTNi3WtMQzDMAzDMIwZjgdeKSKPAFcBp4iIV8QfB3Zzfd4VpwvNS4CHVfVpVc0B1wDHtaugJvKGYRiGYRiGUUJVP6iqu6rqnjgvqv5cVc/zrPYD4M2l0WtWAc+p6pM4XWpWicioiAhwKsEvzLbMbJoQanXjVYyQWF3Gh9VlvFh9xofVZXxYXcaL1Wd8WF3GiIhcCKCqlwE/As4EHgR2AOeXlt0qIlcDtwN54A7a+DuIqrYrb8MwDMMwDMMw2oR1rTEMwzAMwzCMPsRE3jAMwzAMwzD6kFkl8iLySRH5XWmq3GtFZH63y9RviMjpIvJAacrhD3S7PP2KiOwmIr8QkftL0zRf1O0y9Tv1pso2oiEi80Xk6tL18n4RObbbZepXOjkV+2xERL4qIptE5F7XdwtF5HoR+UPp/wu6WcZ+IaAuzYtmObNK5IHrgUNU9VDg98AHu1yevkJEksDncaYdPgh4vYgc1N1S9S154O9U9UBgFfBOq8uWqTdVthGNzwDXqeoBwGFYvTZFp6din6VcDnjH/v4A8DNV3Rf4Wemz0ZjLqa1L86JZzqwSeVVdo6r50se1OGN6GuE5GnhQVR9S1WmcsVPP6nKZ+hJVfVJVby/9exuOKC3vbqn6lxBTZRshEZG5wIuBrwCo6rSqbu1qofqbjk3FPhtR1ZuAZzxfnwX8V+nf/wW8qpNl6lf86tK8aPYzq0Tew9uAH3e7EH2G73TDXSrLrEFE9gSOAG7tclH6mUtxpsoudrkcs4G9gKeBr5W6Kn1ZRMa6Xah+RFWfAMpTsT+JM45026ZiHyCWlsbjpvT/JV0uz2zBvGgW0nciLyI/LfVF9P6d5VrnQzhdG77RvZL2Jb7TDXe8FLMIERkHvgu8R1Wf73Z5+hH3VNndLsssIQUcCXxRVY8AtmNdF5rCMxX7LsCYiHgnjTGMrmNeNHvpuwmhVPUl9ZaLyFuAVwCnqg2SH5Wg6YaNJhCRNI7Ef0NVr+l2efqY8lTZZwLDwFwRucJnlj0jHI8Dj6tq+QnR1ZjIN0tlKnYAESlPxe6dyt2Ixp9EZJmqPikiy4BN3S5QP2NeNLvpu4h8PUTkdOD9wCtVdUe3y9OH3AbsKyIrRGQI56WtH3S5TH1JaVrmrwD3q+qnu12efibkVNlGSFT1KeAxEdm/9NWpwH1dLFI/09Gp2AeIHwBvKf37LcD3u1iWvsa8aPYzq2Z2FZEHgQywpfTVWlW9sItF6jtKUc9LcUZf+Kqq/kt3S9SfiMgJwC+Be5jp1/0Pqvqj7pWq/xGRk4D3quorulyUvkZEDsd5cXgIeAg4X1Wf7Wqh+hQR+b/A65iZiv0dqprtbqn6BxG5EjgJWAz8CbgE+B7wbWB3nMbSn6uq94VYw0NAXX4Q86JZzawSecMwDMMwDMMYFGZV1xrDMAzDMAzDGBRM5A3DMAzDMAyjDzGRNwzDMAzDMIw+xETeMAzDMAzDMPoQE3nDMAzDMAzD6ENM5A3DMAzDMAyjDzGRNwzDMAzDMIw+5P8Drc98iL+HOWsAAAAASUVORK5CYII=\n", + "text/plain": [ + "<Figure size 1008x288 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from py_wake.deflection_models import GCLHillDeflection\n", + "plot_deflection(GCLHillDeflection())" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1980,16 +2027,16 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<matplotlib.contour.QuadContourSet at 0x22980f59520>" + "<matplotlib.contour.QuadContourSet at 0x178e4681520>" ] }, - "execution_count": 49, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, @@ -2033,7 +2080,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -2060,7 +2107,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -2092,7 +2139,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -2128,7 +2175,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -2164,7 +2211,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -2199,7 +2246,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -2229,7 +2276,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -2249,16 +2296,16 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x22982eacf10>" + "<matplotlib.legend.Legend at 0x178869faee0>" ] }, - "execution_count": 57, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, @@ -2296,7 +2343,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 59, "metadata": { "scrolled": true }, @@ -2304,10 +2351,10 @@ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x22982f3ac40>" + "<matplotlib.legend.Legend at 0x178e8d1ed90>" ] }, - "execution_count": 58, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, @@ -2350,16 +2397,16 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<matplotlib.contour.QuadContourSet at 0x229f26edd90>" + "<matplotlib.contour.QuadContourSet at 0x17880091ac0>" ] }, - "execution_count": 59, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" }, diff --git a/py_wake/deficit_models/gaussian.py b/py_wake/deficit_models/gaussian.py index 0e8bf9bb42952331c80d89b83362db2ff6f44953..39aa7e5e8322615d798edd208ff08f3e5a91c146 100644 --- a/py_wake/deficit_models/gaussian.py +++ b/py_wake/deficit_models/gaussian.py @@ -20,15 +20,16 @@ class BastankhahGaussianDeficit(ConvectionDeficitModel): self.use_effective_ws = use_effective_ws ConvectionDeficitModel.__init__(self) - def k_ilk(self, **_): - return np.reshape(self._k, (1, 1, 1)) + def k_ilk(self, **kwargs): + shape = np.ones_like(kwargs.get('WS_ilk', np.ones((1, 1, 1))).shape) + return np.reshape(self._k, shape) def _calc_deficit(self, WS_ilk, WS_eff_ilk, D_src_il, dw_ijlk, ct_ilk, **kwargs): WS_ref_ilk = (WS_ilk, WS_eff_ilk)[self.use_effective_ws] sqrt1ct_ilk = np.sqrt(1 - ct_ilk) beta_ilk = 1 / 2 * (1 + sqrt1ct_ilk) / sqrt1ct_ilk - sigma_sqr_ijlk = (self.k_ilk(**kwargs)[:, na] * dw_ijlk / - D_src_il[:, na, :, na] + .2 * np.sqrt(beta_ilk)[:, na])**2 + k = self.k_ilk(WS_ilk=WS_ilk, **kwargs)[:, na] + sigma_sqr_ijlk = (k * dw_ijlk / D_src_il[:, na, :, na] + .2 * np.sqrt(beta_ilk)[:, na])**2 # maximum added to avoid sqrt of negative number radical_ijlk = np.maximum(0, (1. - ct_ilk[:, na] / (8. * sigma_sqr_ijlk))) deficit_centre_ijlk = WS_ref_ilk[:, na] * (1. - np.sqrt(radical_ijlk)) * (dw_ijlk > 0) @@ -51,7 +52,8 @@ class BastankhahGaussianDeficit(ConvectionDeficitModel): def wake_radius(self, D_src_il, dw_ijlk, ct_ilk, **kwargs): sqrt1ct_ilk = np.sqrt(1 - ct_ilk) beta_ilk = 1 / 2 * (1 + sqrt1ct_ilk) / sqrt1ct_ilk - sigma_ijlk = self.k_ilk(**kwargs)[:, na] * dw_ijlk / D_src_il[:, na, :, na] + .2 * np.sqrt(beta_ilk)[:, na] + sigma_ijlk = self.k_ilk(**kwargs)[:, na] * dw_ijlk / \ + D_src_il[:, na, :, na] + .2 * np.sqrt(beta_ilk)[:, na] return 2 * sigma_ijlk * D_src_il[:, na, :, na] def calc_deficit_convection(self, WS_ilk, WS_eff_ilk, D_src_il, dw_ijlk, cw_ijlk, ct_ilk, **kwargs): @@ -161,7 +163,7 @@ class IEA37SimpleBastankhahGaussianDeficit(BastankhahGaussianDeficit): def _calc_layout_terms(self, WS_ilk, D_src_il, dw_ijlk, cw_ijlk, **kwargs): eps = 1e-10 - sigma_ijlk = self.k_ilk(**kwargs) * dw_ijlk * (dw_ijlk > eps) + (D_src_il / np.sqrt(8.))[:, na, :, na] + sigma_ijlk = self.k_ilk(WS_ilk=WS_ilk) * dw_ijlk * (dw_ijlk > eps) + (D_src_il / np.sqrt(8.))[:, na, :, na] self.layout_factor_ijlk = WS_ilk[:, na] * (dw_ijlk > eps) * \ np.exp(-0.5 * (cw_ijlk / sigma_ijlk)**2) self.denominator_ijlk = 8. * (sigma_ijlk / D_src_il[:, na, :, na])**2 diff --git a/py_wake/deflection_models/__init__.py b/py_wake/deflection_models/__init__.py index b114353583891e45b8589965a3ffde27af397a89..d82672a07834e6d85408e4dcd5b1bb4bc33f2054 100644 --- a/py_wake/deflection_models/__init__.py +++ b/py_wake/deflection_models/__init__.py @@ -1,3 +1,4 @@ from .deflection_model import DeflectionModel from .jimenez import JimenezWakeDeflection from .fuga_deflection import FugaDeflection +from .gcl_hill_vortex import GCLHillDeflection diff --git a/py_wake/deflection_models/gcl_hill_vortex.py b/py_wake/deflection_models/gcl_hill_vortex.py new file mode 100644 index 0000000000000000000000000000000000000000..72c9a696c4e7ad64b1bd51b990cbc28587e0d154 --- /dev/null +++ b/py_wake/deflection_models/gcl_hill_vortex.py @@ -0,0 +1,102 @@ +from numpy import newaxis as na +import numpy as np +from py_wake.deflection_models import DeflectionModel + + +class GCLHillDeflection(DeflectionModel): + """Deflection based on Hill's ring vortex theory + + Implemented according to + Larsen, G. C., Ott, S., Liew, J., van der Laan, M. P., Simon, E., R.Thorsen, G., & Jacobs, P. (2020). + Yaw induced wake deflection - a full-scale validation study. + Journal of Physics - Conference Series, 1618, [062047]. + https://doi.org/10.1088/1742-6596/1618/6/062047 + """ + + def __init__(self, N=20, wake_deficitModel=None): + """ + Parameters + ---------- + N : int, optional + Number of logarithmic distributed downstream points included in the numerical integration + wake_deficitModel : WakeDeficitModel, optional + Wake deficit model used to calculate the center wake deficit needed by this model. + If None, the windFarmModel.wake_deficitModel is used + """ + self._wake_deficitModel = wake_deficitModel + self.N = N + + @property + def args4deflection(self): + return set(['D_src_il', 'yaw_ilk', 'ct_ilk', 'tilt_ilk']) | \ + set(self.wake_deficitModel.args4deficit) - {'dw_ijlk', 'hcw_ijlk', 'cw_ijlk', 'dh_ijlk'} + + @property + def wake_deficitModel(self): + return self._wake_deficitModel or self.windFarmModel.wake_deficitModel + + def calc_deflection(self, WS_eff_ilk, dw_ijl, hcw_ijl, dh_ijl, yaw_ilk, tilt_ilk, **kwargs): + + dw_lst = (np.logspace(0, 1.1, self.N) - 1) / (10**1.1 - 1) + dw_ijlkx = dw_ijl[:, :, :, na, na] * dw_lst[na, na, na, na, :] + z = np.zeros_like(dw_ijlkx) + U_w_ijlx = self.wake_deficitModel.calc_deficit( + WS_eff_ilk=(WS_eff_ilk * np.cos(np.deg2rad(yaw_ilk)))[..., na], + dw_ijlk=dw_ijlkx, hcw_ijlk=z, cw_ijlk=z, dh_ijlk=z, + tilt_ilk=tilt_ilk[..., na], + **{k: v[..., na] for k, v in kwargs.items()}) + + theta_yaw_ilk, theta_tilt_ilk = np.deg2rad(yaw_ilk), np.deg2rad(-tilt_ilk) + theta_ilk = np.sqrt(theta_yaw_ilk**2 + theta_tilt_ilk**2) + theta_deflection_ilk = np.arctan2(theta_tilt_ilk, theta_yaw_ilk) + + U_d_ijlkx = -0.4 * U_w_ijlx * np.sin(theta_ilk)[:, na, :, :, na] + U_a_ijlkx = WS_eff_ilk[:, na, :, :, na] - 0.4 * U_w_ijlx * np.cos(theta_ilk)[:, na, :, :, na] + + deflection_ijlk = np.trapz(U_d_ijlkx / U_a_ijlkx, dw_ijlkx, axis=4) + self.hcw_ijlk = hcw_ijl[..., na] - deflection_ijlk * np.cos(theta_deflection_ilk[:, na]) + self.dh_ijlk = dh_ijl[..., na] + deflection_ijlk * np.sin(theta_deflection_ilk[:, na]) + return dw_ijl[..., na], self.hcw_ijlk, self.dh_ijlk + + +def main(): + if __name__ == '__main__': + import matplotlib.pyplot as plt + from py_wake.deficit_models.gaussian import ZongGaussian + from py_wake.site.xrsite import UniformSite + from py_wake.examples.data.hornsrev1 import V80 + from py_wake.flow_map import XYGrid + from py_wake.turbulence_models.crespo import CrespoHernandez + from py_wake.deficit_models.gaussian import BastankhahGaussianDeficit + + site = UniformSite(p_wd=[1], ti=0.06) + x, y = [0], [0] # site.initial_position[:2].T + + wt = V80() + D = wt.diameter() + wfm = ZongGaussian(site, wt, deflectionModel=GCLHillDeflection(), + turbulenceModel=CrespoHernandez()) + wfm2 = ZongGaussian(site, wt, + deflectionModel=GCLHillDeflection(wake_deficitModel=BastankhahGaussianDeficit()), + turbulenceModel=CrespoHernandez()) + + ws = 10 + yaw = 30 + plt.figure(figsize=(12, 3)) + grid = XYGrid(x=np.linspace(-2 * D, D * 10, 100), y=np.linspace(-1.5 * D, 1.5 * D, 100)) + fm = wfm(x, y, yaw=yaw, wd=270, ws=ws).flow_map(grid) + fm.plot_wake_map(normalize_with=D) + center_line = fm.min_WS_eff() + plt.title(f'{wfm}, {yaw}') + plt.plot(center_line.x / D, center_line / D, label='Deflection centerline with ZongGaussianDeficit') + + fm = wfm2(x, y, yaw=yaw, wd=270, ws=ws).flow_map(grid) + center_line = fm.min_WS_eff() + plt.plot(center_line.x / D, center_line / D, label='Deflection centerline with BastankhahGaussianDeficit') + + plt.grid() + plt.legend() + plt.show() + + +main() diff --git a/py_wake/tests/test_deficit_models/test_deficit_models.py b/py_wake/tests/test_deficit_models/test_deficit_models.py index def097c1a95a312edea21b0832d8e7904998311d..e691ede5d828cfc15435c7f4899af9e4115cc799 100644 --- a/py_wake/tests/test_deficit_models/test_deficit_models.py +++ b/py_wake/tests/test_deficit_models/test_deficit_models.py @@ -2,7 +2,7 @@ import pytest import matplotlib.pyplot as plt import numpy as np -from py_wake.deficit_models.deficit_model import DeficitModel, WakeDeficitModel, BlockageDeficitModel +from py_wake.deficit_models.deficit_model import WakeDeficitModel, BlockageDeficitModel from py_wake.deficit_models.fuga import FugaDeficit, Fuga from py_wake.deficit_models.gaussian import BastankhahGaussianDeficit, IEA37SimpleBastankhahGaussianDeficit,\ ZongGaussianDeficit, NiayifarGaussianDeficit, BastankhahGaussian, IEA37SimpleBastankhahGaussian, ZongGaussian,\ @@ -186,6 +186,7 @@ def test_wake_radius(deficitModel, wake_radius_ref): npt.assert_array_almost_equal(deficitModel.wake_radius( D_src_il=np.reshape([100, 50, 100, 100, 100], (5, 1)), dw_ijlk=np.reshape([500, 500, 1000, 500, 500], (5, 1, 1, 1)), + WS_ilk=np.reshape([10, 10, 10, 10, 10], (5, 1, 1)), ct_ilk=np.reshape([.8, .8, .8, .4, .8], (5, 1, 1)), TI_ilk=np.reshape([.1, .1, .1, .1, .05], (5, 1, 1)), TI_eff_ilk=np.reshape([.1, .1, .1, .1, .05], (5, 1, 1)))[:, 0, 0, 0], diff --git a/py_wake/tests/test_deflection_models/test_hillvortex.py b/py_wake/tests/test_deflection_models/test_hillvortex.py new file mode 100644 index 0000000000000000000000000000000000000000..f2cfb59de984de3464bf0cf7c994bcabc419b830 --- /dev/null +++ b/py_wake/tests/test_deflection_models/test_hillvortex.py @@ -0,0 +1,108 @@ +import numpy as np +import matplotlib.pyplot as plt +from py_wake.deficit_models.gaussian import BastankhahGaussian, ZongGaussian, ZongGaussianDeficit +from py_wake.deflection_models import GCLHillDeflection +from py_wake.examples.data.hornsrev1 import V80 +from py_wake.flow_map import XYGrid +from py_wake.site.xrsite import UniformSite +from py_wake.turbulence_models.crespo import CrespoHernandez +from py_wake.wind_turbines._wind_turbines import WindTurbine +from py_wake.wind_turbines.power_ct_functions import PowerCtFunction +from py_wake.tests import npt +from py_wake.tests.check_speed import timeit + + +def test_torque_result(): + """Reproduce case from + + Larsen, G. C., Ott, S., Liew, J., van der Laan, M. P., Simon, E., R.Thorsen, G., & Jacobs, P. (2020). + Yaw induced wake deflection - a full-scale validation study. + Journal of Physics - Conference Series, 1618, [062047]. + https://doi.org/10.1088/1742-6596/1618/6/062047 + + Note that the implementation used in the paper differs from the actual implementation by: + - using rotor average deficit instead of peak deficit + - using ainslie deficit model + static "meander distribution" + - dismiss Ua (downwind distance reduction) term in integration formula + Hence the result is not expected to match + """ + site = UniformSite(p_wd=[1], ti=0.06) + x, y = [0], [0] + + def power_ct_function(ws, yaw, run_only): + return (np.zeros_like(ws), np.where(yaw == 17.5, .86, 0.83))[run_only] + D = 52 + v52 = WindTurbine(name="V52", diameter=D, hub_height=44, + powerCtFunction=PowerCtFunction(input_keys=['ws', 'yaw'], + power_ct_func=power_ct_function, + power_unit='w', additional_models=[])) + + wfm = ZongGaussian(site, v52, + deflectionModel=GCLHillDeflection(), + turbulenceModel=CrespoHernandez()) + x_ref = np.arange(2, 12, 2) + positive_ref = [-0.2, -.35, -.45, -.55, -.65] + negative_ref = [.15, .3, .4, .45, .5] + for ws, yaw, ref in [(9.48, 17.5, positive_ref), (9.73, -14.5, negative_ref)]: + plt.figure(figsize=(12, 3)) + + grid = XYGrid(x=np.linspace(-2 * D, D * 10, 100), y=np.linspace(-1.5 * D, 1.5 * D, 100)) + fm = wfm(x, y, yaw=yaw, wd=270, ws=ws).flow_map(grid) + fm.plot_wake_map(normalize_with=D) + center_line = fm.min_WS_eff() + plt.title(f'Yaw {yaw}deg') + plt.plot(center_line.x / D, center_line / D, label='Centerline') + plt.plot(x_ref, ref) + npt.assert_allclose(center_line.interp(x=x_ref * D) / D, ref, atol=.1) + + plt.grid() + plt.legend() + if 0: + plt.show() + + +def test_N(): + + site = UniformSite(p_wd=[1], ti=0.06) + x, y = [0], [0] + wt = V80() + D = wt.diameter() + + plt.figure(figsize=(12, 3)) + grid = XYGrid(x=[10 * D, 20 * D], y=np.linspace(-1.5 * D, 1.5 * D, 100)) + + def deflection_20d(N): + wfm = ZongGaussian(site, wt, deflectionModel=GCLHillDeflection(N=N), + turbulenceModel=CrespoHernandez()) + fm = wfm(x, y, yaw=30, wd=270, ws=10).flow_map(grid) + return fm.min_WS_eff().values[-1] + + N_lst = [10, 20, 100] + res = [timeit(deflection_20d, min_runs=10)(N) for N in N_lst] + plt.plot(N_lst, [d for d, _ in res]) + plt.ylabel('Deflection 20D downstream [m]') + plt.xlabel('N') + ax = plt.twinx() + ax.plot(N_lst, [np.mean(t) for _, t in res], '--') + ax.set_ylabel('Time [s]') + + assert np.abs(res[-1][0] - res[1][0]) < .3 # Mismatch between N=20 and 100 is less than 30cm 20D downstream + if 0: + plt.show() + + +def test_wake_deficitModel_input(): + site = UniformSite(p_wd=[1], ti=0.06) + x, y = [0], [0] + + wt = V80() + D = wt.diameter() + wfm1 = ZongGaussian(site, wt, deflectionModel=GCLHillDeflection(), + turbulenceModel=CrespoHernandez()) + wfm2 = BastankhahGaussian(site, wt, + deflectionModel=GCLHillDeflection(wake_deficitModel=ZongGaussianDeficit()), + turbulenceModel=CrespoHernandez()) + + grid = XYGrid(x=np.linspace(10, D * 10, 100), y=np.linspace(-1.5 * D, 1.5 * D, 100)) + cl1, cl2 = [wfm(x, y, yaw=30, wd=270, ws=10).flow_map(grid).min_WS_eff() for wfm in [wfm1, wfm2]] + npt.assert_array_almost_equal(cl1, cl2, 3) diff --git a/py_wake/wind_farm_models/engineering_models.py b/py_wake/wind_farm_models/engineering_models.py index 20be91100a1a16c7a929a454f1a566cdc0351a35..5beb583a4f92af09e87db5c9a1f99baf6ef6162d 100644 --- a/py_wake/wind_farm_models/engineering_models.py +++ b/py_wake/wind_farm_models/engineering_models.py @@ -50,15 +50,20 @@ class EngineeringWindFarmModel(WindFarmModel): groundModel=None): WindFarmModel.__init__(self, site, windTurbines) - check_model(wake_deficitModel, WakeDeficitModel, 'wake_deficitModel') - check_model(rotorAvgModel, RotorAvgModel, 'rotorAvgModel') - check_model(superpositionModel, SuperpositionModel, 'superpositionModel') - check_model(blockage_deficitModel, BlockageDeficitModel, 'blockage_deficitModel') - check_model(deflectionModel, DeflectionModel, 'deflectionModel') - check_model(turbulenceModel, TurbulenceModel, 'turbulenceModel') if groundModel is None: groundModel = NoGround() - check_model(groundModel, GroundModel, 'groundModel') + for model, cls, name in [(wake_deficitModel, WakeDeficitModel, 'wake_deficitModel'), + (rotorAvgModel, RotorAvgModel, 'rotorAvgModel'), + (superpositionModel, SuperpositionModel, 'superpositionModel'), + (blockage_deficitModel, BlockageDeficitModel, 'blockage_deficitModel'), + (deflectionModel, DeflectionModel, 'deflectionModel'), + (turbulenceModel, TurbulenceModel, 'turbulenceModel'), + (groundModel, GroundModel, 'groundModel')]: + check_model(model, cls, name) + if model is not None: + setattr(model, 'windFarmModel', self) + setattr(self, name, model) + if isinstance(superpositionModel, WeightedSum): assert isinstance(wake_deficitModel, ConvectionDeficitModel) assert rotorAvgModel.__class__ is RotorCenter, "Multiple rotor average points not implemented for WeightedSum"