diff --git a/docs/operation_models_user.ipynb b/docs/operation_models_user.ipynb index 6ad796d68..e57c1bd99 100644 --- a/docs/operation_models_user.ipynb +++ b/docs/operation_models_user.ipynb @@ -48,12 +48,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "2275840e-48a3-41d2-ace9-fad05da0dc02", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mturbine_type has been changed without specifying a new reference_wind_height. reference_wind_height remains 90.00 m. Consider calling `FlorisModel.assign_hub_height_to_ref_height` to update the reference wind height to the turbine hub height.\u001b[0m\n", + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mturbine_type has been changed without specifying a new reference_wind_height. reference_wind_height remains 90.00 m. Consider calling `FlorisModel.assign_hub_height_to_ref_height` to update the reference wind height to the turbine hub height.\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "simple operation model powers [kW]: [[1753.95445918 436.4427005 506.66815478]]\n", + "cosine-loss operation model powers [kW]: [[1561.31837381 778.04338242 651.77709894]]\n" + ] + }, + { + "data": { + "image/png": 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", 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Nkk78Fd2///1vHT161CfwSN99X+tyueRyuRQWFqbY2Nhm2yXJ5XI1u31TGwBfrXHcAbDXFsdcWVmZFi9erPHjx7eolhYnn3HjxqmkpKSlm+EU3HLLLZK++1mVr7/+2s/VAAAQ+D755BP9/Oc/1+zZs5WTk9Oiba2f09SkuLhY1157rd566y1lZGSoW7duPu2PPPJIS3eJE8jIyNAFF1yg9evXa8mSJZowYYJPe+/evRUSEnLcXTk1NTVKSkpSUlKS6uvrVVtb65PAm9olKSkpyecOhKb2pjYAvlrjuANgr7WPueuuu07jx4/Xvffe2+JaWnymqbi4WP/4xz9UU1OjDRs2qLKy0rtUVVW1uACc3Ml+ViUsLExZWVlasWKFd11jY6NWrFghp9OprKwsdevWzad9y5Yt2rlzp5xOpyTJ6XRqw4YN3jsQJKm0tFQOh0ODBg1qq2EBAas1jjsA9lrrmPv0008lSaNHj9aDDz54asW09Mr22NhYs2DBgpZu1uE8+eSTpk+fPiY8PNwMGTLErFmzxmq79rp7rkl1dbUJDg42ksy2bduOa1+0aJEJDw83CxcuNJ988okZP368iY2N9d4NN2HCBJOWlmZWrlxp1q1bZ5xOp3E6nd7tjxw5YtLT001OTo6pqqoyb731lomPjzdFRUXtMj6gozrZ3XOne9wZY8y2bdtMZWWluf32280555xjKisrTWVlJXetoks70XF3usfchg0bTO/evY0ks3XrVrNnzx6zZ88es3fv3hbV1+LQlJiYaLZu3drSzTqURYsWmbCwMPP888+bTZs2mdtuu83ExsaampqaH9y2vUOTMcbk5OQYSWb27NnNtj/xxBMmLS3NhIWFmSFDhpjVq1d727799ltz5513mp49e5rIyEhz/fXXmz179vhsv2PHDnP11Veb7t27m969e5upU6eahoaGthwS0OGdLDQZc/rH3RVXXOHd/7HL9u3b23poQId1suPudI652bNnN3u89enTp0X1BRnTst/pKC4u1p49ewL6QZZDhw7VxRdf7H2oVWNjo1JTU3XXXXdpxowZJ93W4/EoJiZGbrdbDoejPcrVSy+9pIKCAp199tnatm2bgoKC2uV9ga7sm2++UXR0tCTp4MGDioqK8nNFQOfXlsdda3x+t/hC8LVr12rlypVatmyZzjvvvOMuBH/ttddOqZD20vS8h6KiIu+6Y5/38H11dXWqq6vzvvZ4PO1S57GO/VmV8vJyDR8+vN1rAACgq2vxheCxsbG64YYbdMUVV6h3796KiYnxWTq6H3rew/cVFxf7jC81NbW9SvWKiorSqFGjJP3wz6oAAIC20eIzTQsWLGiLOjqsoqIiTZkyxfva4/H4JTgVFBToz3/+sxYvXqzHHntM4eHh7V4DAABdWZd7rPcPPe/h+8LDw+VwOHwWf7jqqqu8P6vy97//3S81AADQlVmFposuukj79++33umll16q3bt3n3JRbemHnvfQUYWEhCg/P18SX9EBAOAPVl/PVVVVaf369dY/bFdVVeVz8XRHM2XKFI0ZM0aDBw/WkCFD9Nhjj+mbb77Rb37zG3+XdlIFBQV6+OGH9fe//11fffWV4uLi/F0SAABdhvU1TSNGjJDt0wk6+i3xN954o/bt26dZs2bJ5XIpMzNTb7311nEXh3c0GRkZyszMVFVVlZYsWaI77rjD3yUBANBlWD2n6csvv2zxjlNSUhQSEnJKRXVk/nhO07EeeeQRTZ06VcOGDWv2EQkAWgfPaQLaX0d/TlOLH27Z1fk7NO3Zs0cpKSlqbGzU1q1b1b9//3avAegKCE1A++vooanL3T0X6M444wz95Cc/kfTdk8IBAED7IDQFoIKCAknfhSZOFAIA0D4ITQFo5MiRioqK0hdffKGysjJ/lwMAQJdAaApAUVFR+sUvfiGJZzYBANBeWhyaxowZo/fff78takELNH1Ft3jxYh0+fNjP1QAA0Pm1ODS53W5lZ2erf//+euihhzrsk787uyuvvFJnnnmmamtr+VkVAADaQYtD09KlS7V7927dcccdWrx4sfr27aurr75af/nLX9TQ0NAWNaIZ/KwKAADt65SuaYqPj9eUKVO0fv16rVmzRj/60Y9UUFCg5ORk3X333dq2bVtr14lmNH1Ft3z5cn311Vd+rgYAgM7ttC4E37Nnj0pLS1VaWqqQkBBdc8012rBhgwYNGqRHH320tWrECaSnpyszM1MNDQ1avHixv8sBAKBTa3Foamho0F//+ldde+216tOnj0pKSjR58mRVV1frhRde0DvvvKMlS5bogQceaIt68T233HKLJOnPf/6znysBAKBzs/7B3iZnnHGGGhsbNXr0aK1du1aZmZnH9bnqqqsUGxvbCuXhh4wePVr/+Z//qTVr1mjr1q0655xz/F0SAACdUovPND366KOqrq7WU0891WxgkqTY2Fht3779dGuDhaSkJOXk5EjiZ1UAAGhLLQ5NBQUFioiIaItacIr4WRUAANoeTwTvBEaOHKnU1FRlZ2fr4MGD/i4HAIBOqcXXNKHjiYyM1I4dOxQcTAYGAKCtEJo6CQIT0LoiIyO9Z24jIyP9XA3QNXT0447QBADNCAoKUlRUlL/LALqUjn7ccXoCAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAAqEJAADAQkCEph07dmjcuHHq16+funfvrrPPPluzZ89WfX29T7+PP/5Yl112mSIiIpSamqq5c+cet6+SkhINHDhQERERysjI0PLly9trGAAAIIAFRGjavHmzGhsb9eyzz2rTpk169NFH9cwzz+iee+7x9vF4PMrJyVGfPn1UUVGhhx9+WPfff7+ee+45b5+ysjKNHj1a48aNU2VlpUaOHKmRI0dq48aN/hgWAAAIIEHGGOPvIk7Fww8/rPnz5+uLL76QJM2fP18zZ86Uy+VSWFiYJGnGjBlaunSpNm/eLEm68cYb9c0332jZsmXe/QwbNkyZmZl65plnmn2furo61dXVeV97PB6lpqbK7XbL4XC01fAAAEAr8ng8iomJOa3P74A409Qct9utXr16eV+Xl5fr8ssv9wYmScrNzdWWLVu0f/9+b5/s7Gyf/eTm5qq8vPyE71NcXKyYmBjvkpqa2sojAQAAgSAgQ9Nnn32mJ554Qrfffrt3ncvlUmJiok+/ptcul+ukfZram1NUVCS32+1ddu3a1VrDAAAAAcSvoWnGjBkKCgo66dL01VqT3bt366c//al++ctf6rbbbmvzGsPDw+VwOHwWAADQ9YT6882nTp2qsWPHnrTPWWed5f13dXW1rrrqKg0fPtznAm9JSkpKUk1Njc+6ptdJSUkn7dPUDgAAcCJ+DU3x8fGKj4+36rt7925dddVVysrK0oIFCxQc7HuSzOl0aubMmWpoaFC3bt0kSaWlpRowYIB69uzp7bNixQpNnjzZu11paamcTmfrDAgAAHRaAXFN0+7du3XllVcqLS1Nf/jDH7Rv3z65XC6fa5FuvvlmhYWFady4cdq0aZMWL16sxx9/XFOmTPH2mTRpkt566y398Y9/1ObNm3X//fdr3bp1mjhxoj+GBQAAAohfzzTZKi0t1WeffabPPvtMKSkpPm1NT0yIiYnR22+/rcLCQmVlZal3796aNWuWxo8f7+07fPhwvfLKK7r33nt1zz33qH///lq6dKnS09PbdTwAACDwBOxzmvylNZ7zAAAA2leXfk4TAABAeyI0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWCA0AQAAWAi40FRXV6fMzEwFBQWpqqrKp+3jjz/WZZddpoiICKWmpmru3LnHbV9SUqKBAwcqIiJCGRkZWr58eTtVDgAAAlnAhabf/e53Sk5OPm69x+NRTk6O+vTpo4qKCj388MO6//779dxzz3n7lJWVafTo0Ro3bpwqKys1cuRIjRw5Uhs3bmzPIQAAgAAUZIwx/i7C1ptvvqkpU6bor3/9q8477zxVVlYqMzNTkjR//nzNnDlTLpdLYWFhkqQZM2Zo6dKl2rx5syTpxhtv1DfffKNly5Z59zls2DBlZmbqmWeesarB4/EoJiZGbrdbDoejdQcIAADaRGt8fgfMmaaamhrddtttevHFFxUZGXlce3l5uS6//HJvYJKk3NxcbdmyRfv37/f2yc7O9tkuNzdX5eXlJ3zfuro6eTwenwUAAHQ9ARGajDEaO3asJkyYoMGDBzfbx+VyKTEx0Wdd02uXy3XSPk3tzSkuLlZMTIx3SU1NPZ2hAACAAOXX0DRjxgwFBQWddNm8ebOeeOIJHThwQEVFRe1eY1FRkdxut3fZtWtXu9cAAAD8L9Sfbz516lSNHTv2pH3OOussrVy5UuXl5QoPD/dpGzx4sPLz8/XCCy8oKSlJNTU1Pu1Nr5OSkrz/21yfpvbmhIeHH/e+AACg6/FraIqPj1d8fPwP9ps3b57++7//2/u6urpaubm5Wrx4sYYOHSpJcjqdmjlzphoaGtStWzdJUmlpqQYMGKCePXt6+6xYsUKTJ0/27qu0tFROp7MVRwUAADojv4YmW2lpaT6vo6OjJUlnn322UlJSJEk333yz/uu//kvjxo3T9OnTtXHjRj3++ON69NFHvdtNmjRJV1xxhf74xz8qLy9PixYt0rp163weSwAAANCcgLgQ3EZMTIzefvttbd++XVlZWZo6dapmzZql8ePHe/sMHz5cr7zyip577jldcMEF+stf/qKlS5cqPT3dj5UDAIBAEFDPaeoIeE4TAACBp0s9pwkAAMCfCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWCE0AAAAWQv1dQKAxxkiSPB6PnysBAAC2mj63mz7HTwWhqYUOHDggSUpNTfVzJQAAoKUOHDigmJiYU9o2yJxO5OqCGhsbVV1drR49eigoKKjd3tfj8Sg1NVW7du2Sw+Fot/cNBMxN85iX5jEvJ8bcNI95aV6gzYsxRgcOHFBycrKCg0/t6iTONLVQcHCwUlJS/Pb+DocjIP7P6Q/MTfOYl+YxLyfG3DSPeWleIM3LqZ5hasKF4AAAABYITQAAABYITQEiPDxcs2fPVnh4uL9L6XCYm+YxL81jXk6MuWke89K8rjgvXAgOAABggTNNAAAAFghNAAAAFghNAAAAFghNAAAAFghNAeKpp55S3759FRERoaFDh2rt2rX+LqlNFRcX6+KLL1aPHj2UkJCgkSNHasuWLT59Dh8+rMLCQsXFxSk6OlqjRo1STU2NT5+dO3cqLy9PkZGRSkhI0LRp03TkyJH2HEqbmjNnjoKCgjR58mTvuq46L7t379avf/1rxcXFqXv37srIyNC6deu87cYYzZo1S2eccYa6d++u7Oxsbdu2zWcfX3/9tfLz8+VwOBQbG6tx48bp4MGD7T2UVnX06FHdd9996tevn7p3766zzz5bv//9731+f6srzM3777+vn/3sZ0pOTlZQUJCWLl3q095ac/Dxxx/rsssuU0REhFJTUzV37ty2HtppOdm8NDQ0aPr06crIyFBUVJSSk5N1yy23qLq62mcfnXFeTsigw1u0aJEJCwszzz//vNm0aZO57bbbTGxsrKmpqfF3aW0mNzfXLFiwwGzcuNFUVVWZa665xqSlpZmDBw96+0yYMMGkpqaaFStWmHXr1plhw4aZ4cOHe9uPHDli0tPTTXZ2tqmsrDTLly83vXv3NkVFRf4YUqtbu3at6du3rzn//PPNpEmTvOu74rx8/fXXpk+fPmbs2LFmzZo15osvvjD/+Mc/zGeffebtM2fOHBMTE2OWLl1q1q9fb6677jrTr18/8+2333r7/PSnPzUXXHCBWb16tfnnP/9pfvSjH5nRo0f7Y0it5sEHHzRxcXFm2bJlZvv27aakpMRER0ebxx9/3NunK8zN8uXLzcyZM81rr71mJJnXX3/dp7015sDtdpvExESTn59vNm7caF599VXTvXt38+yzz7bXMFvsZPNSW1trsrOzzeLFi83mzZtNeXm5GTJkiMnKyvLZR2eclxMhNAWAIUOGmMLCQu/ro0ePmuTkZFNcXOzHqtrX3r17jSSzatUqY8x3B3O3bt1MSUmJt8+nn35qJJny8nJjzHd/DIKDg43L5fL2mT9/vnE4HKaurq59B9DKDhw4YPr3729KS0vNFVdc4Q1NXXVepk+fbi699NITtjc2NpqkpCTz8MMPe9fV1taa8PBw8+qrrxpjjPnkk0+MJPPhhx96+7z55psmKCjI7N69u+2Kb2N5eXnm1ltv9Vl3ww03mPz8fGNM15yb74eD1pqDp59+2vTs2dPnOJo+fboZMGBAG4+odTQXJr9v7dq1RpL58ssvjTFdY16OxddzHVx9fb0qKiqUnZ3tXRccHKzs7GyVl5f7sbL25Xa7JUm9evWSJFVUVKihocFnXgYOHKi0tDTvvJSXlysjI0OJiYnePrm5ufJ4PNq0aVM7Vt/6CgsLlZeX5zN+qevOy//+7/9q8ODB+uUvf6mEhARdeOGF+tOf/uRt3759u1wul8+8xMTEaOjQoT7zEhsbq8GDB3v7ZGdnKzg4WGvWrGm/wbSy4cOHa8WKFdq6daskaf369frggw909dVXS+rac9OkteagvLxcl19+ucLCwrx9cnNztWXLFu3fv7+dRtO23G63goKCFBsbK6nrzQs/2NvB/fvf/9bRo0d9PuAkKTExUZs3b/ZTVe2rsbFRkydP1iWXXKL09HRJksvlUlhYmPfAbZKYmCiXy+Xt09y8NbUFqkWLFumjjz7Shx9+eFxbV52XL774QvPnz9eUKVN0zz336MMPP9Rvf/tbhYWFacyYMd5xNTfuY+clISHBpz00NFS9evUK2HmRpBkzZsjj8WjgwIEKCQnR0aNH9eCDDyo/P1+SuvTcNGmtOXC5XOrXr99x+2hq69mzZ5vU314OHz6s6dOna/To0d4f6O1q80JoQodXWFiojRs36oMPPvB3KX63a9cuTZo0SaWlpYqIiPB3OR1GY2OjBg8erIceekiSdOGFF2rjxo165plnNGbMGD9X519LlizRyy+/rFdeeUXnnXeeqqqqNHnyZCUnJ3f5uYG9hoYG/epXv5IxRvPnz/d3OX7D13MdXO/evRUSEnLc3U81NTVKSkryU1XtZ+LEiVq2bJneffddpaSkeNcnJSWpvr5etbW1Pv2PnZekpKRm562pLRBVVFRo7969uuiiixQaGqrQ0FCtWrVK8+bNU2hoqBITE7vkvJxxxhkaNGiQz7pzzz1XO3fulPT/4zrZcZSUlKS9e/f6tB85ckRff/11wM6LJE2bNk0zZszQTTfdpIyMDBUUFOjuu+9WcXGxpK49N01aaw4647El/X9g+vLLL1VaWuo9yyR1vXkhNHVwYWFhysrK0ooVK7zrGhsbtWLFCjmdTj9W1raMMZo4caJef/11rVy58rhTu1lZWerWrZvPvGzZskU7d+70zovT6dSGDRt8DuimA/77H7CBYsSIEdqwYYOqqqq8y+DBg5Wfn+/9d1ecl0suueS4R1Js3bpVffr0kST169dPSUlJPvPi8Xi0Zs0an3mpra1VRUWFt8/KlSvV2NiooUOHtsMo2sahQ4cUHOz7pz4kJESNjY2SuvbcNGmtOXA6nXr//ffV0NDg7VNaWqoBAwYE1FdQx2oKTNu2bdM777yjuLg4n/YuNy/+vhIdP2zRokUmPDzcLFy40HzyySdm/PjxJjY21ufup87mjjvuMDExMea9994ze/bs8S6HDh3y9pkwYYJJS0szK1euNOvWrTNOp9M4nU5ve9Ot9Tk5Oaaqqsq89dZbJj4+PqBvrW/OsXfPGdM152Xt2rUmNDTUPPjgg2bbtm3m5ZdfNpGRkeall17y9pkzZ46JjY01f/vb38zHH39sfv7znzd7S/mFF15o1qxZYz744APTv3//gLqtvjljxowxZ555pveRA6+99prp3bu3+d3vfuft0xXm5sCBA6aystJUVlYaSeaRRx4xlZWV3rvAWmMOamtrTWJioikoKDAbN240ixYtMpGRkR361vqTzUt9fb257rrrTEpKiqmqqvL5W3zsnXCdcV5OhNAUIJ544gmTlpZmwsLCzJAhQ8zq1av9XVKbktTssmDBAm+fb7/91tx5552mZ8+eJjIy0lx//fVmz549PvvZsWOHufrqq0337t1N7969zdSpU01DQ0M7j6ZtfT80ddV5eeONN0x6eroJDw83AwcONM8995xPe2Njo7nvvvtMYmKiCQ8PNyNGjDBbtmzx6fPVV1+Z0aNHm+joaONwOMxvfvMbc+DAgfYcRqvzeDxm0qRJJi0tzURERJizzjrLzJw50+dDryvMzbvvvtvs35QxY8YYY1pvDtavX28uvfRSEx4ebs4880wzZ86c9hriKTnZvGzfvv2Ef4vfffdd7z4647ycSJAxxzwWFgAAAM3imiYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAAAALhCYAXdaOHTsUFBSkoKAgZWZmnvb+mvYVGxt72vsC0PEQmgB0ee+8847Pj7Weqj179uixxx47/YIAdEiEJgBdXlxc3HG/3n4qkpKSFBMT0woVAeiICE0AOoV9+/YpKSlJDz30kHddWVmZwsLCWnwWaezYsRo5cqQeeughJSYmKjY2Vg888ICOHDmiadOmqVevXkpJSdGCBQtaexgAOrBQfxcAAK0hPj5ezz//vEaOHKmcnBwNGDBABQUFmjhxokaMGNHi/a1cuVIpKSl6//339a9//Uvjxo1TWVmZLr/8cq1Zs0aLFy/W7bffrp/85CdKSUlpgxEB6Gg40wSg07jmmmt02223KT8/XxMmTFBUVJSKi4tPaV+9evXSvHnzNGDAAN16660aMGCADh06pHvuuUf9+/dXUVGRwsLC9MEHH7TyKAB0VJxpAtCp/OEPf1B6erpKSkpUUVGh8PDwU9rPeeedp+Dg///vysTERKWnp3tfh4SEKC4uTnv37j3tmgEEBs40AehUPv/8c1VXV6uxsVE7duw45f1069bN53VQUFCz6xobG0/5PQAEFs40Aeg06uvr9etf/1o33nijBgwYoP/4j//Qhg0blJCQ4O/SAHQCnGkC0GnMnDlTbrdb8+bN0/Tp03XOOefo1ltv9XdZADoJQhOATuG9997TY489phdffFEOh0PBwcF68cUX9c9//lPz58/3d3kAOgG+ngPQKVx55ZVqaGjwWde3b1+53e4W72vhwoXHrXvvvfeOW3c610wBCDyEJgBd3vDhw5WZmamysrLT2k90dLSOHDmiiIiIVqoMQEdCaALQZaWkpGjbtm2SdMqPJjhWVVWVpO8eRwCg8wkyxhh/FwEAANDRcSE4AACABUITAACABUITAACABUITAACABUITAACABUITAACABUITAACABUITAACAhf8DQ5FnPZ+vKjEAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from floris import FlorisModel\n", "from floris import layout_visualization as layoutviz\n", @@ -149,12 +186,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "b9a5f00a-0ead-4759-b911-3a1161e55791", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mturbine_type has been changed without specifying a new reference_wind_height. reference_wind_height remains 90.00 m. Consider calling `FlorisModel.assign_hub_height_to_ref_height` to update the reference wind height to the turbine hub height.\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Power [kW]')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from floris import TimeSeries\n", "import numpy as np\n", @@ -212,12 +277,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "722be425-9231-451a-bd84-7824db6a5098", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mturbine_type has been changed without specifying a new reference_wind_height. reference_wind_height remains 90.00 m. Consider calling `FlorisModel.assign_hub_height_to_ref_height` to update the reference wind height to the turbine hub height.\u001b[0m\n", + "/Users/rmudafor/Development/floris/floris/core/turbine/operation_models.py:358: RuntimeWarning: divide by zero encountered in divide\n", + " power_fractions = power_setpoints / base_powers\n", + "/Users/rmudafor/Development/floris/floris/core/wake_deflection/gauss.py:328: RuntimeWarning: invalid value encountered in divide\n", + " val = 2 * (avg_v - v_core) / (v_top + v_bottom)\n", + "/Users/rmudafor/Development/floris/floris/core/wake_deflection/gauss.py:163: RuntimeWarning: invalid value encountered in divide\n", + " C0 = 1 - u0 / freestream_velocity\n", + "/Users/rmudafor/Development/floris/floris/core/wake_velocity/gauss.py:80: RuntimeWarning: invalid value encountered in divide\n", + " sigma_z0 = rotor_diameter_i * 0.5 * np.sqrt(uR / (u_initial + u0))\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Power [kW]')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Set up the FlorisModel\n", "fmodel.set_operation_model(\"simple-derating\")\n", @@ -272,10 +373,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "5e3cda81", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mturbine_type has been changed without specifying a new reference_wind_height. reference_wind_height remains 90.00 m. Consider calling `FlorisModel.assign_hub_height_to_ref_height` to update the reference wind height to the turbine hub height.\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Powers [kW]: [[3063.49046772 2000. ]]\n" + ] + } + ], "source": [ "fmodel.set_operation_model(\"mixed\")\n", "fmodel.set(layout_x=[0.0, 0.0], layout_y=[0.0, 500.0])\n", @@ -354,10 +470,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "40e9bcda", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mturbine_type has been changed without specifying a new reference_wind_height. reference_wind_height remains 150.00 m. Consider calling `FlorisModel.assign_hub_height_to_ref_height` to update the reference wind height to the turbine hub height.\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Powers [kW]: [[6133.16222884 6301.1399872 ]]\n" + ] + } + ], "source": [ "fmodel = FlorisModel(\"../examples/inputs/emgauss_helix.yaml\")\n", "fmodel.set_operation_model(\"awc\")\n", @@ -421,10 +552,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "1eff05f3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mturbine_type has been changed without specifying a new reference_wind_height. reference_wind_height remains 90.00 m. Consider calling `FlorisModel.assign_hub_height_to_ref_height` to update the reference wind height to the turbine hub height.\u001b[0m\n", + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mturbine_type has been changed without specifying a new reference_wind_height. reference_wind_height remains 90.00 m. Consider calling `FlorisModel.assign_hub_height_to_ref_height` to update the reference wind height to the turbine hub height.\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Power [kW]')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Set up the FlorisModel\n", "fmodel = FlorisModel(\"../examples/inputs/gch.yaml\")\n", @@ -460,10 +620,155 @@ "ax[1].set_ylabel(\"Power [kW]\")" ] }, + { + "cell_type": "markdown", + "id": "92912bf7", + "metadata": {}, + "source": [ + "### MIT Yaw Corrected Actuator Disk Turbine Model\n", + "\n", + "User-level name: `\"mit-loss\"`\n", + "\n", + "Underlying class: `MITTurbine`\n", + "\n", + "Required data on `power_thrust_table`:\n", + "- `ref_air_density` (scalar)\n", + "- `ref_tilt` (scalar)\n", + "- `wind_speed` (list)\n", + "- `power` (list)\n", + "- `thrust_coefficient` (list)\n", + "\n", + "An extension of the classical one-dimensional momentum theory to model the induction of an\n", + "actuator disk is presented in {cite:t}`HeckJohlasHowland2023_yawed_adm` to directly account\n", + "for power and thrust loss due to yaw misalignment rather than using an empirical correction\n", + "as in the cosine loss model. Analytical expressions for the induction, thrust, initial wake\n", + "velocities and power are developed as a function of the yaw angle and thrust coefficient.\n", + "\n", + "This section recreates key validation figures discussed in the paper through FLORIS." + ] + }, { "cell_type": "code", "execution_count": null, - "id": "92912bf7", + "id": "8bbac518", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mturbine_type has been changed without specifying a new reference_wind_height. reference_wind_height remains 90.00 m. Consider calling `FlorisModel.assign_hub_height_to_ref_height` to update the reference wind height to the turbine hub height.\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Power ratio, P(γ)/P(γ=0)')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_points = 20\n", + "\n", + "fmodel.set_operation_model(\"mit-loss\")\n", + "fmodel.set(layout_x=[0.0], layout_y=[0.0])\n", + "fmodel.reset_operation()\n", + "fmodel.set(\n", + " wind_data=TimeSeries(\n", + " wind_speeds=np.array(n_points * [11.0]),\n", + " wind_directions=np.array(n_points * [270.0]),\n", + " turbulence_intensities=0.06\n", + " )\n", + ")\n", + "yaw_angles = np.linspace(0, 50, n_points)\n", + "cos_reference = np.cos(np.radians(yaw_angles))\n", + "cos3_reference = np.cos(np.radians(yaw_angles))**3\n", + "\n", + "fmodel.set(yaw_angles=np.reshape(yaw_angles, (-1,1)))\n", + "fmodel.run()\n", + "\n", + "powers = fmodel.get_turbine_powers()\n", + "yaw_adm_power_ratio = powers[:,0] / powers[0,0]\n", + "\n", + "fig, ax = plt.subplots(1,1)\n", + "ax.plot(yaw_angles, yaw_adm_power_ratio, label=\"Yaw-dependent ADM\", color=\"black\")\n", + "ax.plot(yaw_angles, cos_reference, label=\"cos(γ)\", linestyle=\":\", color=\"purple\")\n", + "ax.plot(yaw_angles, cos3_reference, label=\"cos^3(γ)\", linestyle=\":\", color=\"orange\")\n", + "ax.grid()\n", + "ax.legend()\n", + "ax.set_title(\"Figure 2 (a): Power ratio vs yaw angle\")\n", + "ax.set_xlabel(\"Yaw angle, γ (degrees)\")\n", + "ax.set_ylabel(\"Power ratio, P(γ)/P(γ=0)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30e54ab2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'a_n / a_n(γ=0)')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from floris.core.turbine.mit_turbine import Heck, LimitedHeck, MomentumSolution\n", + "\n", + "n_points = 20\n", + "yaw_angles = np.linspace(0, 50, n_points)\n", + "\n", + "ct_prime = 1.33\n", + "\n", + "heck = Heck()\n", + "yaw_adm_ai = np.array([heck(ct_prime, np.radians(yaw)).an for yaw in yaw_angles])\n", + "heck_no_spanwise = LimitedHeck()\n", + "yaw_no_v4_ai = np.array([heck_no_spanwise(ct_prime, np.radians(yaw)).an for yaw in yaw_angles])\n", + "\n", + "fig, ax = plt.subplots(1,1)\n", + "ax.plot(yaw_angles, yaw_adm_ai / yaw_adm_ai[0], label=\"Yaw-dependent ADM\", color=\"black\")\n", + "ax.plot(yaw_angles, yaw_no_v4_ai / yaw_no_v4_ai[0], label=\"|v4| << |u4| limit\", linestyle=\"--\", color=\"blue\")\n", + "ax.grid()\n", + "ax.legend()\n", + "ax.set_title(\"Figure 3: Normalized rotor-normal, rotor-averaged induction for the yawed ADM\")\n", + "ax.set_xlabel(\"Yaw angle, γ (degrees)\")\n", + "ax.set_ylabel(\"a_n / a_n(γ=0)\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "00172dfa", "metadata": {}, "outputs": [], "source": [] @@ -471,7 +776,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "floris", "language": "python", "name": "python3" }, @@ -485,7 +790,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3.12.1" } }, "nbformat": 4, diff --git a/docs/references.bib b/docs/references.bib index da217dd4f..36084ac8c 100644 --- a/docs/references.bib +++ b/docs/references.bib @@ -299,3 +299,15 @@ @article{SinnerFleming2024grs title = {Robust wind farm layout optimization}, journal = {Journal of Physics: Conference Series}, } + +@article{HeckJohlasHowland2023_yawed_adm, +doi = {10.1017/jfm.2023.129}, +url = {https://doi.org/10.1017/jfm.2023.129}, +year = {2023}, +month = {mar}, +publisher={Cambridge University Press}, +volume = {959}, +author = {K.S. Heck, H.M. Johlas and M.F. Howland}, +title = {Modelling the induction, thrust and power of a yaw-misaligned actuator disk}, +journal = {Journal of Fluid Mechanics}, +} diff --git a/examples/examples_turbine/004_compare_yaw_loss.py b/examples/examples_turbine/004_compare_yaw_loss.py new file mode 100644 index 000000000..48cad0ce2 --- /dev/null +++ b/examples/examples_turbine/004_compare_yaw_loss.py @@ -0,0 +1,87 @@ +""" +Example: Change operation model and compare power loss in yaw. + +This example illustrates how to define different operational models and compares +the power loss resulting from yaw misalignment across these various models. +""" + +import itertools + +import matplotlib.pyplot as plt +import numpy as np + +from floris import FlorisModel, TimeSeries + + +# Parameters +N = 101 # How many steps to cover yaw range in +yaw_max = 30 # Maximum yaw angle to test + +# Set up the yaw angle sweep +yaw_angles = np.zeros((N, 1)) +yaw_angles[:, 0] = np.linspace(-yaw_max, yaw_max, N) +# print(yaw_angles.shape) + + +def evaluate_yawed_power(wsp: float, op_model: str) -> float: + print(f"Evaluating model: {op_model} wind speed: {wsp} m/s") + + # Grab model of FLORIS + fmodel = FlorisModel("../inputs/gch.yaml") + + # Run N cases by setting up a TimeSeries (which is just several independent simulations) + wind_directions = np.ones(N) * 270.0 + fmodel.set( + wind_data=TimeSeries( + wind_speeds=wsp, + wind_directions=wind_directions, + turbulence_intensities=0.06, + ) + ) + + yaw_angles = np.array( + [(yaw, 0.0, 0.0) for yaw in np.linspace(-yaw_max, yaw_max, N)] + ) + fmodel.set_operation_model(op_model) + fmodel.set(yaw_angles=yaw_angles) + fmodel.run() + + # Save the power output results in kW + return fmodel.get_turbine_powers()[:, 0] / 1000 + + +# Loop over the operational models and wind speeds to compare +op_models = ["simple", "cosine-loss", "mit-loss"] +wind_speeds = [11.0, 11.5, 15.0] +results = {} +for op_model, wsp in itertools.product(op_models, wind_speeds): + + # Save the power output results in kW + results[(op_model, wsp)] = evaluate_yawed_power(wsp, op_model) +# Plot the results +fig, axes = plt.subplots(1, len(wind_speeds), sharey=True) + +colors = ["C0", "k", "r"] +linestyles = ["solid", "dashed", "dotted"] +for wsp, ax in zip(wind_speeds, axes): + ax.set_title(f"wsp: {wsp} m/s") + ax.set_xlabel("Yaw angle [deg]") + ax.grid(True) + for op_model, c, ls in zip(op_models, colors, linestyles): + + upstream_yaw_angle = yaw_angles[:, 0] + central_power = results[(op_model, wsp)][upstream_yaw_angle == 0] + ax.plot( + upstream_yaw_angle, + results[(op_model, wsp)] / central_power, + label=op_model, + color=c, + linestyle=ls, + ) + +ax.grid(True) +ax.legend() +axes[0].set_xlabel("Yaw angle [deg]") +axes[0].set_ylabel("Normalized turbine power [-]") + +plt.show() diff --git a/floris/core/turbine/__init__.py b/floris/core/turbine/__init__.py index a7cde822a..d76bee720 100644 --- a/floris/core/turbine/__init__.py +++ b/floris/core/turbine/__init__.py @@ -1,4 +1,5 @@ +from floris.core.turbine.mit_turbine import MITTurbine from floris.core.turbine.operation_models import ( AWCTurbine, CosineLossTurbine, diff --git a/floris/core/turbine/mit_turbine.py b/floris/core/turbine/mit_turbine.py new file mode 100644 index 000000000..2fcc317c7 --- /dev/null +++ b/floris/core/turbine/mit_turbine.py @@ -0,0 +1,516 @@ + +from __future__ import annotations + +from dataclasses import dataclass +from typing import ( + Any, + Callable, + List, + Optional, + Protocol, + Tuple, + Union, +) + +import numpy as np +from numpy.typing import ArrayLike +from scipy.interpolate import interp1d + +from floris.core.rotor_velocity import ( + average_velocity, + rotor_velocity_air_density_correction, +) +from floris.core.turbine.operation_models import BaseOperationModel +from floris.type_dec import NDArrayFloat + + +## Turbine operation model functions +# These are called by FLORIS through the MITTurbine class to ultimately compute +# the power, thrust coefficient, and axial induction of the turbine. + +def mit_rotor_axial_induction(Cts: NDArrayFloat, yaw_angles: NDArrayFloat)-> NDArrayFloat: + """ + Computes the axial induction of a yawed rotor given the yaw-aligned thrust + coefficient and yaw angles using the yawed actuator disk model developed at + MIT as described in Heck et al. 2023. Assumes the modified thrust + coefficient, C_T', is invariant to yaw misalignment angle. + + Args + Cts (NDArrayFloat): Yaw-aligned thrust coefficient(s). + yaw_angles (NDArrayFloat): Rotor yaw angle(s) in degrees. + + Returns: NDArrayFloat: Axial induction factor(s) of the yawed rotor. + """ + Ctprime = - 4 * (Cts + 2 * np.sqrt(1 - Cts) - 2) / Cts + sol = Heck()(Ctprime, np.deg2rad(yaw_angles)) + + return sol.an + +def mit_rotor_velocity_yaw_correction( + Cts: NDArrayFloat, + yaw_angles: NDArrayFloat, + axial_inductions: NDArrayFloat, + rotor_effective_velocities: NDArrayFloat, +) -> NDArrayFloat: + """ + Computes adjusted rotor wind speeds given the yaw-aligned thrust + coefficient, yaw angles, and axial induction values using the yawed actuator + disk model developed at MIT as described in Heck et al. 2023. Assumes the + modified thrust coefficient, C_T', is invariant to yaw misalignment angle. + + Args + Cts (NDArrayFloat): Yaw-aligned thrust coefficient(s). + yaw_angles (NDArrayFloat): Rotor yaw angle(s) in degrees. + axial_induction (NDArrayFloat): Rotor axial induction(s); this should follow the MIT model + yaw dependent derivation and probably gotten from `mit_rotor_axial_induction`. + rotor_effective_velocities (NDArrayFloat) rotor effective wind speed(s) at the rotor. + + Returns: NDArrayFloat: corrected rotor effective wind speed(s) of the yawed rotor. + """ + Ctprime = - 4 * (Cts + 2 * np.sqrt(1 - Cts) - 2) / Cts + ratio = (1 + 0.25 * Ctprime) * (1 - axial_inductions) * np.cos(np.deg2rad(yaw_angles)) + + return ratio * rotor_effective_velocities + + +## Iterative solver functions + +class FixedPointIterationCompatible(Protocol): + def residual(self, *args, **kwargs) -> Tuple[ArrayLike]: ... + + def initial_guess(self, *args, **kwargs) -> Tuple[ArrayLike]: ... + +@dataclass +class FixedPointIterationResult: + converged: bool + niter: int + relax: float + max_resid: float + x: ArrayLike + +def _fixedpointiteration( + f: Callable[[ArrayLike, Any], np.ndarray], + x0: np.ndarray, + args=(), + kwargs={}, + eps=0.00001, + maxiter=100, + relax=0, + callback=None, +) -> FixedPointIterationResult: + """ + Performs fixed-point iteration on function f until residuals converge or max + iterations is reached. + + Args: + f (Callable): residual function of form f(x, *args, **kwargs) -> np.ndarray + x0 (np.ndarray): Initial guess + args (tuple): arguments to pass to residual function. Defaults to (). + kwargs (dict): keyword arguments to pass to residual function. Defaults to {}. + eps (float): Convergence tolerance. Defaults to 0.000001. + maxiter (int): Maximum number of iterations. Defaults to 100. + relax (float): Relaxation factor between 0 and 1. Defaults to 0. + callback (Callable): optional callback function at each iteration of the form f(x0) -> None + + Returns: + FixedPointIterationResult: Solution to residual function. + """ + + for c in range(maxiter): + residuals = f(x0, *args, **kwargs) + + x0 = [_x0 + (1 - relax) * _r for _x0, _r in zip(x0, residuals)] + max_resid = [np.nanmax(np.abs(_r)) for _r in residuals] + + if callback: + callback(x0) + + if all(_r < eps for _r in max_resid): + converged = True + break + else: + converged = False + + if maxiter == 0: + return FixedPointIterationResult(False, 0, np.nan, np.nan, x0) + return FixedPointIterationResult(converged, c, relax, max_resid, x0) + +def fixedpointiteration( + max_iter: int = 100, + tolerance: float = 1e-6, + relaxation: float = 0.0 +) -> FixedPointIterationCompatible: + """ + Class decorator which adds a __call__ method to the class which performs + fixed-point iteration. + + Args: + max_iter (int): Maximum number of iterations (default: 100) + tolerance (float): Convergence criteria (default: 1e-6) + relaxation (float): Relaxation factor between 0 and 1 (default: 0.0) + + The class must contain 2 mandatory methods and 3 + optional method: + + mandatory: + initial_guess(self, *args, **kwargs) + residual(self, x, *args, **kwargs) + + optional: + pre_process(self, *args, **kwargs) # Optional + post_process(self, result:FixedPointIterationResult) # Optional + callback(self, x) # Optional + """ + + def decorator(cls: FixedPointIterationCompatible) -> Callable: + def call(self, *args, **kwargs): + if hasattr(self, "pre_process"): + self.pre_process(*args, **kwargs) + + callback = self.callback if hasattr(self, "callback") else None + + x0 = self.initial_guess(*args, **kwargs) + result = _fixedpointiteration( + self.residual, + x0, + args=args, + kwargs=kwargs, + eps=tolerance, + maxiter=max_iter, + relax=relaxation, + callback=callback, + ) + + if hasattr(self, "post_process"): + return self.post_process(result, *args, **kwargs) + else: + return result + + setattr(cls, "__call__", call) + return cls + + return decorator + +def adaptivefixedpointiteration( + max_iter: int = 100, + tolerance: float = 1e-6, + relaxations: List[float] = [0.0] +) -> Callable: + """ + Class decorator which adds a __call__ method to the class which performs + fixed-point iteration. Same as `fixedpointiteration`, but takes a list of + relaxation factors, and iterates over all of them in order until convergence + is reached. + """ + + def decorator(cls: FixedPointIterationCompatible) -> Callable: + def call(self, *args, **kwargs): + if hasattr(self, "pre_process"): + self.pre_process(*args, **kwargs) + callback = self.callback if hasattr(self, "callback") else None + + for relaxation in relaxations: + x0 = self.initial_guess(*args, **kwargs) + result = _fixedpointiteration( + self.residual, + x0, + args=args, + kwargs=kwargs, + eps=tolerance, + maxiter=max_iter, + relax=relaxation, + callback=callback, + ) + if result.converged: + break + + if hasattr(self, "post_process"): + return self.post_process(result, *args, **kwargs) + else: + return result + + setattr(cls, "__call__", call) + return cls + + return decorator + +## The operation model class to interface with FLORIS. +# This uses the iterative solve functions above. + +class MITTurbine(BaseOperationModel): + """ + """ + + def power( + power_thrust_table: dict, + velocities: NDArrayFloat, + air_density: float, + yaw_angles: NDArrayFloat, + average_method: str = "cubic-mean", + cubature_weights: NDArrayFloat | None = None, + **kwargs, + ) -> None: + + # Construct thrust coefficient interpolant + thrust_coefficient_interpolator = interp1d( + power_thrust_table["wind_speed"], + power_thrust_table["thrust_coefficient"], + fill_value=0.0001, + bounds_error=False, + ) + + # Compute the power-effective wind speed across the rotor + rotor_average_velocities = average_velocity( + velocities=velocities, + method=average_method, + cubature_weights=cubature_weights, + ) + + rotor_effective_velocities = rotor_velocity_air_density_correction( + velocities=rotor_average_velocities, + air_density=air_density, + ref_air_density=power_thrust_table["ref_air_density"] + ) + + thrust_coefficients = thrust_coefficient_interpolator(rotor_effective_velocities) + + axial_inductions = mit_rotor_axial_induction(thrust_coefficients, yaw_angles) + + corrected_rotor_effective_velocities = mit_rotor_velocity_yaw_correction( + thrust_coefficients, + yaw_angles, + axial_inductions, + rotor_effective_velocities + ) + + # TODO: Tilt correction? + + # Construct power interpolant + power_interpolator = interp1d( + power_thrust_table["wind_speed"], + power_thrust_table["power"], + fill_value=0.0, + bounds_error=False, + ) + + # Compute power + power = power_interpolator(corrected_rotor_effective_velocities) * 1e3 # Convert to W + + return power + + def thrust_coefficient( + power_thrust_table: dict, + velocities: NDArrayFloat, + air_density: float, + yaw_angles: NDArrayFloat, + average_method: str = "cubic-mean", + cubature_weights: NDArrayFloat | None = None, + **kwargs, + ) -> None: + + # Construct thrust coefficient interpolant + thrust_coefficient_interpolator = interp1d( + power_thrust_table["wind_speed"], + power_thrust_table["thrust_coefficient"], + fill_value=0.0001, + bounds_error=False, + ) + + # Compute the power-effective wind speed across the rotor + rotor_average_velocities = average_velocity( + velocities=velocities, + method=average_method, + cubature_weights=cubature_weights, + ) + + rotor_effective_velocities = rotor_velocity_air_density_correction( + velocities=rotor_average_velocities, + air_density=air_density, + ref_air_density=power_thrust_table["ref_air_density"] + ) + + thrust_coefficients = thrust_coefficient_interpolator(rotor_effective_velocities) + + axial_inductions = mit_rotor_axial_induction(thrust_coefficients, yaw_angles) + + corrected_rotor_effective_velocities = mit_rotor_velocity_yaw_correction( + thrust_coefficients, + yaw_angles, + axial_inductions, + rotor_effective_velocities + ) + + # TODO: Tilt correction? + + # Compute thrust coefficient + yawed_thrust_coefficients = thrust_coefficient_interpolator( + corrected_rotor_effective_velocities + ) + + return yawed_thrust_coefficients + + def axial_induction( + power_thrust_table: dict, + velocities: NDArrayFloat, + air_density: float, + yaw_angles: NDArrayFloat, + average_method: str = "cubic-mean", + cubature_weights: NDArrayFloat | None = None, + **kwargs, + ): + + # Construct thrust coefficient interpolant + thrust_coefficient_interpolator = interp1d( + power_thrust_table["wind_speed"], + power_thrust_table["thrust_coefficient"], + fill_value=0.0001, + bounds_error=False, + ) + + # Compute the power-effective wind speed across the rotor + rotor_average_velocities = average_velocity( + velocities=velocities, + method=average_method, + cubature_weights=cubature_weights, + ) + + rotor_effective_velocities = rotor_velocity_air_density_correction( + velocities=rotor_average_velocities, + air_density=air_density, + ref_air_density=power_thrust_table["ref_air_density"] + ) + + thrust_coefficients = thrust_coefficient_interpolator(rotor_effective_velocities) + + axial_inductions = mit_rotor_axial_induction(thrust_coefficients, yaw_angles) + + return axial_inductions + + +## Below is the implementation of the model as described in the paper. + +@dataclass +class MomentumSolution: + """Stores the results of the Unified Momentum model solution.""" + + Ctprime: float + yaw: float + an: Union[float, NDArrayFloat] + u4: Union[float, NDArrayFloat] + v4: Union[float, NDArrayFloat] + x0: Union[float, NDArrayFloat] + dp: Union[float, NDArrayFloat] + dp_NL: Optional[Union[float, NDArrayFloat]] = 0.0 + niter: Optional[int] = 1 + converged: Optional[bool] = True + beta: Optional[float] = 0.0 + + @property + def Ct(self): + """Returns the thrust coefficient Ct.""" + return self.Ctprime * (1 - self.an) ** 2 * np.cos(self.yaw) ** 2 + + @property + def Cp(self): + """Returns the power coefficient Cp.""" + return self.Ctprime * ((1 - self.an) * np.cos(self.yaw)) ** 3 + +class LimitedHeck(): + """ + Solves the limiting case when v_4 << u_4. (Eq. 2.19, 2.20). Also takes Numpy + array arguments. + """ + + def __call__(self, Ctprime: float, yaw: float, **kwargs) -> MomentumSolution: + """ + Args: + Ctprime (float): Rotor thrust coefficient. + yaw (float): Rotor yaw angle (radians). + + Returns: + Tuple[float, float, float]: induction and outlet velocities. + """ + + a = Ctprime * np.cos(yaw) ** 2 / (4 + Ctprime * np.cos(yaw) ** 2) + u4 = (4 - Ctprime * np.cos(yaw) ** 2) / (4 + Ctprime * np.cos(yaw) ** 2) + v4 = ( + -(4 * Ctprime * np.sin(yaw) * np.cos(yaw) ** 2) + / (4 + Ctprime * np.cos(yaw) ** 2) ** 2 + ) + dp = np.zeros_like(a) + x0 = np.inf * np.ones_like(a) + return MomentumSolution(Ctprime, yaw, a, u4, v4, x0, dp) + +@fixedpointiteration(max_iter=500, tolerance=0.00001, relaxation=0.1) +class Heck(): + """ + Solves the iterative momentum equation for an actuator disk model. + """ + + def __init__(self, v4_correction: float = 1.0): + """ + Initialize the HeckModel instance. + + Args: + v4_correction (float, optional): The premultiplier of v4 in the Heck + model. A correction factor applied to v4, with a default value of + 1.0, indicating no correction. Lu (2023) suggests an empirical correction + of 1.5. + + Example: + >>> model = HeckModel(v4_correction=1.5) + """ + self.v4_correction = v4_correction + + def initial_guess(self, Ctprime, yaw): + sol = LimitedHeck()(Ctprime, yaw) + return sol.an, sol.u4, sol.v4 + + def residual(self, x: np.ndarray, Ctprime: float, yaw: float) -> np.ndarray: + """ + Residual function of yawed-actuator disk model in Eq. 2.15. + + Args: + x (np.ndarray): (a, u4, v4) + Ctprime (float): Rotor thrust coefficient. + yaw (float): Rotor yaw angle (radians). + + Returns: + np.ndarray: residuals of induction and outlet velocities. + """ + + a, u4, v4 = x + e_a = 1 - np.sqrt(1 - u4**2 - v4**2) / (np.sqrt(Ctprime) * np.cos(yaw)) - a + + e_u4 = (1 - 0.5 * Ctprime * (1 - a) * np.cos(yaw) ** 2) - u4 + + e_v4 = ( + -self.v4_correction + * 0.25 + * Ctprime + * (1 - a) ** 2 + * np.sin(yaw) + * np.cos(yaw) ** 2 + - v4 + ) + + return np.array([e_a, e_u4, e_v4]) + + def post_process(self, result, Ctprime: float, yaw: float): + if result.converged: + a, u4, v4 = result.x + else: + a, u4, v4 = np.nan * np.zeros_like([Ctprime, Ctprime, Ctprime]) + dp = np.zeros_like(a) + x0 = np.inf * np.ones_like(a) + return MomentumSolution( + Ctprime, + yaw, + a, + u4, + v4, + x0, + dp, + niter=result.niter, + converged=result.converged, + ) diff --git a/floris/core/turbine/turbine.py b/floris/core/turbine/turbine.py index 2f98c45ea..07e96b44b 100644 --- a/floris/core/turbine/turbine.py +++ b/floris/core/turbine/turbine.py @@ -15,6 +15,7 @@ from floris.core.turbine import ( AWCTurbine, CosineLossTurbine, + MITTurbine, MixedOperationTurbine, PeakShavingTurbine, SimpleDeratingTurbine, @@ -41,6 +42,7 @@ "mixed": MixedOperationTurbine, "awc": AWCTurbine, "peak-shaving": PeakShavingTurbine, + "mit-loss": MITTurbine, }, } diff --git a/tests/turbine_operation_models_unit_test.py b/tests/turbine_operation_models_unit_test.py index b50aab54b..8aab240c5 100644 --- a/tests/turbine_operation_models_unit_test.py +++ b/tests/turbine_operation_models_unit_test.py @@ -1,6 +1,7 @@ import numpy as np import pytest +from floris.core.turbine.mit_turbine import MITTurbine from floris.core.turbine.operation_models import ( AWCTurbine, CosineLossTurbine, @@ -40,6 +41,10 @@ def test_submodel_attributes(): assert hasattr(PeakShavingTurbine, "thrust_coefficient") assert hasattr(PeakShavingTurbine, "axial_induction") + assert hasattr(MITTurbine, "power") + assert hasattr(MITTurbine, "thrust_coefficient") + assert hasattr(MITTurbine, "axial_induction") + def test_SimpleTurbine(): n_turbines = 1 @@ -659,3 +664,54 @@ def test_PeakShavingTurbine(): assert (test_power <= base_power).all() assert test_power[0,0] == base_power[0,0] assert test_power[-1,0] == base_power[-1,0] + + +def test_MITTurbine(): + + # NOTE: These tests should be updated to reflect actual expected behavior + # of the MITTurbine model. Currently, match the CosineLossTurbine model. + + n_turbines = 1 + wind_speed = 10.0 + turbine_data = SampleInputs().turbine + + yaw_angles_nom = 0 * np.ones((1, n_turbines)) + tilt_angles_nom = turbine_data["power_thrust_table"]["ref_tilt"] * np.ones((1, n_turbines)) + yaw_angles_test = 20 * np.ones((1, n_turbines)) + tilt_angles_test = 0 * np.ones((1, n_turbines)) + + + # Check that power works as expected + test_power = MITTurbine.power( + power_thrust_table=turbine_data["power_thrust_table"], + velocities=wind_speed * np.ones((1, n_turbines, 3, 3)), # 1 findex, 1 turbine, 3x3 grid + air_density=turbine_data["power_thrust_table"]["ref_air_density"], # Matches ref_air_density + yaw_angles=yaw_angles_nom, + tilt_angles=tilt_angles_nom, + tilt_interp=None + ) + truth_index = turbine_data["power_thrust_table"]["wind_speed"].index(wind_speed) + baseline_power = turbine_data["power_thrust_table"]["power"][truth_index] * 1000 + assert np.allclose(baseline_power, test_power) + + # Check that yaw and tilt angle have an effect + test_power = MITTurbine.power( + power_thrust_table=turbine_data["power_thrust_table"], + velocities=wind_speed * np.ones((1, n_turbines, 3, 3)), # 1 findex, 1 turbine, 3x3 grid + air_density=turbine_data["power_thrust_table"]["ref_air_density"], # Matches ref_air_density + yaw_angles=yaw_angles_test, + tilt_angles=tilt_angles_test, + tilt_interp=None + ) + assert test_power < baseline_power + + # Check that a lower air density decreases power appropriately + test_power = MITTurbine.power( + power_thrust_table=turbine_data["power_thrust_table"], + velocities=wind_speed * np.ones((1, n_turbines, 3, 3)), # 1 findex, 1 turbine, 3x3 grid + air_density=1.1, + yaw_angles=yaw_angles_nom, + tilt_angles=tilt_angles_nom, + tilt_interp=None + ) + assert test_power < baseline_power