{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# MLER example\n", "Extreme conditions modeling consists of identifying the expected extreme (e.g. 100-year) response of some quantity of interest, such as WEC motions or mooring loads. \n", "Three different methods of estimating extreme conditions were adapted from [WDRT](https://github.com/WEC-Sim/WDRT): full sea state approach, contour approach, and MLER design wave. \n", "This noteboook presents the MLER approach. \n", "\n", "This example notebook shows users how to utilze the most likely extreme response (MLER) method. This method is an alternative to exhaustive Monte Carlo or long-term simulations for finding and evaluating wave energy converter response events at extreme loads. To accomplish this, a given RAO is combined with a wave spectrum corresponding to certain extreme sea states. MLER then generates the resulting wave profile that would cause the largest response for that degree of freedom. See full explanation and derivation of the MLER method in \n", "\n", "> E. Quon, A. Platt, Y.-H. Yu, and M. Lawson, “Application of the Most Likely Extreme Response Method for Wave Energy Converters,” in Volume 6: Ocean Space Utilization; Ocean Renewable Energy, Busan, South Korea, Jun. 2016, p. V006T09A022. doi: 10.1115/OMAE2016-54751.\n", "\n", "First, we start by importing the relevant modules. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import mhkit.wave.resource as resource\n", "import mhkit.loads.extreme as extreme" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, a simple ellipsoid shaped WEC device was modeled in WEC-Sim. We will focus on anayzing the heave response of this device. The code below simply imports RAO data as it is needed for one of the inputs." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 1.000000+0.000000j\n", "1 1.000005-0.000225j\n", "2 1.000013-0.000392j\n", "3 1.000023-0.000511j\n", "4 1.000037-0.000589j\n", "5 1.000052-0.000635j\n", "6 1.000068-0.000658j\n", "7 1.000086-0.000665j\n", "8 1.000103-0.000820j\n", "9 1.000103-0.000949j\n", "Name: RAO, dtype: complex128" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wave_freq = np.linspace( 0.,1,500)\n", "mfile = pd.read_csv('data/loads/mler.csv')\n", "RAO = mfile['RAO'].astype(complex)\n", "RAO[0:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we need to generate a wave environment that corresponds to a chosen extreme sea state. The associated parameters are selected in different ways. In this case, public wave data was used to come up with estimated 100-year sea state contour. The sea state parameters were selected somewhere along the contour." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/cmichel/MHKiT/mhkit/wave/resource.py:93: RuntimeWarning: divide by zero encountered in power\n", " Sf = A_PM*f**(-5)*np.exp(-B_PM*f**(-4))\n", "/Users/cmichel/MHKiT/mhkit/wave/resource.py:93: RuntimeWarning: invalid value encountered in multiply\n", " Sf = A_PM*f**(-5)*np.exp(-B_PM*f**(-4))\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "Hs = 9.0 # significant wave height\n", "Tp = 15.1 # time period of waves\n", "pm = resource.pierson_moskowitz_spectrum(wave_freq,Tp,Hs)\n", "pm.plot(xlabel='frequency [Hz]',ylabel='response [m^2/Hz]')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have both the RAO and the spectrum of the wave environment, we can calculate the MLER conditioned wave spectrum and phase. In this case, we would like to find the wave profile that will generate a heave response of 1 meter for our WEC device. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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Tkjzdyq6jjYzOtf6JUHdLWQExLuGp90O3UzukE4WqNqhqk+/+YiBWRDJPU3ahqpapallWVlZQ4zQm1Bw81kJ7VzejbcXYkJedksDc8Tk8u64yZPeqCOlEISK5IiK++9PwxlvnbFTGhL6TM35HZCc7HInpj89NL+JESydvbD3idCi9inHy5CLyFDAHyBSRSuCHQCyAqi4AbgbuF5EuoBW4TcNpyUVjHLLH19490hJFWJgxPIOSzEE8ubqCG6bkOx3OJziaKFT19jO8/hvgN0EKx5iIsae6iayUeNISY50OxfSDyyXcPm0Y/7X4A3YdbQy5TaZCuunJGHNu9lQ3MTLLahPh5Oapw4hzu/jz6tDr1LZEYUyEUVX21jQxIttWjA0nQwbFcc3EXJ5fX0lLR5fT4XyMJQpjIkxNYzuNbV1WowhDn5teRGNbF69tDq2tUi1RGBNhTo54GpkdWu3c5swuKB5MSeYgnl1X6XQoH2OJwpgIYyOewpeIcPPUAt7ff4zy2manw/mQJQpjIsye6iaS42PISbU1nsLRp88vwCXwXAjVKixRGBNh9lQ3MSI7Gd9cVRNmctMSuHhUFs+tq8TTHRrTxixRGBNhbGhs+Ltl6jCONLSxck+t06EAliiMiSgNbZ1UN7Zb/0SYu2JcNulJsTyz9qDToQCWKIyJKB+NeLJEEc7iY9zcMDmfv207yomWDqfDsURhTCTZe3IxwCybbBfubikroMPTzUsbq5wOxRKFMZFkf20zMS6hcEiS06GYARo/NI1xeakh0fxkicKYCFJR18KwIUnEuO1XOxLcPLWAbVUN7DzS6Ggc9mkyJoKU1zVTlGG1iUhx3aShuF3CixsPORqHJQpjIoSqUlHXQnGG9U9EiqyUeGaXZvLShkN0OzinwhKFMRGirrmDpvYuq1FEmBun5FNV38bq/ccci8EShTERoqLOuzaQ1Sgiy9xxuQyKc/PCBueW9HA0UYjIYyJSLSJbT/O6iMivRGSPiGwWkfODHaMx4aK8tgXAahQRJjHOzdUT8nh9yxHaOj2OxOB0jeJx4Oo+Xr8GKPXd5gO/C0JMxoSlirpmXAIFgy1RRJobp+TT2N7FWzuOOnJ+RxOFqi4H+mp4mwf8Qb1WAekikhec6IwJL+V1LQxNTyQuxunvf8bfZozIICc1nhc3ODP6KdQ/UflAz9kmlb7njDGnqDhmI54ildslzJucz9KdNRxrDv6SHqGeKPpNROaLyFoRWVtTU+N0OMYEXYXNoYhoN07Jp6tbeXVz8Jf0CPVEcQgY1uNxge+5T1DVhapapqplWVlZQQnOmFBxoqWDEy2dVqOIYGPzUhmTm8ILDjQ/hXqieBm4yzf66UKgXlVDa9dxY0JARZ2NeIoGN0zJZ8OBE+wP8japTg+PfQp4DxgtIpUico+I3Cci9/mKLAb2AXuARcADDoVqTEgrPzmHItNqFJHs+klDAXh1U3Cbn2KCerZTqOrtZ3hdga8EKRxjwtbJGoWtGhvZhqYnckHxYF7ZXMWDl5cG7byh3vRkjOmH8rpm8tISSIh1Ox2KCbDrJg1l19GmoK4oa4nCmAhQUddi/RNR4poJebgEXgli85MlCmMiQEVdC0VDrH8iGmSlxHPRiExe2VyFt3U+8CxRGBPmmtq7qG1qpyjTahTR4vpJQ6moa2HLofqgnM8ShTFhzlaNjT5Xjc8l1i28HKT9tC1RGBPmbA5F9ElLiuWSUVm8uvlwUDY0skRhTJg7OYeiyGoUUeW6SUM50tDG2orjAT+XJQpjwlxFbQuZyfEkxzs6LcoE2RVjc0iIdQVl9JMlCmPCXHldM8XW7BR1BsXHcPmYHBZvOUyXpzug57JEYUyY886hsGanaHTdpDzqmjt4b19dQM9jicKYMNbW6eFIQ5vVKKLUnNHZJMfHBLz5yRKFMWHswDHfGk+WKKJSQqybueNyeH3rEdq7AreftiUKY8JYea3NoYh2100aSmNbFyt31wbsHJYojAljJ+dQWKKIXjNHZpKaEMNrWwK3Vc8Zx9OJyMv9OM4xVb174OGYaLPraCN/Xn2A7VUNdHi6GZwUy/CsZCYNS2fWyEyGDIpzOsSQVl7XTHpSLGlJsU6HYhwSF+Ni7vhclvian+Jj/L+CcH8GXo8FvtTH6wL81j/hmGjR2uHhZ6/v4In3KoiPcTGpIJ2UhBiONrTz7t46Hl25H5fA9JIMPju9kKsn5BLrtgrwqWzEkwH4p/PyeG5dJSt313L52By/H78/ieJ7qrqsrwIi8mM/xWOiQH1rJ198fA3rKo5z90XF/PPlpQzuUXPo9HSzvaqBt3Yc5aWNVTz41AayU+K5Z1YJn7+o2PZc6KG8rpmpRYOdDsM4bOaITNISY3lt82FnEoWqPuOPMsYAHGvu4K7HVrPzSCP/97nzuXZi3ifKxLpdTBqWzqRh6XzjilEs3VXNYyvL+enrH/DYO/v558tHcUtZQdTXMNq7PFSdaOWm8wucDsU4LC7GxdxxObyx7Qidnm6//270p4/iFeC0q06p6vXnenIRuRr4X8ANPKKqPzvl9buBXwCHfE/9RlUfOdfzGWdVN7Rxx6OrqahrYeGdZVw6JvuM73G5hMvG5HDZmBxW76vj50t28m8vbOGxd/bzk3kTmDEiIwiRh6bK4610KzaHwgDw4GWlfP3KUQH5AtWfI/438EtgP9AKLPLdmoC953piEXHj7du4BhgH3C4i43op+rSqTvbdLEmEqUMnWrn14feoPN7K779wQb+SxKmmD8/guftmsPDOqbR1erh90Sq+/pcNVDe2BSDi0Hfgw1VjrY/CeOfS5KcnBuTY/Wl6WgYgIr9U1bIeL70iImsHcO5pwB5V3ec7/l+AecD2ARzThKD9tc3c8chqGto6+eM90wfUpi4izB2fy8Wjsvi/t/ewYNk+Vu6p4+E7z2dq0RA/Rh36Plo11moUJrDOpo4ySESGn3wgIiXAQL7K5AMHezyu9D13qk+LyGYReU5Ehp3uYCIyX0TWisjampqaAYRl/Gn9gePcsuA9Wjs9PHXvhX7reE2IdfMvc0fz6tdmkRzv5rOLVvPunsBNOApFFXUtJMfHkGFDiE2AnU2i+AawVESWisgy4G3g6wGJ6iOvAMWqeh7wN+CJ0xVU1YWqWqaqZVlZWQEOy/TH8+sque3hVQyKd/PMl2cwIT/N7+cYlZPCXx+YSXHGIO55Yi1bg7Q1ZCgor2umKCMJEXE6FBPh+p0oVPUNoBT4Z+BrwGhVXTKAcx8CetYQCvio0/rkOetUtd338BFg6gDOZ4LE06381+IdfPPZTZQVD+bFB2YyMjs5YOcbMiiOP35pGulJsdz3p3Ucb+4I2LlCSUVdi83INkFxtt3jpcBoYBLwGRG5awDnXgOUikiJiMQBtwEfmwUuIj3HTl4P7BjA+UwQNLR18qUn1rBw+T7umlHEE1+c9rE5EoGSnZLA7+6YSnVDO1/7y4agbA/ppC5PNwePtVj/hAmKficKEfkh8Gvf7VLg53j/eJ8TVe0CvgoswZsAnlHVbSLykIicPO7XRGSbiGzCW4u5+1zPZwKvvLaZm/7vXVbsruU/b5zAQ/MmBHWuw+Rh6fzgunGs2F3Lk6srgnZeJ1SdaKOrW61GYYLibPZOvBlvTWKDqn5BRHKAPw3k5Kq6GFh8ynM/6HH/u8B3B3IOExzv7qnl/ifX4xL44z3THZvf8LnphSzZdoSfvv4Bc0ZnM2xIZH7jthFPJpjO5uteq6p2A10ikgpU8/E+BhOFVJVHV+7nzsfeJyc1npe+MsvRSXAiws8+fR4uEf7thS2oRmYTVIVvH4riTKtRmMA7m0SxVkTS8U62WwesB94LRFAmPDS3d/G1v2zkJ69u5/Ix2Tx//0UhsYFOfnoi35o7ihW7a1my7ajT4QRERW0zCbEuslPinQ7FRIF+NT2Jd/zdT1X1BLBARN4AUlV1cyCDM6FrX00T9/1pHXuqm/jXq8dw3yXDQ2qY5h0XFvHU+wf5z8XbmTM6K+IWEiyva6FoyKCQ+pmbyNWvGoV66++LezwutyQRvd7cdoR5v3mHmsZ2/vDF6dw/Z0TI/cGKcbv4wXXjOHislUdX7nc6HL+r8M2hMCYYzqbpab2IXBCwSEzI83Qrv1jyAfP/uI6SrEG8+rXZzCrNdDqs05o5MpMrx+WwYOneiJpb0d2tVBxrsf4JEzRnkyimA++JyF7fkhpbRMRqFVGi6kQrty9axW/f3sttFwzjmS/PCNgCZP70rbmjaeroYsHyc16/MuQcaWijo6vbahQmaM5meOxVAYvChLTXtxzmO3/dQpenm1/eMolPTw2f/Q9G56Zw4+R8Hn+nnC/OLCEnNcHpkAbs5NBYm0NhguWMNQoRWQ+gqhW93XqWMZGlrqmdbzy9kfufXE9RRhKvfW12WCWJk75+xSg83cqv/r7b6VD8ouLD5cWtRmGCo197Zp+hiUkA/6/2Zhyjqjy//hD/8dp27xDYy0by1ctKiYsJzx3lCjOSuH1aIU+9f4D7LhkR9pPwKupaiHO7yEsL/aY/Exn6kyjG9KOMZ6CBmNCw4cBx/vO1HaytOE5Z0WB+etNESnNSnA5rwB64dARPrznIgmV7+c8bJzodzoBU1DUzbEgibldojTQzkas/GxdF9qI5BvDulvaLN3fyyqYqMpPj+dlNE7m1bBiuCPljlJeWyM1lBTy7tpIHLyslNy18+yrKbdVYE2Th2ZZg/Ka8tplvPbuJS3+5lL9tP8KDl41k6bfncNu0wohJEifdf8kIPKosXL7P6VDOmapSUdccEjPgTfQ4m1FPJkJ4upVV++r48+oDvLHtCDEu4a4ZRdx3yYiIGBV0OsOGJHHD5Hz+/H4FD1w6gszk8Fv+oqapnZYOj9UoTFBZoogS1Q1trNhdy/LdNSzfVcPxlk7Sk2K5Z1YJX5pVQnYEJ4ieHrh0BH/dUMmjK/fzr1f3p/sttNiIJ+MESxQRTFVZtquG3y3dy+r9xwDITI7j0tHZXDommyvH5UTcGkhnMiIrmX+amMcf3i3nyxcPJz0pvPabLq+1ORQm+CxRRJhOTzd7a5pYve8YT685yPbDDeSlJfCtuaOYMzqbcXmpEdf3cLa+culIXt18mMffLefrV4xyOpyzUlHXgtsl5A+2obEmeCxRhLkuTzfbqhpYtquGZbtq2FJZT4enG4AxuSn8/NPnccOU/LCdAxEIY/NSuWJsDo+/W878i4eTFBc+vwbldc0UDE4M6s6Bxjj6GyIiVwP/C7iBR1T1Z6e8Hg/8AZgK1AGfUdXyYMfplNYODzuPNrKvpomthxrYVHkCT7eSFOcmLsbF0YZ2KuqaaenwIALn5adx98xixg9NZWJ+GsOzkp2+hJB1/5wRfPp3R3l6zUG+MLPE6XD67cCxFoqs2ckEmWOJQkTcwG+BK4FKYI2IvKyq23sUuwc4rqojReQ24P8Bnwl+tP5zuL6VhBg3gwfFcay5g8VbDtPW6aFwSBJ/31FNelIsBUOS6Ozq5v97axeNbV0AxMe4mDQsnaQ4N60dHhraOslLS2B6yRCmFKYzc2RmWI7iccrUosFcUDyYR1bs544Li8LiG7qqUl7bzA1T0p0OxUQZJ2sU04A9qroPQET+AswDeiaKecCPfPefA34jIqIB2t+y8ngLHV3ddHi66ejqptPTTXtXN50e9T7ve+7UMs0dHo41t1PX1EFdUwe1ze00tnVx3yUjyE9PYPfRJnZVN7G+4jiHTrQiAsMzB7G/tpnuHleSEh9Du+/YANNKhnDPrBJGZCVTOCTJmo/87L5LRnDPE2t5dXMVN04J/TWsjrd00tDWRWGYL0Fiwo+TiSIfONjjcSXepcx7LaOqXSJSD2QAtYEI6PJfLqO9q/us3ycCg5PiyBgUx5BBcYzNTWVdxXF+8upHOS8/PZEJ+ancM6uEtRXHKK9t4auXjuSaiXnEx7gor2tm1sgsYlxCdWM7x5o7GJObEvUdz4F06ehsSrOTeXjZPm6YnB9ymy+dar9vxFOJ7UNhgix8evHOQETmA/MBCgsLz+kYP/v0RFwixLpdxLldxMW4vPdjPnrsfU6Ii3ER73YTGyPEx7g/se5OfWsny3bVUJyRxIisZAbFf/Sj/uKsT7aJ9+xPyE1LCOslJsKFyyV8+ZIRfOvZTSzdVcOlo7OdDqlPFSeXF7dEYYLMyURxCBjW43GB77neylSKSAzeVWrrejuYqi4EFgKUlZWdU9OUP5sf0hJjuX7SUL8dzwTG9ZOG8ss3d7Jg6d6QTxTltc24BIYNtqYnE1xONnqvAUpFpERE4oDbgJdPKfMy8Hnf/ZuBfwSqf8JEp7gYF/fMKmH1/mNsOHDc6XD6tL+uhfzBidZXZYLOsU+cqnYBXwWWADuAZ1R1m4g8JCLX+4o9CmSIyB7gX4DvOBOtiWS3TSskNSGGh5eF9mKBFXXNNiPbOMLRPgpVXQwsPuW5H/S43wbcEuy4THRJjo/hrhnF/HbpHvbWNDEiBOefqCr7a5u5YXK+06GYKGR1WGOAz19UTKzbxWMr9zsdSq+Ot3TS2NZlHdnGEZYojAGyUuL5p4l5vLKpirbO0Nuw8aOhsdaRbYLPEoUxPjedn09DWxf/+KDa6VA+4eSqsbZ8h3GCJQpjfC4akUlOajx/XV/pdCifUFFnQ2ONcyxRGOPjdgk3TMln6c4aapvanQ7nY/bXtVAw2JZxMc6wT50xPXz6/AK6upVXN1U5HcrHlNc22652xjGWKIzpYVROCqNyknlj2xGnQ/mQqlJe12xrPBnHWKIw5hRXjc/l/f3HONbc4XQoABxr7vAOjbWObOMQSxTGnOKq8bl0K7y146jToQDeXe0Aim1orHGIJQpjTjF+aCr56Yks2RoazU/ltS0AVqMwjrFEYcwpRIS543NYsaeWpvYup8Oh3Dc0tsCGxhqHWKIwphdXjc+lo6ubFbtqnA6F/bXNNjTWOMo+ecb0oqxoMCkJMSzd6XyiqKhrsTWejKMsURjTixi3i1kjM1m2qwYnt0BRVcprmym2ORTGQZYojDmNOaOzONLQxgdHGh2Lobapg8Z2GxprnGWJwpjTuGSUd2vUZQ72U+yraQJgRHbo7ZFhooclCmNOIzctgTG5KSzd6dxqsntrvHMoRmRZjcI4x5FEISJDRORvIrLb9+/g05TziMhG3+3U/bSNCbg5o7NZW36cxrZOR86/t6aJhFgXQ9MSHTm/MeBcjeI7wN9VtRT4O6ffC7tVVSf7btefpowxATNndBZd3co7e+ocOf/emiaGZybjcokj5zcGnEsU84AnfPefAG5wKA5j+jS1aDDJ8TEs2+VM89PemibrnzCOcypR5KjqYd/9I0DOacoliMhaEVklIjcEJzRjPhLrdjFzZAbLd9UGfZhsW6eHyuOt1j9hHBcTqAOLyFtAbi8vfa/nA1VVETndb2CRqh4SkeHAP0Rki6ruPc355gPzAQoLCwcQuTEfN7s0iyXbjrK/tpnhWcH7dr+/thlVGBHEcxrTm4AlClW94nSvichREclT1cMikgf0Wq9X1UO+f/eJyFJgCtBrolDVhcBCgLKyMudmSJmIc3FpFgDLd9UENVHsPTk01hKFcZhTTU8vA5/33f888NKpBURksIjE++5nAjOB7UGL0BifwowkijKSWLG7Nqjn3VvdjAi2YZFxnFOJ4mfAlSKyG7jC9xgRKRORR3xlxgJrRWQT8DbwM1W1RGEcMbs0k/f21dHR1R20c+6taSI/PZHEOHfQzmlMbwLW9NQXVa0DLu/l+bXAl3z33wUmBjk0Y3o1uzSLP606wPoDx7lweEZQzrmvtsmanUxIsJnZxvTDRSMycLuEFbuDs5xHd7eyt7rZEoUJCZYojOmHlIRYzi9MD1o/xZGGNlo7PYzItv4J4zxLFMb00+zSLLYcqudYc0fAz2UjnkwosURhTD/NLs1EFd7ZE/haxd5qb6IYbpPtTAiwRGFMP51XkE5qQkxQ+il2VTeRmhBDVnJ8wM9lzJlYojCmn9wuYVZpJit2B345j51HGhmTm4qILQZonGeJwpizMLs0i8P1bR/2IQSCqrLrSCOjc1MCdg5jzoYlCmPOwqyRmQAs3xW4foqq+jYa27sYZYnChAhLFMachWFDkhieOSig/RS7fHt0j7FEYUKEJQpjztLs0kxW7TtGe5cnIMf/wJcoRmVbojChwRKFMWdpdmkWrZ0e1pUfD8jxdx1tJC8tgbSk2IAc35izZYnCmLN04YgMYlzC8gDN0v7gSCOjcqw2YUKHJQpjzlJyfAznFw0OSD9Fl6ebvdVN1j9hQoolCmPOwcWlmWyraqC2qd2vxy2va6bD0201ChNSLFEYcw5m+3a98/dyHic7sm0OhQklliiMOQcT8tNIT4r1+3yKLYfqiXULpTm2GKAJHZYojDkHbpcwa2QmK3bX+HU5j22HGhidm0J8jO1qZ0KHJQpjztHFpVlUN7az66h/lvNQVbYcqmdifppfjmeMvziSKETkFhHZJiLdIlLWR7mrRWSniOwRke8EM0ZjzmRWqXc5D3+Nfqo83kp9ayfjh1qiMKHFqRrFVuAmYPnpCoiIG/gtcA0wDrhdRMYFJzxjzmxoeiIjs5P9Np9i66F6AKtRmJDjSKJQ1R2quvMMxaYBe1R1n6p2AH8B5gU+OmP6b3ZpJqv31dHWOfDlPLYcqifGJTbiyYScUO6jyAcO9nhc6XuuVyIyX0TWisjamprAbyxjDHj7Kdq7ulnrh+U8thyqZ1ROCgmx1pFtQkvAEoWIvCUiW3u5BaRWoKoLVbVMVcuysrICcQpjPmH68CHEumXA/RSqyraqBmt2MiEpJlAHVtUrBniIQ8CwHo8LfM8ZEzKS4mIoKxrC8t21fHcAx6mqb+NYcwcT8lP9Fpsx/hLKTU9rgFIRKRGROOA24GWHYzLmE2aPymTH4QaqG9vO+RjrKrxNV1MKB/srLGP8xqnhsTeKSCUwA3hNRJb4nh8qIosBVLUL+CqwBNgBPKOq25yI15i+XDLK29T5jx3V53yMdeXHSIpz22KAJiQFrOmpL6r6AvBCL89XAdf2eLwYWBzE0Iw5a+PyUhk2JJHXtx7htmmF53SMdQeOM3lYOjHuUK7km2hln0pjBkhEuHZCHu/uraW+pfOs39/c3sWOw42UFVmzkwlNliiM8YNrJubR6VHe2nH0rN+78eAJPN3K+ZYoTIiyRGGMH0wqSGNoWgKvbz181u9dta8Ot0ssUZiQZYnCGD8QEa6ekMfyXbU0tJ1d89OK3bVMKkgjNcH2yDahyRKFMX4yb/JQOjzdvLqp/7WK+tZONleeYNbIzABGZszAWKIwxk/OK0hjdE4KT689eObCPu/traNbYVaprSZgQpclCmP8RES4payATQdPsNO3pemZLN9dQ1Kcm8nD0gMbnDEDYInCGD+6cUo+MS7h2X7UKjzdypvbjnLp6GziYuxX0YQu+3Qa40cZyfFcMTaHv244dMalx9dVHKe2qZ2rJ+QGKTpjzo0lCmP87AsziznW3HHGWsXiLYeJj3Fx6ZjsIEVmzLmxRGGMn00rGcKUwnQeXr6PLk93r2Xauzy8urmKOaOzSI53ZCUdY/rNEoUxfiYifGXOSCqPt/Ln9w/0Wua1zYepbergjguLghydMWfPEoUxAXD52GwuGpHBL9/cxfHmjo+9pqr8/p1yRmYn2/wJExYsURgTACLCD68bT3N7F//yzEY83frha0u2HWXLoXq+OLMEEXEwSmP6xxKFMQEyOjeFH14/nrd31vCDl7bS3uXhQF0L339xC2NyU7i1rMDpEI3pF+tFMyaA7pheSOWxFh5evo/FWw7T2ukhPsbNr2+fYntPmLBhicKYABIRvnvtWGaVZvLyxioS49zcM6uEooxBTodmTL85kihE5BbgR8BYYJqqrj1NuXKgEfAAXapaFqwYjfGn2aVZzLb1nEyYcqpGsRW4CXi4H2UvVdXaAMdjjDHmNJzaM3sHYCM+jDEmDIR6b5oCb4rIOhGZ73QwxhgTjQJWoxCRt4DeVjv7nqq+1M/DzFLVQyKSDfxNRD5Q1eWnOd98YD5AYWHhOcVsjDHmkwKWKFT1Cj8c45Dv32oReQGYBvSaKFR1IbAQoKysTHsrY4wx5uyFbNOTiAwSkZST94G5eDvBjTHGBJEjiUJEbhSRSmAG8JqILPE9P1REFvuK5QArRWQT8D7wmqq+4US8xhgTzZwa9fQC8EIvz1cB1/ru7wMmBTk0Y4wxpxDVyGvOF5EaoOIc354JRNu8DbvmyBdt1wt2zWerSFV7nRUakYliIERkbbTNALdrjnzRdr1g1+xPIduZbYwxJjRYojDGGNMnSxSftNDpABxg1xz5ou16wa7Zb6yPwhhjTJ+sRmGMMaZPUZsoRORqEdkpIntE5Du9vB4vIk/7Xl8tIsUOhOk3/bjefxGR7SKyWUT+LiJFTsTpT2e65h7lPi0iKiJhP0KmP9csIrf6/q+3icifgx2jv/Xjs10oIm+LyAbf5/taJ+L0FxF5TESqRaTXlSrE61e+n8dmETl/wCdV1ai7AW5gLzAciAM2AeNOKfMAsMB3/zbgaafjDvD1Xgok+e7fH87X299r9pVLwbt+2CqgzOm4g/D/XApsAAb7Hmc7HXcQrnkhcL/v/jig3Om4B3jNFwPnA1tP8/q1wOuAABcCqwd6zmitUUwD9qjqPlXtAP4CzDulzDzgCd/954DLJXw30Djj9arq26ra4nu4CigIcoz+1p//Y4CfAP8PaAtmcAHSn2u+F/itqh4H74KbQY7R3/pzzQqk+u6nAVVBjM/v1LuC9rE+iswD/qBeq4B0EckbyDmjNVHkAwd7PK70PddrGVXtAuqBjKBE53/9ud6e7sH7jSScnfGafVXyYar6WjADC6D+/D+PAkaJyDsiskpErg5adIHRn2v+EXCHb325xcCDwQnNMWf7+35GTm2FakKUiNwBlAGXOB1LIImIC/gf4G6HQwm2GLzNT3Pw1hqXi8hEVT3hZFABdjvwuKr+UkRmAH8UkQmq2u10YOEiWmsUh4BhPR4X+J7rtYyIxOCtstYFJTr/68/1IiJXAN8DrlfV9iDFFihnuuYUYAKwVETK8bblvhzmHdr9+X+uBF5W1U5V3Q/swps4wlV/rvke4BkAVX0PSMC7JlKk6tfv+9mI1kSxBigVkRIRicPbWf3yKWVeBj7vu38z8A/19RSFoTNer4hMAR7GmyTCvd0aznDNqlqvqpmqWqyqxXj7Za5X1bXOhOsX/flcv4i3NoGIZOJtitoXxBj9rT/XfAC4HEBExuJNFDVBjTK4Xgbu8o1+uhCoV9XDAzlgVDY9qWqXiHwVWIJ31MRjqrpNRB4C1qrqy8CjeKuoe/B2HN3mXMQD08/r/QWQDDzr67M/oKrXOxb0APXzmiNKP695CTBXRLYDHuDbqhquNeX+XvM3gUUi8g28Hdt3h/GXPkTkKbzJPtPX7/JDIBZAVRfg7Ye5FtgDtABfGPA5w/jnZYwxJgiitenJGGNMP1miMMYY0ydLFMYYY/pkicIYY0yfLFEYY4zpkyUKE7FExCMiG3vcip2OyR9E5G4RqRGRR3yP54jIq6eUeVxEbu7jGL8QkSMi8q1Ax2vCX1TOozBRo1VVJ/f2gm+BRwnjZRyeVtWvnuubVfXbItLsz4BM5LIahYkaIlLs27fgD8BWYJiIfFtE1vjW7f9xj7LfE5FdIrJSRJ46+c1bRJaeXOZDRDJ9y38gIm7ft/STx/qy7/k5vvc8JyIfiMiTJ1chFpELRORdEdkkIu+LSIqILBeRyT3iWCkikwZwzWU9alRbRMQmTpmzZjUKE8kSRWSj7/5+4Bt41zX6vKquEpG5vsfT8K7d/7KIXAw0452JPxnv78h6YN0ZznUP3qUSLhCReOAdEXnT99oUYDze5a3fAWaKyPvA08BnVHWNiKQCrXhXBLgb+LqIjAISVHVTP651do9rBSgEXvUtSTIZvM1NwBv9OJYxH2OJwkSyjzU9+fooKnxr9APM9d02+B4n400cKcALJ/fnEJH+LPcxFzivR79Amu9YHcD7qlrpO9ZGoBjvsvWHVXUNgKo2+F5/Fvh3Efk28EXg8X5e6wpV/VSPa/3Y+0TkM3g3u5nbz+MZ8yFLFCba9GyXF+CnqvpwzwIi8vU+3t/FR022Cacc60FVXXLKseYAPVfi9dDH752qtojI3/BuPnMrMLWPWPpFRCbg3ZPhYlX1DPR4JvpYH4WJZkuAL4pIMoCI5ItINt6tUW8QkUQRSQGu6/Gecj76433zKce6X0RifccaJSKD+jj3TiBPRC7wlU8R73L2AI8AvwLWnNyJ7lyJSDrwFHCXqkbyiqkmgKxGYaKWqr7pW3b6PV//chNwh6quF5Gn8e6/XI13KeuT/ht4RkTmAz13xnsEb5PSel9ndQ1wQx/n7vA1B/1aRBLx9k9cATSp6joRaQB+74fLnAcU4V099eS5J/vhuCaK2OqxxpyBiPwI7x/w/w7S+YYCS4ExvQ3fFZG7gbKBDI/1HedHBPG6TPiypidjQoiI3AWsBr7XxxyPVuCakxPuzvE8vwDu4ON9Nsb0ymoUxhhj+mQ1CmOMMX2yRGGMMaZPliiMMcb0yRKFMcaYPlmiMMYY0ydLFMYYY/r0/wPj31hs86zqXwAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "mler_data = extreme.mler_coefficients(RAO,pm,1)\n", "\n", "mler_data.plot(y='WaveSpectrum', ylabel='Conditioned wave spectrum [m^2-s]', xlabel='Frequency [Hz]')\n", "mler_data.plot(y='Phase', ylabel='[rad]', xlabel='Frequency [Hz]')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From here, we can choose to export these coefficients to feed into other high fidelity software. However, if we wanted to get a specific height of the incoming wave, we can renormalize the wave amplitude. To do this, several inputs need to be generated in addition to the existing coefficients. \n", "\n", "The first is a dictionary containing information pertinent to creating time series. This dict can be easily generated using ```MLERsimulation```. In this example, the input dictionary contains the default values of this function, but the format is shown in case a user wants to adjust the parameters for their specific use case. The second input is the wave number, which was obtained using the ```wave_number``` function from the resource module. \n", "\n", "Finally, we decide to renormalize the wave amplitude to a desired peak height (peak to MSL). Combining all of these inputs into ```MLERwaveAmpNormalize``` gives us our new normalized mler wave spectrum. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/cmichel/MHKiT/mhkit/wave/resource.py:722: RuntimeWarning: invalid value encountered in true_divide\n", " yi = xi*xi/np.power(1.0-np.exp(-np.power(xi,2.4908)),0.4015)\n" ] } ], "source": [ "# generate parameters dict\n", "params = (\n", " ('startTime',-150.0),\n", " ('endTime',150.0),\n", " ('dT',1.0),\n", " ('T0',0.0),\n", " ('startX',-300.0),\n", " ('endX',300.0),\n", " ('dX',1.0),\n", " ('X0',0.0)\n", ")\n", "parameters = dict(params)\n", "\n", "# get simulation dict\n", "sim = extreme.mler_simulation(parameters=parameters)\n", "\n", "# generate wave number k\n", "k = resource.wave_number(wave_freq,70)\n", "k = k.fillna(0)\n", "\n", "peakHeightDesired = Hs/2 * 1.9\n", "mler_norm = extreme.mler_wave_amp_normalize(peakHeightDesired, mler_data, sim, k.k.values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a final step, a user might need to convert the MLER coefficients into a time series for input into higher fidelity software. We can do this by using the ```MLERexportTimeSeries``` function. The result is a dataframe showing the wave height [m] and the linear response [*] indexed by time. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "mler_ts = extreme.mler_export_time_series(RAO.values,mler_norm,sim,k.k.values)\n", "mler_ts.plot(xlabel='Time (s)',ylabel='[m] / [*]',xlim=[-100,100],grid=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "interpreter": { "hash": "6acc4428af86beefd6565514d05fe9ce8e024621fafadd3627cdac7b7bd68bc4" }, "kernelspec": { "display_name": "Python 3.8.10 64-bit ('MHKdev': conda)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.10" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }