{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Extreme Conditions Modeling - Contour Approach\n", "\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 contour approach. \n", "\n", "The contour approach consists of the following steps: \n", "1. Sample the environmental contour for the return period of interest at N points near the maximum $H_s$.\n", "2. For each sample $(H_{s}, T_{e})_i$ calculate the short-term (e.g. 3-hours) extreme for the quantity of interest (e.g. WEC motions or mooring tension).\n", "3. Select the sea state with the highest short-term expected value for the quantity of interest as the design sea state, and some metric (e.g. 95th percentile) of the distribution as the extreme/design response. \n", "\n", "**NOTE:** Prior to running this example it is recommended to become familiar with `environmental_contours_example.ipynb` and `short_term_extremes_example.ipynb` since some code blocks are adapted from those examples and used here without the additional description. \n", "\n", "We start by importing the relevant modules, including `waves.contours` submodule which includes the contour function, and `loads.extreme` which inlcudes the short-term extreme functions. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from mhkit.wave import resource, contours, graphics\n", "from mhkit.loads import extreme\n", "import matplotlib.pyplot as plt\n", "from mhkit.wave.io import ndbc\n", "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Obtain and Process NDBC Buouy Data\n", "The first step will be obtaining the environmental data and creating the contours.\n", "See `environmental_contours_example.ipynb` for more details and explanations of how this is being done in the following code block. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "parameter = 'swden'\n", "buoy_number = '46022'\n", "ndbc_available_data = ndbc.available_data(parameter, buoy_number)\n", "\n", "years_of_interest = ndbc_available_data[ndbc_available_data.year < 2013]\n", "\n", "filenames = years_of_interest['filename']\n", "ndbc_requested_data = ndbc.request_data(parameter, filenames)\n", "\n", "ndbc_data = {}\n", "for year in ndbc_requested_data:\n", " year_data = ndbc_requested_data[year]\n", " ndbc_data[year] = ndbc.to_datetime_index(parameter, year_data)\n", "\n", "Hm0_list = []\n", "Te_list = []\n", "\n", "# Iterate over each year and save the result in the initalized dictionary\n", "for year in ndbc_data:\n", " year_data = ndbc_data[year]\n", " Hm0_list.append(resource.significant_wave_height(year_data.T))\n", " Te_list.append(resource.energy_period(year_data.T))\n", "\n", "# Concatenate list of Series into a single DataFrame\n", "Te = pd.concat(Te_list, axis=0)\n", "Hm0 = pd.concat(Hm0_list, axis=0)\n", "Hm0_Te = pd.concat([Hm0, Te], axis=1)\n", "\n", "# Drop any NaNs created from the calculation of Hm0 or Te\n", "Hm0_Te.dropna(inplace=True)\n", "# Sort the DateTime index\n", "Hm0_Te.sort_index(inplace=True)\n", "\n", "Hm0_Te_clean = Hm0_Te[Hm0_Te.Hm0 < 20]\n", "\n", "Hm0 = Hm0_Te_clean.Hm0.values\n", "Te = Hm0_Te_clean.Te.values\n", "\n", "dt = (Hm0_Te_clean.index[2]-Hm0_Te_clean.index[1]).seconds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Sampling\n", "The first step is to create the environmental contour for the return period of interest, which is 100-years in this case,\n", "using the `wave.contours.environmental_contours`. See `environmental_contours_example.ipynb` for more details on using this function. \n", "\n", "Next we choose 5 sample sea states along this contour. We choose 5 equally spaced energy period values, between 15-22 seconds, to sample the contour. We then obtain the significant wave height for each sample using the `wave.contours.samples_contour` function." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# 100 year contour\n", "period = 100.0\n", "copula = contours.environmental_contours(Hm0, Te, dt, period, 'PCA')\n", "hs_contour = copula['PCA_x1']\n", "te_contour = copula['PCA_x2']\n", "\n", "# 5 samples \n", "te_samples = np.linspace(15, 22, 5)\n", "hs_samples = contours.samples_contour(te_samples, te_contour, hs_contour);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now plot the contour and samples." ] }, { "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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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# plot\n", "fig, ax = plt.subplots(figsize=(8, 4))\n", "ax = graphics.plot_environmental_contour(\n", " Te, Hm0, te_contour, hs_contour,\n", " data_label='bouy data', contour_label='100-year contour',\n", " x_label='Energy Period, $Te$ [s]',\n", " y_label='Sig. wave height, $Hm0$ [m]', ax=ax)\n", "ax.plot(te_samples, hs_samples, 'ro', label='samples')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Short-Term Extreme Distributions\n", "Many different methods for short-term extremes were adapted from WDRT, and a summary and examples can be found in `short_term_extremes_example.ipynb`. \n", "The response quantity of interest is typically related to the WEC itself, e.g. maximum heave displacement, PTO extension, or load on the mooring lines. \n", "This requires running a simulation (e.g. WEC-Sim) for each of the 5 sampled sea states $(H_s, T_e)_i$. \n", "For the sake of example we will consider the wave elevation as the quantity of interest (can be thought as a proxy for heave motion in this example). \n", "Wave elevation time-series for a specific sea state can be created quickly without running any external software. \n", "\n", "**NOTE:** The majority of the for-loop below is simply creating the synthetic data (wave elevation time series). In a realistic case the variables `time` and `data` describing each time series would be obtained externally, e.g. through simulation software such as WEC-Sim or CFD. For this reason the details of creating the synthetic data are not presented here, instead assume for each sea state there is time-series data available. \n", "\n", "The last lines of the for-loop create the short-term extreme distribution from the time-series using the `loads.extreme.short_term_extreme` function. The short-term period will be 3-hours and we will use 1-hour \"simulations\" and the Weibul-tail-fitting method to estimate the 3-hour short-term extreme distributions for each of the 5 samples.\n", "\n", "For more details see `short_term_extremes_example.ipynb` and \n", "\n", "> [3] Michelén Ströfer, Carlos A., and Ryan Coe. 2015. “Comparison of Methods for Estimating Short-Term Extreme Response of Wave Energy Converters.” In OCEANS 2015 - MTS/IEEE Washington, 1–6. IEEE." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sea state 1/5. (Hs, Te) = (12.29610748127811 m, 15.0 s). Tp = 16.599299336182796 s\n", "Sea state 2/5. (Hs, Te) = (13.015332697148471 m, 16.75 s). Tp = 18.535884258737457 s\n", "Sea state 3/5. (Hs, Te) = (13.259537838635064 m, 18.5 s). Tp = 20.472469181292116 s\n", "Sea state 4/5. (Hs, Te) = (12.922246647881362 m, 20.25 s). Tp = 22.409054103846774 s\n", "Sea state 5/5. (Hs, Te) = (11.849171354690998 m, 22.0 s). Tp = 24.345639026401436 s\n" ] } ], "source": [ "# create the short-term extreme distribution for each sample sea state\n", "t_st = 3.0 * 60.0 * 60.0\n", "gamma = 3.3 \n", "t_sim = 1.0 * 60.0 * 60.0\n", "\n", "ste_all = []\n", "i = 0\n", "n = len(hs_samples)\n", "for hs, te in zip(hs_samples, te_samples):\n", " tp = te / (0.8255 + 0.03852*gamma - 0.005537*gamma**2 + 0.0003154*gamma**3)\n", " i += 1\n", " print(f\"Sea state {i}/{n}. (Hs, Te) = ({hs} m, {te} s). Tp = {tp} s\")\n", " # time & frequency arrays\n", " f0 = 1.0/t_sim\n", " T_min = tp/10.0 # s\n", " f_max = 1.0/T_min\n", " Nf = int(f_max/f0)\n", " time = np.linspace(0, t_sim, 2*Nf+1)\n", " f = np.linspace(f0, f_max, Nf)\n", " # spectrum\n", " S = resource.jonswap_spectrum(f, tp, hs, gamma)\n", " # 1-hour elevation time-series\n", " data = resource.surface_elevation(S, time).values.squeeze()\n", " # 3-hour extreme distribution\n", " ste = extreme.short_term_extreme(time, data, t_st, 'peaks_weibull_tail_fit')\n", " ste_all.append(ste)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Select Design Sea State and Design Response\n", "Finally, we will choose the sea state with the largest expected value as the design sea state. \n", "We will then use the 95th percentile of the short-term extreme distribution as the extreme design response. \n", "\n", "First we find the sampled sea state with the largest expected value, which is Hs = 12.24m, Te=15s. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\hivanov\\AppData\\Local\\Continuum\\anaconda3\\envs\\MHKdev\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:2738: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n", " vals = integrate.quad(fun, lb, ub, **kwds)[0] / invfac\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Design sea state (Hs, Te): (13.015332697148471 m, 16.75 s)\n" ] } ], "source": [ "ev = []\n", "for ste in ste_all:\n", " ev.append(ste.expect())\n", "max_ind = np.argmax(ev)\n", "\n", "hs_design = hs_samples[max_ind]\n", "te_design = te_samples[max_ind]\n", "print(f\"Design sea state (Hs, Te): ({hs_design} m, {te_design} s)\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we choose the 95th percentile of the short-term extreme distribution at this sea state as the extreme design response. \n", "This gives an extreme design elevation of 14.1 meters." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Design (extreme) reponse: 15.53183951172818 m\n" ] } ], "source": [ "ste_design = ste_all[max_ind]\n", "response_design = ste_design.ppf(0.95)\n", "\n", "print(f\"Design (extreme) reponse: {response_design} m\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 4 }