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Merge pull request #175 from akeeste/wecsim_example_2
WEC-Sim Power performance example
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‎examples/data/wave/mcr_mhkit_power.mat‎

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‎examples/data/wave/pacwave_cluster_32.csv‎

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,Te,Hm0,weights,Tp,J
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‎examples/wecsim_example.html‎

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‎examples/wecsim_example.m‎

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% Mooring, MoorDyn, and PTOSim classes.
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% Relative location and filename of simulated WEC-Sim data (run with mooring)
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filename = './examples/data/RM3MooringMatrix_matlabWorkspace.mat'
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filename = './data/RM3MooringMatrix_matlabWorkspace.mat'
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load(filename, 'output');
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%% 2. WEC-Sim Simulated Data
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xlim([0, 4]);
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%%
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% Calculate Peak Wave Period (Tp) and Significant Wave Height (Hm0)
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% Calculate Wave Statistics using MHKiT wave module functions
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Tp = peak_period(ws_spectrum);
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Hm0 = significant_wave_height(ws_spectrum);
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fprintf('Peak Wave Period (Tp) = %.4f s\n', Tp);
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fprintf('Significant Wave Height (Hm0) = %.4f m\n', Hm0);
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Tp = peak_period(ws_spectrum);
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Te = energy_period(ws_spectrum);
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wave_statistics_data = {
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% Parameter, Value, Units
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'Significant Wave Height, Hm0', Hm0, 'm';
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'Peak Wave Period, Tp', Tp, 's';
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'Energy Period, Te', Te, 's';
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};
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wave_statistics_table = cell2table(wave_statistics_data, 'VariableNames', {'Parameter', 'Value', 'Units'});
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wave_statistics_table.Value = round(wave_statistics_table.Value, 4);
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wave_statistics_table

‎examples/wecsim_example.mlx‎

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‎examples/wecsim_power_performance_example.html‎

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‎examples/wecsim_power_performance_example.m‎

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%% MHKiT WEC-Sim Power Performance Example
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% This notebook demonstrates using the MHKiT wave module and WEC-Sim together
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% to perform a resource characterization study in MHKiT, simulate representative
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% cases with WEC-Sim, and visualize the results in MHKiT to estimate MAEP (Mean
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% Annual Energy Production).
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%
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% 1. Characterize the available resource at a location
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%
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% - See the PacWave example notebook
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%
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% 2. Write a WEC-Sim batch file for the given clusters
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%
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% 3. Simulate the device _in WEC-Sim_.
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%
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% - Ensure that the spectra used in WEC-Sim is identical to the one used in
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% MHKiT.
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%
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% 4. Load WEC-Sim batch results
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%
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% 5. Assess results and visualize quantities of interest
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%
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% This example uses WEC-Sim to simulate the [Oscillating Surge Wave Energy Converter
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% (OSWEC)](https://wec-sim.github.io/WEC-Sim/main/user/tutorials.html#oscillating-surge-wec-oswec),
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% a flap-type device.
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%% 1. Characterize the available resource at a location
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% This example will use an abbreviated version of `PacWave_resource_characterization_example.ipynb`.
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%
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% For full details on downloading, calculating, and visualizing the k-means
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% clusters representation of the site's wave resouce, see that example.
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%
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% We select the N=32 cluster as it's total energy flux is closet to the total
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% energy flux of the site considering all wave conditions. We will load the PacWave
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% example output, which can be easily saved after running the example with the
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% command `results[32].to_csv("pacwave_cluster_32.csv")`. We will start this example
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% by reading in that csv output and formatting it for WEC-Sim.
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% Relative location and filename of simulated WEC-Sim data (run with mooring)
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filename = './data/wave/pacwave_cluster_32.csv'
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results = readtable(filename)
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%% 2. Write a WEC-Sim batch file for the given clusters
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% WEC-Sim MCR (multiple condition run) files should contain a structure `mcr`
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% that contains two variables: `header` and `cases`. Each column of `header` and
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% `cases` denotes a variable and it's value respectively. Each row is distinct
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% simulation. WEC-Sim defines waves using the significant wave height and peak
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% period. We will isolate these values from the results of the cluster analysis
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% and create a dictionary that is written to the `.mat` file.
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mcr = struct();
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mcr.header = {'waves.height','waves.period'};
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mcr.cases = [results.Hm0 results.Tp]
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save('mcr_mhkit.mat', 'mcr');
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%% 3. Simulate the device _in WEC-Sim_
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% Now that the MCR file is created, we need to go simulate WEC performance in
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% these wave conditions using WEC-Sim. To recreate the data used in the next step,
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% use the created MCR file with WEC-Sim's OSWEC example. For an accurate comparison
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% to the power calculated in the resource characterization, we should ensure that
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% the WEC-Sim cases use irregular JONSWAP wave spectra as in the PacWave example.
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%
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%
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%
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% For convenience in this demonstration, we enforce OSWEC model stability in
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% the extreme wave conditions by arbitrarily applying a large PTO stiffness and
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% damping in the wecSimInputFile.m:
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% pto(1).stiffness = 1e5;
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% pto(1).damping = 5e7;
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%%
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% To reduce the amount of extranenous data saved for this example, we limit
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% the WEC-Sim output to the PTO's power output in the `userDefinedFunctions.m`
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% script:
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% if exist('imcr','var')
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% if imcr == 1
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% nmcr = size(mcr.cases,1);
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% power = nan(1, nmcr);
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% end
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%
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% iRampEnd = simu.rampTime./simu.dtOut + 1;
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% power(imcr) = -mean(output.ptos(1).powerInternalMechanics(iRampEnd:end,5));
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%
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% if imcr == nmcr
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% % % Save output
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% save('mcr_mhkit_power.mat', 'power');
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% end
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% end
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% bodies = output.bodies;
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%% 4. Load WEC-Sim batch results
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% Note that in this example we do not save the entire WEC-Sim `output` structure
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% for each case. See the `wecsim_example.ipynb` for information on loading the
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% WEC-Sim responseClass. Here, the output is one array of average power output
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% that we will load and compare to the resource characterization.
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%
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% Note that the power output [W] is significantly larger than the energy flux
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% [W/m] due to the large width of the OSWEC.
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% Relative location and filename of simulated WEC-Sim data (run with mooring)
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filename = './data/wave/mcr_mhkit_power.mat';
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% Load the WEC-Sim output data which contains the variable `power`.
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load(filename)
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results.Power = power'
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%% 5. Assess results and visualize quantities of interest
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% Now that we have loaded the OSWEC's modeled power, we can assess it's performance
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% relative to the incoming wave and calculate the mean annual energy production
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% (MAEP) using MHKiT.
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results.CW = capture_length(results.Power, results.J)';
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oswec_width = 18;
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results.CWR = results.CW / oswec_width
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%%
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% Calculate and display the mean annual energy production.
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CW.values = results.CW;
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J.values = results.J;
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weights.values = results.weights;
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MAEP = mean_annual_energy_production_matrix(CW, J, weights) / 1000 % kWh

‎examples/wecsim_power_performance_example.mlx‎

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