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Merge pull request #47 from symbiotic-engineering/iris-design-of-expe…
…riments Iris design of experiments
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@@ -43,4 +43,7 @@ code-coverage/ | |
| test-results/ | ||
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| # mat data files | ||
| *.mat | ||
| *.mat | ||
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| # excel cache | ||
| ~$*.xlsx | ||
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| % Runs one-at-a-time design of experiments | ||
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| clear;clc;close all | ||
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| p = parameters(); | ||
| b = var_bounds(); | ||
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| DOE_strategy = 'ratios'; % 'sample' or 'bounds' or 'ratios' | ||
| n = 20; | ||
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| if strcmp(DOE_strategy,'sample') | ||
| X = [ 20 10 30; % outer diameter of float | ||
| .3 .1 .5; % inner diameter ratio of float | ||
| 1.5 1.2 2; % outer diameter of reaction plate | ||
| 1 2 3; % material | ||
| 10 1 20; % Number of WECs in array | ||
| 10 5 50 % D_int | ||
| 2*pi/7 2*pi/8 2*pi/9 | ||
| ]; | ||
| elseif strcmp(DOE_strategy,'bounds') | ||
| X = [ b.D_f_nom, linspace(b.D_f_min, b.D_f_max, n); | ||
| b.D_s_ratio_nom, linspace(b.D_s_ratio_min, b.D_s_ratio_max, n); | ||
| b.h_f_ratio_nom, linspace(b.h_f_ratio_min, b.h_f_ratio_max, n); | ||
| b.T_s_ratio_nom, linspace(b.T_s_ratio_min, b.T_s_ratio_max, n); | ||
| b.F_max_nom, linspace(b.F_max_min, b.F_max_max, n); | ||
| b.B_p_nom, linspace(b.B_p_min, b.B_p_max, n); | ||
| b.w_n_nom, linspace(b.w_n_min, b.w_n_max, n) ]; | ||
| ratios = X./X(:,1); | ||
| elseif strcmp(DOE_strategy,'ratios') | ||
| ratios = logspace(log10(1/3),log10(3),n); | ||
| ratios = [1, ratios(ratios~=1)]; | ||
| X = [b.D_f_nom b.D_s_ratio_nom b.h_f_ratio_nom b.T_s_ratio_nom b.F_max_nom b.B_p_nom b.w_n_nom b.M_nom]' * ratios; | ||
| end | ||
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| X_nom = X(:,1); | ||
| design_size = size(X); | ||
| num_vars = design_size(1); | ||
| num_vals_per_var = design_size(2); | ||
| num_vals_swept = num_vals_per_var - 1; % don't sweep 1st value of each variable (corresponding to ratio 1) | ||
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| % don't sweep material | ||
| idx_material = 8; | ||
| X(idx_material,:) = b.M_nom * ones(1,n+1); | ||
| num_vars_swept = num_vars - 1; | ||
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| % initialize variables | ||
| LCOE = X*inf; | ||
| P_var = X*inf; | ||
| opt_idx = zeros(num_vars,1); | ||
| recommended = zeros(num_vars,2); | ||
| number_runs = 1 + num_vars_swept * num_vals_swept; % nominal run plus all sweeps | ||
| failed = cell(number_runs,1); | ||
| power = zeros(number_runs,1); | ||
| FOS = zeros(number_runs,1); | ||
| X_ins = zeros(number_runs, num_vars); | ||
| design = 0; | ||
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| % run design of experiments | ||
| for i = 1:num_vars_swept | ||
| X_in = X_nom; | ||
| for j = 1:num_vals_per_var | ||
| if i == 1 || j~=1 % prevent rerunning nominal multiple times | ||
| changed_entry = X(i,j); | ||
| if ~isnan(changed_entry) | ||
| design = design+1; | ||
| X_in(i) = changed_entry; | ||
| X_ins(design,:) = X_in; | ||
| [LCOE_temp, P_var_temp, P_matrix, g, val] = simulation(X_in,p); | ||
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| % only add to results if first 12 constraints are feasible | ||
| g(13) = 0; | ||
| g(14) = 0; | ||
| [feasible, which_failed] = is_feasible(g, b); | ||
| if feasible | ||
| LCOE(i,j) = LCOE_temp; | ||
| P_var(i,j) = P_var_temp; | ||
| else | ||
| LCOE(i,j) = NaN; | ||
| P_var(i,j) = NaN; | ||
| end | ||
| failed{design} = which_failed; | ||
| end | ||
| end | ||
| end | ||
| [~, opt_idx(i)] = min(LCOE(i,:)); | ||
| recommended(i,:) = [X(i,opt_idx(i)), opt_idx(i)]; | ||
| end | ||
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| % create table for display | ||
| results = array2table(X_ins, 'VariableNames', b.var_names); | ||
| LCOE = LCOE'; | ||
| P_var = P_var'; | ||
| results = addvars(results, round(LCOE(LCOE~=Inf),2), round(P_var(P_var~=Inf),1), failed, ... | ||
| 'NewVariableNames', {'LCOE ($/kWh)','c_v (%)','Failed Constraints'}); | ||
| disp(results) | ||
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| % plot pareto curve for comparison | ||
| pareto_curve_heuristics() | ||
| figure(2) | ||
| plot(LCOE, P_var, '*--') | ||
| xlabel('LCOE') | ||
| ylabel('P_{var}') | ||
| title('Design of Experiments Pareto Front') | ||
| l = legend(b.var_names_pretty); | ||
| improvePlot | ||
| l.Location = 'bestoutside'; | ||
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| %% sensitivities plot | ||
| [ratios_sorted,idx] = sort(ratios); | ||
| LCOE(1,:) = LCOE(1,1); % fill in nominal LCOE results for each DV where it wasn't repeatedly tested | ||
| P_var(1,:) = P_var(1,1); | ||
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| figure | ||
| t = tiledlayout(2,1); | ||
| t.TileSpacing = 'compact'; | ||
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| ax1 = nexttile(1); | ||
| plot(ratios_sorted,LCOE(idx,:).') | ||
| ylabel('LCOE ($/kWh)') | ||
| grid on | ||
| ax2 = nexttile(2); | ||
| plot(ratios_sorted,P_var(idx,:).') | ||
| ylabel('Power c_v (%)') | ||
| grid on | ||
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| title(t,'Design of Experiments Results','FontWeight','bold','FontSize',20) | ||
| l = legend(b.var_names_pretty); | ||
| l.Location = 'bestoutside'; | ||
| grid on | ||
| linkaxes([ax1,ax2],'x'); | ||
| xlabel('Design Variable Ratio (-)') | ||
| xticklabels(ax1,{}) | ||
| xticks(ax2,xticks(ax1)) | ||
| improvePlot | ||
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