zenodoopen

Wind tunnel experiment of a micro wind farm model

Simultaneous strain gage measurements of sixty porous disk models, in a scaled wind farm with one hundred models, and for fifty-six different layouts.  For detailed information about the experimental setup and wind farm layouts see:  Bossuyt, J., Meneveau, C., & Meyers, J. (2018). Effect of layout on asymptotic boundary layer regime in deep wind farms. Physical Review Fluids. See also: https://arxiv.org/abs/1808.09579 . For more information about the experimental design of the porous disk models, see also: Bossuyt, J., Howland, M. F., Meneveau, C., & Meyers, J. (2017). Measurement of unsteady loading and power output variability in a micro wind farm model in a wind tunnel. Experiments in Fluids, 58(1), 1. http://doi.org/10.1007/s00348-016-2278-6  Bossuyt, J., Meneveau, C., & Meyers, J. (2017). Wind farm power fluctuations and spatial sampling of turbulent boundary layers. Journal of Fluid Mechanics, 823, 329-344. http://doi.org/10.1017/jfm.2017.328   The data contains matrices 'WF_U', 'x', and 'y', and variable 'fs' for each layout.  The matrix 'WF_U' contains the reconstructed velocity signal in m/s measured by each porous disk, and has size ( 20 , 3 , number of time samples), with 20 the number of porous disk rows, and 3 the number of streamwise aligned porous disk columns in the wind farm. Matrices 'x', and 'y' have size (20,3) and contain the locations of each instrumented porous disk in units of disk diameter D = 0.03m. It is important to note that the wind farm has one extra column of non-instrumented porous disk models on each side, for a total of 20x5=100 porous disk models.The variable 'fs' contains the sampling frequency in Hz, at which all 60 porous disks are simultaneously sampled. -------------------------------------------------------- Example code to load data in Matlab : -------------------------------------------------------- filename =  'U_C1_1.h5'; fileID = H5F.open(filename,'H5F_ACC_RDONLY','H5P_DEFAULT'); datasetID = H5D.open(fileID,'WF_U'); WF_U = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT'); H5D.close(datasetID); datasetID = H5D.open(fileID,'fs'); fs = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT'); H5D.close(datasetID); datasetID = H5D.open(fileID,'x'); x = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT'); H5D.close(datasetID); datasetID = H5D.open(fileID,'y'); y = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT'); H5D.close(datasetID); H5F.close(fileID); -------------------------------------------------------- Example code to load data in Python: -------------------------------------------------------- import h5py filename = 'U_C1_1.h5' f = h5py.File(filename, 'r') U = f['WF_U'][()] x = f['x'][()] y = f['y'][()] fs = f['fs'][0][0] f.close() -------------------------------------------------------- Example code to generate figures 15 and 16 of Bossuyt et al. (2018). Effect of layout on asymptotic boundary layer regime in deep wind farms. Physical Review Fluids, in Matlab -------------------------------------------------------- WF_cases_selected = 1:7; folder = '/';% folder with files WF_cases_l = {'U_C1';'U_C2';'NU1_C1';'NU1_C2';'NU2_C1';'NU2_C2';'NU2_C3'};% name of layout variations WF_cases_n = [6, 7, 11, 8, 11, 7, 6]; % 'number of layout variations for each case WF_data.x = cell( length(WF_cases_selected) , 1);% x - coordinates of porous disk locations WF_data.y = cell( length(WF_cases_selected) , 1);% y - coordinates of porous disk locations WF_data.shift = cell( length(WF_cases_selected) , 1);% spanwise shift of layout series WF_data.fs = cell( length(WF_cases_selected) , 1); WF_data.WF_Pm = cell( length(WF_cases_selected) , 1); WF_data.WF_Um = cell( length(WF_cases_selected) , 1); WF_data.WF_U_rms = cell( length(WF_cases_selected) , 1); for i = 1 : length(WF_cases_selected)          WF_data_case = struct;     WF_data_case.x =              cell( WF_cases_n(i) , 1);     WF_data_case.y =              cell( WF_cases_n(i) , 1);     WF_data_case.shift =          cell( WF_cases_n(i) , 1);     WF_data_case.fs =             cell( WF_cases_n(i) , 1);     WF_data_case.WF_Pm =          cell( WF_cases_n(i) , 1);     WF_data_case.WF_Um =          cell( WF_cases_n(i) , 1);     WF_data_case.WF_U_rms =       cell( WF_cases_n(i) , 1);          for j = 1:WF_cases_n(i)         clc         i         j                  WF_data_var = struct;                  %read the file         filename = [folder WF_cases_l{i} '_' num2str(j) '.h5'];         fileID = H5F.open(filename,'H5F_ACC_RDONLY','H5P_DEFAULT');                  datasetID = H5D.open(fileID,'WF_U');         WF_data_var.WF_U = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');         H5D.close(datasetID);                  datasetID = H5D.open(fileID,'fs');         WF_data_case.fs{j} = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');         H5D.close(datasetID);                  datasetID = H5D.open(fileID,'x');         WF_data_case.x{j} = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');         H5D.close(datasetID);                  datasetID = H5D.open(fileID,'y');         WF_data_case.y{j} = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');         H5D.close(datasetID);                  H5F.close(fileID);                  WF_data_var.WF_P = WF_data_var.WF_U.^3;         % Time averaged power         WF_data_case.WF_Pm{j} = mean(WF_data_var.WF_P,3);                  % normalize by power in first row: Pi/P1         WF_data_case.WF_Pm{j} = WF_data_case.WF_Pm{j}./mean(WF_data_case.WF_Pm{j}(1,:));                  % Time averaged velocity         WF_data_case.WF_Um{j} = mean(WF_data_var.WF_U,3);                  % u_rms --> TI         WF_data_case.WF_U_rms{j} = std(WF_data_var.WF_U,[],3);     end          WF_data.x{i}                = WF_data_case.x;     WF_data.y{i}                = WF_data_case.y;     WF_data.fs{i}               = WF_data_case.fs;     WF_data.WF_Pm{i}            = WF_data_case.WF_Pm;     WF_data.WF_Um{i}            = WF_data_case.WF_Um;     WF_data.WF_U_rms{i}         = WF_data_case.WF_U_rms;          %determine spanwise shift for plot legends     tmp1 = WF_data.y{i}{j-1};     tmp2 = WF_data.y{i}{j};     dy = diff( [tmp1(:,1)  tmp2(:,1)] ,1,2);     dy = max(dy(abs(dy)>0));     WF_data.shift{i}   = 0:dy:(WF_cases_n(i)-1)*dy;      end %% line_tick = {'o-','*-','+-','d-','s-','^-','v-','<-','>-','p-','h-'}; line_color = [51,160,44; 141,211,199; 31,120,180; 106,61,154; 227,26,28; 177,89,40; 255,127,0; 166,206,227]./255; legend_items = cell(size(WF_cases_selected)); for i = 1:length(legend_items)     legend_items{i} = strrep(WF_cases_l{i},'_','-'); end %% average power entire farm row_start = 1; row_end = 19; f1 = figure; set(gcf,'paperposition',[0,0,8.4,4.9]) hold on for i = 1 : length(WF_cases_selected)     tmp_P = zeros(size(WF_data.shift{i}));     for j =  1:WF_cases_n(i)         tmp_P(j) = mean(mean( WF_data.WF_Pm{i}{j}(row_start:row_end,:)));     end     plot( WF_data.shift{i} , tmp_P, line_tick{i} ,'Color', line_color(i,:) ,'MarkerFaceColor', line_color(i,:) ) end % manualy plot errorbars for i = 1:length(WF_cases_selected)     tmp_P = zeros(size(WF_data.shift{i}));     for j =  1:WF_cases_n(i)         tmp_P(j) = mean(mean( WF_data.WF_Pm{i}{j}(row_start:row_end,:)));     end     px = WF_data.shift{i} ;     py = tmp_P;     pw = 0.05;     pe = zeros(size(px))+0.01;%for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids.      for j =  1:WF_cases_n(i)         plot(  [px(j)-pw/2  px(j)+pw/2] , [py(j)+pe(j)  py(j)+pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)         plot(  [px(j)-pw/2  px(j)+pw/2] , [py(j)-pe(j)  py(j)-pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)         plot(  [px(j)  px(j)],[py(j)-pe(j)  py(j)+pe(j)],':', 'Color', line_color(i,:),'LineWidth',0.5)     end end xlabel('\Delta_y [D]') ylabel('$\langle P_i  /P_1\rangle_{1}^{19}$','Interpreter','Latex') box('on') ylim([0.35 0.66]) xlim([-0.1 2.6]) legend1 = legend(legend_items'); set(legend1,'Location','southeast'); print(f1, 'WF_Pm_all','-dpng','-r300') %%  average power end of farm row_start = 16; row_end = 19; f2 = figure; set(gcf,'paperposition',[0,0,8.4,4.9]) hold on for i = 1 : length(WF_cases_selected)     tmp_P = zeros(size(WF_data.shift{i}));     for j =  1:WF_cases_n(i)         tmp_P(j) = mean(mean( WF_data.WF_Pm{i}{j}(row_start:row_end,:)));     end     plot( WF_data.shift{i} , tmp_P, line_tick{i} ,'Color', line_color(i,:) ,'MarkerFaceColor', line_color(i,:) ) end % manualy plot errorbars for i = 1:length(WF_cases_selected)     tmp_P = zeros(size(WF_data.shift{i}));     for j =  1:WF_cases_n(i)         tmp_P(j) = mean(mean( WF_data.WF_Pm{i}{j}(row_start:row_end,:)));     end     px = WF_data.shift{i} ;     py = tmp_P;     pw = 0.05;     pe = zeros(size(px))+0.02; %for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids.      for j =  1:WF_cases_n(i)         plot(  [px(j)-pw/2  px(j)+pw/2] , [py(j)+pe(j)  py(j)+pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)         plot(  [px(j)-pw/2  px(j)+pw/2] , [py(j)-pe(j)  py(j)-pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)         plot(  [px(j)  px(j)],[py(j)-pe(j)  py(j)+pe(j)],':', 'Color', line_color(i,:),'LineWidth',0.5)     end end xlabel('\Delta_y [D]') ylabel('$\langle P_i  /P_1\rangle_{16}^{19}$','Interpreter','Latex') box('on') ylim([0.27 0.52]) xlim([-0.1 2.6]) legend1 = legend(legend_items'); set(legend1,'Location','southeast'); print(f2, 'WF_Pm_end', '-dpng','-r300') %% plot average unsteady loading total farm row_start = 1; row_end = 19; f3 = figure; set(gcf,'paperposition',[0,0,8.4,4.9]) hold on for i = 1 : length(WF_cases_selected)     tmp_TI = zeros(size(WF_data.shift{i}));     for j =  1:WF_cases_n(i)         tmp_TI(j) =  mean(mean(WF_data.WF_U_rms{i}{j}(row_start:row_end,:)./WF_data.WF_Um{i}{j}(row_start:row_end,:)))*100;     end     plot( WF_data.shift{i} , tmp_TI , line_tick{i} ,'Color', line_color(i,:)  ,'MarkerFaceColor', line_color(i,:)) end % manualy plot errorbars for i = 1:length(WF_cases_selected)     tmp_TI = zeros(size(WF_data.shift{i}));     for j =  1:WF_cases_n(i)         tmp_TI(j) =  mean(mean(WF_data.WF_U_rms{i}{j}(row_start:row_end,:)./WF_data.WF_Um{i}{j}(row_start:row_end,:)))*100;     end     px = WF_data.shift{i} ;     py = tmp_TI;     pw = 0.05;     pe = zeros(size(px))+ 0.004*tmp_TI;%for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids.      for j =  1:WF_cases_n(i)         plot(  [px(j)-pw/2  px(j)+pw/2] , [py(j)+pe(j)  py(j)+pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)         plot(  [px(j)-pw/2  px(j)+pw/2] , [py(j)-pe(j)  py(j)-pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)         plot(  [px(j)  px(j)],[py(j)-pe(j)  py(j)+pe(j)],':', 'Color', line_color(i,:),'LineWidth',0.5)     end end xlabel('\Delta_y [D]') ylabel('$ \langle TI \rangle_{1}^{19} [\%]$','Interpreter','Latex') box('on') xlim([-0.1 2.6]) legend1 = legend(legend_items'); set(legend1,'Location','northeast'); print(f3, 'WF_TI_all','-dpng','-r300') %% plot average unsteady loading end of farm row_start = 16; row_end = 19; f4 = figure; set(gcf,'paperposition',[0,0,8.4,4.9]) hold on for i = 1 : length(WF_cases_selected)     tmp_TI = zeros(size(WF_data.shift{i}));     for j =  1:WF_cases_n(i)         tmp_TI(j) =  mean(mean(WF_data.WF_U_rms{i}{j}(row_start:row_end,:)./WF_data.WF_Um{i}{j}(row_start:row_end,:)))*100;     end     plot( WF_data.shift{i} , tmp_TI , line_tick{i} ,'Color', line_color(i,:)  ,'MarkerFaceColor', line_color(i,:)) end % manualy plot errorbars for i = 1:length(WF_cases_selected)     tmp_TI = zeros(size(WF_data.shift{i}));     for j =  1:WF_cases_n(i)         tmp_TI(j) =  mean(mean(WF_data.WF_U_rms{i}{j}(row_start:row_end,:)./WF_data.WF_Um{i}{j}(row_start:row_end,:)))*100;     end     px = WF_data.shift{i} ;     py = tmp_TI;     pw = 0.05;     pe = zeros(size(px))+ 0.01*tmp_TI;%for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids.      for j =  1:WF_cases_n(i)         plot(  [px(j)-pw/2  px(j)+pw/2] , [py(j)+pe(j)  py(j)+pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)         plot(  [px(j)-pw/2  px(j)+pw/2] , [py(j)-pe(j)  py(j)-pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)         plot(  [px(j)  px(j)],[py(j)-pe(j)  py(j)+pe(j)],':', 'Color', line_color(i,:),'LineWidth',0.5)     end end xlabel('\Delta_y [D]') ylabel('$ \langle TI \rangle_{16}^{19} [\%]$','Interpreter','Latex') box('on') xlim([-0.1 2.6]) legend1 = legend(legend_items'); set(legend1,'Location','northeast'); print(f4, 'WF_TI_end','-dpng','-r300')

ShareScore

44/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship: 8
Harmonization: 8
Access: 16
Reuse readiness: 8
Engagement: 4

Topics

wind tunnel experiment, wind farm, porous disk, strain gage, layout