Dataset Open Access

Wind tunnel experiment of a micro wind farm model

Bossuyt Juliaan; Meneveau Charles; Meyers Johan


JSON Export

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    "description": "<p>Simultaneous strain gage measurements of sixty porous disk models, in a scaled wind farm with one hundred models, and for fifty-six different layouts.&nbsp;</p>\n\n<p>For detailed information about the experimental setup and wind farm layouts see:&nbsp;</p>\n\n<p>Bossuyt, J., Meneveau, C., &amp; Meyers, J. (2018). Effect of layout on asymptotic boundary layer regime in deep wind farms. <em>Physical Review Fluids. See also:</em>&nbsp;https://arxiv.org/abs/1808.09579 .</p>\n\n<p>For more information about the experimental design of the porous disk models, see also:</p>\n\n<p>Bossuyt, J., Howland, M. F., Meneveau, C., &amp; Meyers, J. (2017). Measurement of unsteady loading and power output variability in a micro wind farm model in a wind tunnel.&nbsp;<em>Experiments in Fluids</em>,&nbsp;<em>58</em>(1), 1.&nbsp;http://doi.org/10.1007/s00348-016-2278-6</p>\n\n<p>&nbsp;Bossuyt, J., Meneveau, C., &amp; Meyers, J. (2017). Wind farm power fluctuations and spatial sampling of turbulent boundary layers.&nbsp;<em>Journal of Fluid Mechanics</em>,&nbsp;<em>823</em>, 329-344.&nbsp;http://doi.org/10.1017/jfm.2017.328</p>\n\n<p>&nbsp;</p>\n\n<p>The data contains matrices &#39;WF_U&#39;, &#39;x&#39;, and &#39;y&#39;, and variable &#39;fs&#39; for each layout.&nbsp;<br>\nThe matrix &#39;WF_U&#39; 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 &#39;x&#39;, and &#39;y&#39; 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 &#39;fs&#39; contains the sampling frequency in Hz, at which all 60 porous disks are simultaneously sampled.</p>\n\n<p>--------------------------------------------------------<br>\nExample code to load data in Matlab :<br>\n--------------------------------------------------------<br>\nfilename = &nbsp;&#39;U_C1_1.h5&#39;;<br>\nfileID = H5F.open(filename,&#39;H5F_ACC_RDONLY&#39;,&#39;H5P_DEFAULT&#39;);</p>\n\n<p>datasetID = H5D.open(fileID,&#39;WF_U&#39;);<br>\nWF_U = H5D.read(datasetID,&#39;H5ML_DEFAULT&#39;,&#39;H5S_ALL&#39;,&#39;H5S_ALL&#39;,&#39;H5P_DEFAULT&#39;);<br>\nH5D.close(datasetID);</p>\n\n<p>datasetID = H5D.open(fileID,&#39;fs&#39;);<br>\nfs = H5D.read(datasetID,&#39;H5ML_DEFAULT&#39;,&#39;H5S_ALL&#39;,&#39;H5S_ALL&#39;,&#39;H5P_DEFAULT&#39;);<br>\nH5D.close(datasetID);</p>\n\n<p>datasetID = H5D.open(fileID,&#39;x&#39;);<br>\nx = H5D.read(datasetID,&#39;H5ML_DEFAULT&#39;,&#39;H5S_ALL&#39;,&#39;H5S_ALL&#39;,&#39;H5P_DEFAULT&#39;);<br>\nH5D.close(datasetID);</p>\n\n<p>datasetID = H5D.open(fileID,&#39;y&#39;);<br>\ny = H5D.read(datasetID,&#39;H5ML_DEFAULT&#39;,&#39;H5S_ALL&#39;,&#39;H5S_ALL&#39;,&#39;H5P_DEFAULT&#39;);<br>\nH5D.close(datasetID);</p>\n\n<p>H5F.close(fileID);</p>\n\n<p>--------------------------------------------------------<br>\nExample code to load data in Python:<br>\n--------------------------------------------------------<br>\nimport h5py<br>\nfilename = &#39;U_C1_1.h5&#39;<br>\nf = h5py.File(filename, &#39;r&#39;)</p>\n\n<p>U = f[&#39;WF_U&#39;][()]<br>\nx = f[&#39;x&#39;][()]<br>\ny = f[&#39;y&#39;][()]<br>\nfs = f[&#39;fs&#39;][0][0]<br>\nf.close()</p>\n\n<p>--------------------------------------------------------<br>\nExample 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<br>\n--------------------------------------------------------<br>\nWF_cases_selected = 1:7;</p>\n\n<p>folder = &#39;/&#39;;% folder with files</p>\n\n<p>WF_cases_l = {&#39;U_C1&#39;;&#39;U_C2&#39;;&#39;NU1_C1&#39;;&#39;NU1_C2&#39;;&#39;NU2_C1&#39;;&#39;NU2_C2&#39;;&#39;NU2_C3&#39;};% name of layout variations<br>\nWF_cases_n = [6, 7, 11, 8, 11, 7, 6]; % &#39;number of layout variations for each case</p>\n\n<p>WF_data.x = cell( length(WF_cases_selected) , 1);% x - coordinates of porous disk locations<br>\nWF_data.y = cell( length(WF_cases_selected) , 1);% y - coordinates of porous disk locations<br>\nWF_data.shift = cell( length(WF_cases_selected) , 1);% spanwise shift of layout series<br>\nWF_data.fs = cell( length(WF_cases_selected) , 1);<br>\nWF_data.WF_Pm = cell( length(WF_cases_selected) , 1);<br>\nWF_data.WF_Um = cell( length(WF_cases_selected) , 1);<br>\nWF_data.WF_U_rms = cell( length(WF_cases_selected) , 1);</p>\n\n<p><br>\nfor i = 1 : length(WF_cases_selected)<br>\n&nbsp; &nbsp;&nbsp;<br>\n&nbsp; &nbsp; WF_data_case = struct;<br>\n&nbsp; &nbsp; WF_data_case.x = &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;cell( WF_cases_n(i) , 1);<br>\n&nbsp; &nbsp; WF_data_case.y = &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;cell( WF_cases_n(i) , 1);<br>\n&nbsp; &nbsp; WF_data_case.shift = &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;cell( WF_cases_n(i) , 1);<br>\n&nbsp; &nbsp; WF_data_case.fs = &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; cell( WF_cases_n(i) , 1);<br>\n&nbsp; &nbsp; WF_data_case.WF_Pm = &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;cell( WF_cases_n(i) , 1);<br>\n&nbsp; &nbsp; WF_data_case.WF_Um = &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;cell( WF_cases_n(i) , 1);<br>\n&nbsp; &nbsp; WF_data_case.WF_U_rms = &nbsp; &nbsp; &nbsp; cell( WF_cases_n(i) , 1);<br>\n&nbsp; &nbsp;&nbsp;<br>\n&nbsp; &nbsp; for j = 1:WF_cases_n(i)<br>\n&nbsp; &nbsp; &nbsp; &nbsp; clc<br>\n&nbsp; &nbsp; &nbsp; &nbsp; i<br>\n&nbsp; &nbsp; &nbsp; &nbsp; j<br>\n&nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br>\n&nbsp; &nbsp; &nbsp; &nbsp; WF_data_var = struct;<br>\n&nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br>\n&nbsp; &nbsp; &nbsp; &nbsp; %read the file<br>\n&nbsp; &nbsp; &nbsp; &nbsp; filename = [folder WF_cases_l{i} &#39;_&#39; num2str(j) &#39;.h5&#39;];<br>\n&nbsp; &nbsp; &nbsp; &nbsp; fileID = H5F.open(filename,&#39;H5F_ACC_RDONLY&#39;,&#39;H5P_DEFAULT&#39;);<br>\n&nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br>\n&nbsp; &nbsp; &nbsp; &nbsp; datasetID = H5D.open(fileID,&#39;WF_U&#39;);<br>\n&nbsp; &nbsp; &nbsp; &nbsp; WF_data_var.WF_U = H5D.read(datasetID,&#39;H5ML_DEFAULT&#39;,&#39;H5S_ALL&#39;,&#39;H5S_ALL&#39;,&#39;H5P_DEFAULT&#39;);<br>\n&nbsp; &nbsp; &nbsp; &nbsp; H5D.close(datasetID);<br>\n&nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br>\n&nbsp; &nbsp; &nbsp; &nbsp; datasetID = H5D.open(fileID,&#39;fs&#39;);<br>\n&nbsp; &nbsp; &nbsp; &nbsp; WF_data_case.fs{j} = H5D.read(datasetID,&#39;H5ML_DEFAULT&#39;,&#39;H5S_ALL&#39;,&#39;H5S_ALL&#39;,&#39;H5P_DEFAULT&#39;);<br>\n&nbsp; &nbsp; &nbsp; &nbsp; H5D.close(datasetID);<br>\n&nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br>\n&nbsp; &nbsp; &nbsp; &nbsp; datasetID = H5D.open(fileID,&#39;x&#39;);<br>\n&nbsp; &nbsp; &nbsp; &nbsp; WF_data_case.x{j} = H5D.read(datasetID,&#39;H5ML_DEFAULT&#39;,&#39;H5S_ALL&#39;,&#39;H5S_ALL&#39;,&#39;H5P_DEFAULT&#39;);<br>\n&nbsp; &nbsp; &nbsp; &nbsp; H5D.close(datasetID);<br>\n&nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br>\n&nbsp; &nbsp; &nbsp; &nbsp; datasetID = H5D.open(fileID,&#39;y&#39;);<br>\n&nbsp; &nbsp; &nbsp; &nbsp; WF_data_case.y{j} = H5D.read(datasetID,&#39;H5ML_DEFAULT&#39;,&#39;H5S_ALL&#39;,&#39;H5S_ALL&#39;,&#39;H5P_DEFAULT&#39;);<br>\n&nbsp; &nbsp; &nbsp; &nbsp; H5D.close(datasetID);<br>\n&nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br>\n&nbsp; &nbsp; &nbsp; &nbsp; H5F.close(fileID);<br>\n&nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br>\n&nbsp; &nbsp; &nbsp; &nbsp; WF_data_var.WF_P = WF_data_var.WF_U.^3;</p>\n\n<p>&nbsp; &nbsp; &nbsp; &nbsp; % Time averaged power<br>\n&nbsp; &nbsp; &nbsp; &nbsp; WF_data_case.WF_Pm{j} = mean(WF_data_var.WF_P,3);<br>\n&nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br>\n&nbsp; &nbsp; &nbsp; &nbsp; % normalize by power in first row: Pi/P1<br>\n&nbsp; &nbsp; &nbsp; &nbsp; WF_data_case.WF_Pm{j} = WF_data_case.WF_Pm{j}./mean(WF_data_case.WF_Pm{j}(1,:));<br>\n&nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br>\n&nbsp; &nbsp; &nbsp; &nbsp; % Time averaged velocity<br>\n&nbsp; &nbsp; &nbsp; &nbsp; WF_data_case.WF_Um{j} = mean(WF_data_var.WF_U,3);<br>\n&nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br>\n&nbsp; &nbsp; &nbsp; &nbsp; % u_rms --&gt; TI<br>\n&nbsp; &nbsp; &nbsp; &nbsp; WF_data_case.WF_U_rms{j} = std(WF_data_var.WF_U,[],3);<br>\n&nbsp; &nbsp; end<br>\n&nbsp; &nbsp;&nbsp;<br>\n&nbsp; &nbsp; WF_data.x{i} &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;= WF_data_case.x;<br>\n&nbsp; &nbsp; WF_data.y{i} &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;= WF_data_case.y;<br>\n&nbsp; &nbsp; WF_data.fs{i} &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; = WF_data_case.fs;<br>\n&nbsp; &nbsp; WF_data.WF_Pm{i} &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;= WF_data_case.WF_Pm;<br>\n&nbsp; &nbsp; WF_data.WF_Um{i} &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;= WF_data_case.WF_Um;<br>\n&nbsp; &nbsp; WF_data.WF_U_rms{i} &nbsp; &nbsp; &nbsp; &nbsp; = WF_data_case.WF_U_rms;<br>\n&nbsp; &nbsp;&nbsp;<br>\n&nbsp; &nbsp; %determine spanwise shift for plot legends<br>\n&nbsp; &nbsp; tmp1 = WF_data.y{i}{j-1};<br>\n&nbsp; &nbsp; tmp2 = WF_data.y{i}{j};<br>\n&nbsp; &nbsp; dy = diff( [tmp1(:,1) &nbsp;tmp2(:,1)] ,1,2);<br>\n&nbsp; &nbsp; dy = max(dy(abs(dy)&gt;0));<br>\n&nbsp; &nbsp; WF_data.shift{i} &nbsp; = 0:dy:(WF_cases_n(i)-1)*dy;<br>\n&nbsp; &nbsp;&nbsp;<br>\nend</p>\n\n<p>%%<br>\nline_tick = {&#39;o-&#39;,&#39;*-&#39;,&#39;+-&#39;,&#39;d-&#39;,&#39;s-&#39;,&#39;^-&#39;,&#39;v-&#39;,&#39;&lt;-&#39;,&#39;&gt;-&#39;,&#39;p-&#39;,&#39;h-&#39;};<br>\nline_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;</p>\n\n<p>legend_items = cell(size(WF_cases_selected));<br>\nfor i = 1:length(legend_items)<br>\n&nbsp; &nbsp; legend_items{i} = strrep(WF_cases_l{i},&#39;_&#39;,&#39;-&#39;);<br>\nend</p>\n\n<p>%% average power entire farm<br>\nrow_start = 1;<br>\nrow_end = 19;<br>\nf1 = figure;<br>\nset(gcf,&#39;paperposition&#39;,[0,0,8.4,4.9])<br>\nhold on</p>\n\n<p>for i = 1 : length(WF_cases_selected)<br>\n&nbsp; &nbsp; tmp_P = zeros(size(WF_data.shift{i}));<br>\n&nbsp; &nbsp; for j = &nbsp;1:WF_cases_n(i)<br>\n&nbsp; &nbsp; &nbsp; &nbsp; tmp_P(j) = mean(mean( WF_data.WF_Pm{i}{j}(row_start:row_end,:)));<br>\n&nbsp; &nbsp; end<br>\n&nbsp; &nbsp; plot( WF_data.shift{i} , tmp_P, line_tick{i} ,&#39;Color&#39;, line_color(i,:) ,&#39;MarkerFaceColor&#39;, line_color(i,:) )<br>\nend</p>\n\n<p>% manualy plot errorbars<br>\nfor i = 1:length(WF_cases_selected)<br>\n&nbsp; &nbsp; tmp_P = zeros(size(WF_data.shift{i}));<br>\n&nbsp; &nbsp; for j = &nbsp;1:WF_cases_n(i)<br>\n&nbsp; &nbsp; &nbsp; &nbsp; tmp_P(j) = mean(mean( WF_data.WF_Pm{i}{j}(row_start:row_end,:)));<br>\n&nbsp; &nbsp; end<br>\n&nbsp; &nbsp; px = WF_data.shift{i} ;<br>\n&nbsp; &nbsp; py = tmp_P;<br>\n&nbsp; &nbsp; pw = 0.05;<br>\n&nbsp; &nbsp; pe = zeros(size(px))+0.01;%for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids.&nbsp;<br>\n&nbsp; &nbsp; for j = &nbsp;1:WF_cases_n(i)<br>\n&nbsp; &nbsp; &nbsp; &nbsp; plot( &nbsp;[px(j)-pw/2 &nbsp;px(j)+pw/2] , [py(j)+pe(j) &nbsp;py(j)+pe(j)],&#39;-&#39;, &#39;Color&#39;, line_color(i,:),&#39;LineWidth&#39;,0.5)<br>\n&nbsp; &nbsp; &nbsp; &nbsp; plot( &nbsp;[px(j)-pw/2 &nbsp;px(j)+pw/2] , [py(j)-pe(j) &nbsp;py(j)-pe(j)],&#39;-&#39;, &#39;Color&#39;, line_color(i,:),&#39;LineWidth&#39;,0.5)<br>\n&nbsp; &nbsp; &nbsp; &nbsp; plot( &nbsp;[px(j) &nbsp;px(j)],[py(j)-pe(j) &nbsp;py(j)+pe(j)],&#39;:&#39;, &#39;Color&#39;, line_color(i,:),&#39;LineWidth&#39;,0.5)<br>\n&nbsp; &nbsp; end<br>\nend<br>\nxlabel(&#39;\\Delta_y [D]&#39;)<br>\nylabel(&#39;$\\langle P_i &nbsp;/P_1\\rangle_{1}^{19}$&#39;,&#39;Interpreter&#39;,&#39;Latex&#39;)<br>\nbox(&#39;on&#39;)<br>\nylim([0.35 0.66])<br>\nxlim([-0.1 2.6])<br>\nlegend1 = legend(legend_items&#39;);<br>\nset(legend1,&#39;Location&#39;,&#39;southeast&#39;);<br>\nprint(f1, &#39;WF_Pm_all&#39;,&#39;-dpng&#39;,&#39;-r300&#39;)</p>\n\n<p>%% &nbsp;average power end of farm<br>\nrow_start = 16;<br>\nrow_end = 19;<br>\nf2 = figure;<br>\nset(gcf,&#39;paperposition&#39;,[0,0,8.4,4.9])<br>\nhold on</p>\n\n<p>for i = 1 : length(WF_cases_selected)<br>\n&nbsp; &nbsp; tmp_P = zeros(size(WF_data.shift{i}));<br>\n&nbsp; &nbsp; for j = &nbsp;1:WF_cases_n(i)<br>\n&nbsp; &nbsp; &nbsp; &nbsp; tmp_P(j) = mean(mean( WF_data.WF_Pm{i}{j}(row_start:row_end,:)));<br>\n&nbsp; &nbsp; end<br>\n&nbsp; &nbsp; plot( WF_data.shift{i} , tmp_P, line_tick{i} ,&#39;Color&#39;, line_color(i,:) ,&#39;MarkerFaceColor&#39;, line_color(i,:) )<br>\nend</p>\n\n<p>% manualy plot errorbars<br>\nfor i = 1:length(WF_cases_selected)<br>\n&nbsp; &nbsp; tmp_P = zeros(size(WF_data.shift{i}));<br>\n&nbsp; &nbsp; for j = &nbsp;1:WF_cases_n(i)<br>\n&nbsp; &nbsp; &nbsp; &nbsp; tmp_P(j) = mean(mean( WF_data.WF_Pm{i}{j}(row_start:row_end,:)));<br>\n&nbsp; &nbsp; end<br>\n&nbsp; &nbsp; px = WF_data.shift{i} ;<br>\n&nbsp; &nbsp; py = tmp_P;<br>\n&nbsp; &nbsp; pw = 0.05;<br>\n&nbsp; &nbsp; pe = zeros(size(px))+0.02; %for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids.&nbsp;<br>\n&nbsp; &nbsp; for j = &nbsp;1:WF_cases_n(i)<br>\n&nbsp; &nbsp; &nbsp; &nbsp; plot( &nbsp;[px(j)-pw/2 &nbsp;px(j)+pw/2] , [py(j)+pe(j) &nbsp;py(j)+pe(j)],&#39;-&#39;, &#39;Color&#39;, line_color(i,:),&#39;LineWidth&#39;,0.5)<br>\n&nbsp; &nbsp; &nbsp; &nbsp; plot( &nbsp;[px(j)-pw/2 &nbsp;px(j)+pw/2] , [py(j)-pe(j) &nbsp;py(j)-pe(j)],&#39;-&#39;, &#39;Color&#39;, line_color(i,:),&#39;LineWidth&#39;,0.5)<br>\n&nbsp; &nbsp; &nbsp; &nbsp; plot( &nbsp;[px(j) &nbsp;px(j)],[py(j)-pe(j) &nbsp;py(j)+pe(j)],&#39;:&#39;, &#39;Color&#39;, line_color(i,:),&#39;LineWidth&#39;,0.5)<br>\n&nbsp; &nbsp; end<br>\nend<br>\nxlabel(&#39;\\Delta_y [D]&#39;)<br>\nylabel(&#39;$\\langle P_i &nbsp;/P_1\\rangle_{16}^{19}$&#39;,&#39;Interpreter&#39;,&#39;Latex&#39;)<br>\nbox(&#39;on&#39;)<br>\nylim([0.27 0.52])<br>\nxlim([-0.1 2.6])<br>\nlegend1 = legend(legend_items&#39;);<br>\nset(legend1,&#39;Location&#39;,&#39;southeast&#39;);<br>\nprint(f2, &#39;WF_Pm_end&#39;, &#39;-dpng&#39;,&#39;-r300&#39;)</p>\n\n<p>%% plot average unsteady loading total farm<br>\nrow_start = 1;<br>\nrow_end = 19;<br>\nf3 = figure;<br>\nset(gcf,&#39;paperposition&#39;,[0,0,8.4,4.9])<br>\nhold on<br>\nfor i = 1 : length(WF_cases_selected)<br>\n&nbsp; &nbsp; tmp_TI = zeros(size(WF_data.shift{i}));<br>\n&nbsp; &nbsp; for j = &nbsp;1:WF_cases_n(i)<br>\n&nbsp; &nbsp; &nbsp; &nbsp; tmp_TI(j) = &nbsp;mean(mean(WF_data.WF_U_rms{i}{j}(row_start:row_end,:)./WF_data.WF_Um{i}{j}(row_start:row_end,:)))*100;<br>\n&nbsp; &nbsp; end<br>\n&nbsp; &nbsp; plot( WF_data.shift{i} , tmp_TI , line_tick{i} ,&#39;Color&#39;, line_color(i,:) &nbsp;,&#39;MarkerFaceColor&#39;, line_color(i,:))<br>\nend</p>\n\n<p>% manualy plot errorbars<br>\nfor i = 1:length(WF_cases_selected)<br>\n&nbsp; &nbsp; tmp_TI = zeros(size(WF_data.shift{i}));<br>\n&nbsp; &nbsp; for j = &nbsp;1:WF_cases_n(i)<br>\n&nbsp; &nbsp; &nbsp; &nbsp; tmp_TI(j) = &nbsp;mean(mean(WF_data.WF_U_rms{i}{j}(row_start:row_end,:)./WF_data.WF_Um{i}{j}(row_start:row_end,:)))*100;<br>\n&nbsp; &nbsp; end<br>\n&nbsp; &nbsp; px = WF_data.shift{i} ;<br>\n&nbsp; &nbsp; py = tmp_TI;<br>\n&nbsp; &nbsp; pw = 0.05;<br>\n&nbsp; &nbsp; pe = zeros(size(px))+ 0.004*tmp_TI;%for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids.&nbsp;<br>\n&nbsp; &nbsp; for j = &nbsp;1:WF_cases_n(i)<br>\n&nbsp; &nbsp; &nbsp; &nbsp; plot( &nbsp;[px(j)-pw/2 &nbsp;px(j)+pw/2] , [py(j)+pe(j) &nbsp;py(j)+pe(j)],&#39;-&#39;, &#39;Color&#39;, line_color(i,:),&#39;LineWidth&#39;,0.5)<br>\n&nbsp; &nbsp; &nbsp; &nbsp; plot( &nbsp;[px(j)-pw/2 &nbsp;px(j)+pw/2] , [py(j)-pe(j) &nbsp;py(j)-pe(j)],&#39;-&#39;, &#39;Color&#39;, line_color(i,:),&#39;LineWidth&#39;,0.5)<br>\n&nbsp; &nbsp; &nbsp; &nbsp; plot( &nbsp;[px(j) &nbsp;px(j)],[py(j)-pe(j) &nbsp;py(j)+pe(j)],&#39;:&#39;, &#39;Color&#39;, line_color(i,:),&#39;LineWidth&#39;,0.5)<br>\n&nbsp; &nbsp; end<br>\nend<br>\nxlabel(&#39;\\Delta_y [D]&#39;)<br>\nylabel(&#39;$ \\langle TI \\rangle_{1}^{19} [\\%]$&#39;,&#39;Interpreter&#39;,&#39;Latex&#39;)<br>\nbox(&#39;on&#39;)<br>\nxlim([-0.1 2.6])<br>\nlegend1 = legend(legend_items&#39;);<br>\nset(legend1,&#39;Location&#39;,&#39;northeast&#39;);<br>\nprint(f3, &#39;WF_TI_all&#39;,&#39;-dpng&#39;,&#39;-r300&#39;)</p>\n\n<p>%% plot average unsteady loading end of farm<br>\nrow_start = 16;<br>\nrow_end = 19;<br>\nf4 = figure;<br>\nset(gcf,&#39;paperposition&#39;,[0,0,8.4,4.9])<br>\nhold on<br>\nfor i = 1 : length(WF_cases_selected)<br>\n&nbsp; &nbsp; tmp_TI = zeros(size(WF_data.shift{i}));<br>\n&nbsp; &nbsp; for j = &nbsp;1:WF_cases_n(i)<br>\n&nbsp; &nbsp; &nbsp; &nbsp; tmp_TI(j) = &nbsp;mean(mean(WF_data.WF_U_rms{i}{j}(row_start:row_end,:)./WF_data.WF_Um{i}{j}(row_start:row_end,:)))*100;<br>\n&nbsp; &nbsp; end<br>\n&nbsp; &nbsp; plot( WF_data.shift{i} , tmp_TI , line_tick{i} ,&#39;Color&#39;, line_color(i,:) &nbsp;,&#39;MarkerFaceColor&#39;, line_color(i,:))<br>\nend</p>\n\n<p>% manualy plot errorbars<br>\nfor i = 1:length(WF_cases_selected)<br>\n&nbsp; &nbsp; tmp_TI = zeros(size(WF_data.shift{i}));<br>\n&nbsp; &nbsp; for j = &nbsp;1:WF_cases_n(i)<br>\n&nbsp; &nbsp; &nbsp; &nbsp; tmp_TI(j) = &nbsp;mean(mean(WF_data.WF_U_rms{i}{j}(row_start:row_end,:)./WF_data.WF_Um{i}{j}(row_start:row_end,:)))*100;<br>\n&nbsp; &nbsp; end<br>\n&nbsp; &nbsp; px = WF_data.shift{i} ;<br>\n&nbsp; &nbsp; py = tmp_TI;<br>\n&nbsp; &nbsp; pw = 0.05;<br>\n&nbsp; &nbsp; pe = zeros(size(px))+ 0.01*tmp_TI;%for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids.&nbsp;<br>\n&nbsp; &nbsp; for j = &nbsp;1:WF_cases_n(i)<br>\n&nbsp; &nbsp; &nbsp; &nbsp; plot( &nbsp;[px(j)-pw/2 &nbsp;px(j)+pw/2] , [py(j)+pe(j) &nbsp;py(j)+pe(j)],&#39;-&#39;, &#39;Color&#39;, line_color(i,:),&#39;LineWidth&#39;,0.5)<br>\n&nbsp; &nbsp; &nbsp; &nbsp; plot( &nbsp;[px(j)-pw/2 &nbsp;px(j)+pw/2] , [py(j)-pe(j) &nbsp;py(j)-pe(j)],&#39;-&#39;, &#39;Color&#39;, line_color(i,:),&#39;LineWidth&#39;,0.5)<br>\n&nbsp; &nbsp; &nbsp; &nbsp; plot( &nbsp;[px(j) &nbsp;px(j)],[py(j)-pe(j) &nbsp;py(j)+pe(j)],&#39;:&#39;, &#39;Color&#39;, line_color(i,:),&#39;LineWidth&#39;,0.5)<br>\n&nbsp; &nbsp; end<br>\nend<br>\nxlabel(&#39;\\Delta_y [D]&#39;)<br>\nylabel(&#39;$ \\langle TI \\rangle_{16}^{19} [\\%]$&#39;,&#39;Interpreter&#39;,&#39;Latex&#39;)<br>\nbox(&#39;on&#39;)<br>\nxlim([-0.1 2.6])<br>\nlegend1 = legend(legend_items&#39;);<br>\nset(legend1,&#39;Location&#39;,&#39;northeast&#39;);<br>\nprint(f4, &#39;WF_TI_end&#39;,&#39;-dpng&#39;,&#39;-r300&#39;)</p>\n\n<p>&nbsp;</p>", 
    "license": {
      "id": "CC-BY-NC-4.0"
    }, 
    "title": "Wind tunnel experiment of a micro wind farm model", 
    "relations": {
      "version": [
        {
          "count": 1, 
          "index": 0, 
          "parent": {
            "pid_type": "recid", 
            "pid_value": "1467410"
          }, 
          "is_last": true, 
          "last_child": {
            "pid_type": "recid", 
            "pid_value": "1467411"
          }
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      ]
    }, 
    "communities": [
      {
        "id": "wind_energy"
      }
    ], 
    "grants": [
      {
        "code": "306471", 
        "links": {
          "self": "https://zenodo.org/api/grants/10.13039/501100000780::306471"
        }, 
        "title": "Active Wind Farms: Optimization and Control of Atmospheric Energy Extraction in Gigawatt Wind Farms", 
        "acronym": "ACTIVEWINDFARMS", 
        "program": "FP7", 
        "funder": {
          "doi": "10.13039/501100000780", 
          "acronyms": [
            "EC"
          ], 
          "name": "European Commission", 
          "links": {
            "self": "https://zenodo.org/api/funders/10.13039/501100000780"
          }
        }
      }, 
      {
        "code": "1243482", 
        "links": {
          "self": "https://zenodo.org/api/grants/10.13039/100000001::1243482"
        }, 
        "title": "PIRE: USA/Europe Partnership for Integrated Research and Education in Wind Energy Intermittency: From Wind Farm Turbulence to Economic Management", 
        "acronym": "", 
        "program": "Office of the Director", 
        "funder": {
          "doi": "10.13039/100000001", 
          "acronyms": [
            "NSF"
          ], 
          "name": "National Science Foundation", 
          "links": {
            "self": "https://zenodo.org/api/funders/10.13039/100000001"
          }
        }
      }
    ], 
    "references": [
      "Bossuyt, J., Meneveau, C., & Meyers, J. (2018). Effect of layout on asymptotic boundary layer regime in deep wind farms. Physical Review Fluids.", 
      "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.\u00a0Experiments in Fluids,\u00a058(1), 1.\u00a0http://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.\u00a0Journal of Fluid Mechanics,\u00a0823, 329-344.\u00a0http://doi.org/10.1017/jfm.2017.328"
    ], 
    "keywords": [
      "wind tunnel experiment, wind farm, porous disk, strain gage, layout"
    ], 
    "publication_date": "2018-10-19", 
    "creators": [
      {
        "orcid": "0000-0001-8787-1877", 
        "affiliation": "KU Leuven", 
        "name": "Bossuyt Juliaan"
      }, 
      {
        "orcid": "0000-0001-6947-3605", 
        "affiliation": "Johns Hopkins University", 
        "name": "Meneveau Charles"
      }, 
      {
        "orcid": "0000-0002-2828-4397", 
        "affiliation": "KU Leuven", 
        "name": "Meyers Johan"
      }
    ], 
    "access_right": "open", 
    "resource_type": {
      "type": "dataset", 
      "title": "Dataset"
    }, 
    "related_identifiers": [
      {
        "scheme": "doi", 
        "relation": "isVersionOf", 
        "identifier": "10.5281/zenodo.1467410"
      }
    ]
  }
}
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