DOI: 10.5281/zenodo.1467410
title: Wind Tunnel Experiment Of A Micro Wind Farm Model

description: 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. <em>Physical Review Fluids. See also:</em> 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. <em>Experiments in Fluids</em>, <em>58</em>(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. <em>Journal of Fluid Mechanics</em>, <em>823</em>, 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. <br>
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.

--------------------------------------------------------<br>
Example code to load data in Matlab :<br>
--------------------------------------------------------<br>
filename =  'U_C1_1.h5';<br>
fileID = H5F.open(filename,'H5F_ACC_RDONLY','H5P_DEFAULT');

datasetID = H5D.open(fileID,'WF_U');<br>
WF_U = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br>
H5D.close(datasetID);

datasetID = H5D.open(fileID,'fs');<br>
fs = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br>
H5D.close(datasetID);

datasetID = H5D.open(fileID,'x');<br>
x = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br>
H5D.close(datasetID);

datasetID = H5D.open(fileID,'y');<br>
y = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br>
H5D.close(datasetID);

H5F.close(fileID);

--------------------------------------------------------<br>
Example code to load data in Python:<br>
--------------------------------------------------------<br>
import h5py<br>
filename = 'U_C1_1.h5'<br>
f = h5py.File(filename, 'r')

U = f['WF_U'][()]<br>
x = f['x'][()]<br>
y = f['y'][()]<br>
fs = f['fs'][0][0]<br>
f.close()

--------------------------------------------------------<br>
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<br>
--------------------------------------------------------<br>
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<br>
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<br>
WF_data.y = cell( length(WF_cases_selected) , 1);% y - coordinates of porous disk locations<br>
WF_data.shift = cell( length(WF_cases_selected) , 1);% spanwise shift of layout series<br>
WF_data.fs = cell( length(WF_cases_selected) , 1);<br>
WF_data.WF_Pm = cell( length(WF_cases_selected) , 1);<br>
WF_data.WF_Um = cell( length(WF_cases_selected) , 1);<br>
WF_data.WF_U_rms = cell( length(WF_cases_selected) , 1);

<br>
for i = 1 : length(WF_cases_selected)<br>
    <br>
    WF_data_case = struct;<br>
    WF_data_case.x =              cell( WF_cases_n(i) , 1);<br>
    WF_data_case.y =              cell( WF_cases_n(i) , 1);<br>
    WF_data_case.shift =          cell( WF_cases_n(i) , 1);<br>
    WF_data_case.fs =             cell( WF_cases_n(i) , 1);<br>
    WF_data_case.WF_Pm =          cell( WF_cases_n(i) , 1);<br>
    WF_data_case.WF_Um =          cell( WF_cases_n(i) , 1);<br>
    WF_data_case.WF_U_rms =       cell( WF_cases_n(i) , 1);<br>
    <br>
    for j = 1:WF_cases_n(i)<br>
        clc<br>
        i<br>
        j<br>
        <br>
        WF_data_var = struct;<br>
        <br>
        %read the file<br>
        filename = [folder WF_cases_l{i} '_' num2str(j) '.h5'];<br>
        fileID = H5F.open(filename,'H5F_ACC_RDONLY','H5P_DEFAULT');<br>
        <br>
        datasetID = H5D.open(fileID,'WF_U');<br>
        WF_data_var.WF_U = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br>
        H5D.close(datasetID);<br>
        <br>
        datasetID = H5D.open(fileID,'fs');<br>
        WF_data_case.fs{j} = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br>
        H5D.close(datasetID);<br>
        <br>
        datasetID = H5D.open(fileID,'x');<br>
        WF_data_case.x{j} = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br>
        H5D.close(datasetID);<br>
        <br>
        datasetID = H5D.open(fileID,'y');<br>
        WF_data_case.y{j} = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br>
        H5D.close(datasetID);<br>
        <br>
        H5F.close(fileID);<br>
        <br>
        WF_data_var.WF_P = WF_data_var.WF_U.^3;

        % Time averaged power<br>
        WF_data_case.WF_Pm{j} = mean(WF_data_var.WF_P,3);<br>
        <br>
        % normalize by power in first row: Pi/P1<br>
        WF_data_case.WF_Pm{j} = WF_data_case.WF_Pm{j}./mean(WF_data_case.WF_Pm{j}(1,:));<br>
        <br>
        % Time averaged velocity<br>
        WF_data_case.WF_Um{j} = mean(WF_data_var.WF_U,3);<br>
        <br>
        % u_rms --&gt; TI<br>
        WF_data_case.WF_U_rms{j} = std(WF_data_var.WF_U,[],3);<br>
    end<br>
    <br>
    WF_data.x{i}                = WF_data_case.x;<br>
    WF_data.y{i}                = WF_data_case.y;<br>
    WF_data.fs{i}               = WF_data_case.fs;<br>
    WF_data.WF_Pm{i}            = WF_data_case.WF_Pm;<br>
    WF_data.WF_Um{i}            = WF_data_case.WF_Um;<br>
    WF_data.WF_U_rms{i}         = WF_data_case.WF_U_rms;<br>
    <br>
    %determine spanwise shift for plot legends<br>
    tmp1 = WF_data.y{i}{j-1};<br>
    tmp2 = WF_data.y{i}{j};<br>
    dy = diff( [tmp1(:,1)  tmp2(:,1)] ,1,2);<br>
    dy = max(dy(abs(dy)&gt;0));<br>
    WF_data.shift{i}   = 0:dy:(WF_cases_n(i)-1)*dy;<br>
    <br>
end

%%<br>
line_tick = {'o-','*-','+-','d-','s-','^-','v-','&lt;-','&gt;-','p-','h-'};<br>
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));<br>
for i = 1:length(legend_items)<br>
    legend_items{i} = strrep(WF_cases_l{i},'_','-');<br>
end

%% average power entire farm<br>
row_start = 1;<br>
row_end = 19;<br>
f1 = figure;<br>
set(gcf,'paperposition',[0,0,8.4,4.9])<br>
hold on

for i = 1 : length(WF_cases_selected)<br>
    tmp_P = zeros(size(WF_data.shift{i}));<br>
    for j =  1:WF_cases_n(i)<br>
        tmp_P(j) = mean(mean( WF_data.WF_Pm{i}{j}(row_start:row_end,:)));<br>
    end<br>
    plot( WF_data.shift{i} , tmp_P, line_tick{i} ,'Color', line_color(i,:) ,'MarkerFaceColor', line_color(i,:) )<br>
end

% manualy plot errorbars<br>
for i = 1:length(WF_cases_selected)<br>
    tmp_P = zeros(size(WF_data.shift{i}));<br>
    for j =  1:WF_cases_n(i)<br>
        tmp_P(j) = mean(mean( WF_data.WF_Pm{i}{j}(row_start:row_end,:)));<br>
    end<br>
    px = WF_data.shift{i} ;<br>
    py = tmp_P;<br>
    pw = 0.05;<br>
    pe = zeros(size(px))+0.01;%for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids. <br>
    for j =  1:WF_cases_n(i)<br>
        plot(  [px(j)-pw/2  px(j)+pw/2] , [py(j)+pe(j)  py(j)+pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br>
        plot(  [px(j)-pw/2  px(j)+pw/2] , [py(j)-pe(j)  py(j)-pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br>
        plot(  [px(j)  px(j)],[py(j)-pe(j)  py(j)+pe(j)],':', 'Color', line_color(i,:),'LineWidth',0.5)<br>
    end<br>
end<br>
xlabel('\Delta_y [D]')<br>
ylabel('$\langle P_i  /P_1\rangle_{1}^{19}$','Interpreter','Latex')<br>
box('on')<br>
ylim([0.35 0.66])<br>
xlim([-0.1 2.6])<br>
legend1 = legend(legend_items');<br>
set(legend1,'Location','southeast');<br>
print(f1, 'WF_Pm_all','-dpng','-r300')

%%  average power end of farm<br>
row_start = 16;<br>
row_end = 19;<br>
f2 = figure;<br>
set(gcf,'paperposition',[0,0,8.4,4.9])<br>
hold on

for i = 1 : length(WF_cases_selected)<br>
    tmp_P = zeros(size(WF_data.shift{i}));<br>
    for j =  1:WF_cases_n(i)<br>
        tmp_P(j) = mean(mean( WF_data.WF_Pm{i}{j}(row_start:row_end,:)));<br>
    end<br>
    plot( WF_data.shift{i} , tmp_P, line_tick{i} ,'Color', line_color(i,:) ,'MarkerFaceColor', line_color(i,:) )<br>
end

% manualy plot errorbars<br>
for i = 1:length(WF_cases_selected)<br>
    tmp_P = zeros(size(WF_data.shift{i}));<br>
    for j =  1:WF_cases_n(i)<br>
        tmp_P(j) = mean(mean( WF_data.WF_Pm{i}{j}(row_start:row_end,:)));<br>
    end<br>
    px = WF_data.shift{i} ;<br>
    py = tmp_P;<br>
    pw = 0.05;<br>
    pe = zeros(size(px))+0.02; %for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids. <br>
    for j =  1:WF_cases_n(i)<br>
        plot(  [px(j)-pw/2  px(j)+pw/2] , [py(j)+pe(j)  py(j)+pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br>
        plot(  [px(j)-pw/2  px(j)+pw/2] , [py(j)-pe(j)  py(j)-pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br>
        plot(  [px(j)  px(j)],[py(j)-pe(j)  py(j)+pe(j)],':', 'Color', line_color(i,:),'LineWidth',0.5)<br>
    end<br>
end<br>
xlabel('\Delta_y [D]')<br>
ylabel('$\langle P_i  /P_1\rangle_{16}^{19}$','Interpreter','Latex')<br>
box('on')<br>
ylim([0.27 0.52])<br>
xlim([-0.1 2.6])<br>
legend1 = legend(legend_items');<br>
set(legend1,'Location','southeast');<br>
print(f2, 'WF_Pm_end', '-dpng','-r300')

%% plot average unsteady loading total farm<br>
row_start = 1;<br>
row_end = 19;<br>
f3 = figure;<br>
set(gcf,'paperposition',[0,0,8.4,4.9])<br>
hold on<br>
for i = 1 : length(WF_cases_selected)<br>
    tmp_TI = zeros(size(WF_data.shift{i}));<br>
    for j =  1:WF_cases_n(i)<br>
        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;<br>
    end<br>
    plot( WF_data.shift{i} , tmp_TI , line_tick{i} ,'Color', line_color(i,:)  ,'MarkerFaceColor', line_color(i,:))<br>
end

% manualy plot errorbars<br>
for i = 1:length(WF_cases_selected)<br>
    tmp_TI = zeros(size(WF_data.shift{i}));<br>
    for j =  1:WF_cases_n(i)<br>
        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;<br>
    end<br>
    px = WF_data.shift{i} ;<br>
    py = tmp_TI;<br>
    pw = 0.05;<br>
    pe = zeros(size(px))+ 0.004*tmp_TI;%for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids. <br>
    for j =  1:WF_cases_n(i)<br>
        plot(  [px(j)-pw/2  px(j)+pw/2] , [py(j)+pe(j)  py(j)+pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br>
        plot(  [px(j)-pw/2  px(j)+pw/2] , [py(j)-pe(j)  py(j)-pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br>
        plot(  [px(j)  px(j)],[py(j)-pe(j)  py(j)+pe(j)],':', 'Color', line_color(i,:),'LineWidth',0.5)<br>
    end<br>
end<br>
xlabel('\Delta_y [D]')<br>
ylabel('$ \langle TI \rangle_{1}^{19} [\%]$','Interpreter','Latex')<br>
box('on')<br>
xlim([-0.1 2.6])<br>
legend1 = legend(legend_items');<br>
set(legend1,'Location','northeast');<br>
print(f3, 'WF_TI_all','-dpng','-r300')

%% plot average unsteady loading end of farm<br>
row_start = 16;<br>
row_end = 19;<br>
f4 = figure;<br>
set(gcf,'paperposition',[0,0,8.4,4.9])<br>
hold on<br>
for i = 1 : length(WF_cases_selected)<br>
    tmp_TI = zeros(size(WF_data.shift{i}));<br>
    for j =  1:WF_cases_n(i)<br>
        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;<br>
    end<br>
    plot( WF_data.shift{i} , tmp_TI , line_tick{i} ,'Color', line_color(i,:)  ,'MarkerFaceColor', line_color(i,:))<br>
end

% manualy plot errorbars<br>
for i = 1:length(WF_cases_selected)<br>
    tmp_TI = zeros(size(WF_data.shift{i}));<br>
    for j =  1:WF_cases_n(i)<br>
        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;<br>
    end<br>
    px = WF_data.shift{i} ;<br>
    py = tmp_TI;<br>
    pw = 0.05;<br>
    pe = zeros(size(px))+ 0.01*tmp_TI;%for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids. <br>
    for j =  1:WF_cases_n(i)<br>
        plot(  [px(j)-pw/2  px(j)+pw/2] , [py(j)+pe(j)  py(j)+pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br>
        plot(  [px(j)-pw/2  px(j)+pw/2] , [py(j)-pe(j)  py(j)-pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br>
        plot(  [px(j)  px(j)],[py(j)-pe(j)  py(j)+pe(j)],':', 'Color', line_color(i,:),'LineWidth',0.5)<br>
    end<br>
end<br>
xlabel('\Delta_y [D]')<br>
ylabel('$ \langle TI \rangle_{16}^{19} [\%]$','Interpreter','Latex')<br>
box('on')<br>
xlim([-0.1 2.6])<br>
legend1 = legend(legend_items');<br>
set(legend1,'Location','northeast');<br>
print(f4, 'WF_TI_end','-dpng','-r300')
description: {"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"]}