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42 results for “wind tunnel”
A blind test on wind turbine wake modelling based on wind tunnel experiments: Phase I – The benchmark case
<p>This data set ("Data files.zip") contains the wind tunnel measurement data from Phase I of the Blind test on wind turbine wake modelling based on wind tunnel experiments organised during the TWEET-IE project (www.tweet-ie.eu).</p> <p>This updated version <strong>replaces</strong> the older versions 1.0.0 (https://doi.org/10.5281/zenodo.10566401), 1.1.0 (https://doi.org/10.5281/zenodo.11370112), 2.0 (https://doi.org/10.5281/zenodo.12188194) and 2.1 (https://doi.org/ 10.5281/zenodo.13918935). In comparison to the previous version 2.1 the data documentation has been updated to follow the template of the TWEET-IE project documents, indicating the Grant Agreement Number with the European Union and the Call Topic of the project.</p> <p>All tests were conducted in the closed-loop, low-speed boundary layer wind tunnel of the Chair of Aerodynamics and Fluid Mechanics at Technische Universität München (TUM). The experiments concerned two wind turbines, aligned with the flow, one downstream of the other, at a distance of 5 diameters. For Phase I, no control was applied to the wind turbine models, which were operating at constant RPM. The turbine models, designed and manufactured by TUM, were instrumented with multiple sensors and actuators and had a diameter of 1.1M. Measurements include velocity, power and loads on the turbines. A detailed description of the experimental set up can be found in the accompanying document ("Data documentation.pdf"). </p> <p>File "Submission procedure.zip" includes the format description and the templates of the output data that should be submitted by the participants in the blind test comparison.</p>
Wind tunnel distributed temperature sensing with actively heated fibers and microstructures for detecting wind direction
<p>Wind tunnel tests were performed using distributed temperature sensing with actively heated fibers that had microstructures attached in opposing directions on neighboring fibers. These microstructures created a temperature difference between fibers that depended on wind speed, providing a prototype for distributed sensing of wind direction. These data are connected to a publication detailing this work and method, <a href="https://www.atmos-meas-tech-discuss.net/amt-2019-188/">"Distributed observations of wind direction using microstructures attached to actively heated fiber-optic cables"</a>.</p> <p>Data are stored in a netcdf format and includes the instrument reported temperature ('instr_temp') and calibrated temperature ('cal_temp') with the various parameters tested in the linked paper available as coordinates, labeled along an 'expname' dimension.</p> <p>The included ipython notebooks provide examples and explanations for using these laboratory data.</p>
HyPer SMM wind tunnel tests: PIV pictures
<p>In this study, windblown sand transport on flat ground is reproduced by means of Wind-Sand Tunnel Tests (WSTT) carried out in the wind tunnel L-1B of von Karman Institute for Fluid Dynamics. The aim of WSTT is twofold. On one hand, they are intended to characterize the incoming sand flux in open field conditions. On the other hand, they allow to properly tune cheaper Wind-Sand Computational Simulations. The wind tunnel setup implements a uniform 5-meter-long sand fetch as sand source. The wind speed boundary layer is characterized through 2D Particle Image Velocimetry (PIV) technique. Wind flow state variables are assessed along the sand fetch by setting the wind speed equal to 1.3, 1.5, 2 times the threshold one. For the complete wind tunnel setup and data analysis please refer to: Raffaele L., Coste, N., and Glabeke G. "Life-Cycle Performance and Cost Analysis of Sand Mitigation Measures: Toward a Hybrid Experimental-Computational Approach." Journal of Structural Engineering 148.7 (2022): 04022082.</p> <p>The study has been developed in the framework of the MSCA-IF-2019 research project Hybrid Performance Assessment of Sand Mitigation Measures (HyPer SMM, https://hypersmm.vki.ac.be/). This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant Agreement No. 885985</p>
HyPer SMM wind tunnel tests: PTV pictures
<p>In this study, windblown sand transport on flat ground is reproduced by means of Wind-Sand Tunnel Tests (WSTT) carried out in the wind tunnel L-1B of von Karman Institute for Fluid Dynamics. The aim of WSTT is twofold. On one hand, they are intended to characterize the incoming sand flux in open field conditions. On the other hand, they allow to properly tune cheaper Wind-Sand Computational Simulations. The wind tunnel setup implements a uniform 5-meter-long sand fetch as sand source. The sand flux saltation layer are characterized through Particle Tracking Velocimetry (PTV) technique. Sand transport is assessed along the sand fetch by setting the wind speed equal to 1.3, 1.5, 2 times the threshold one. For the complete wind tunnel setup and data analysis please refer to: Raffaele L., Coste, N., and Glabeke G. "Life-Cycle Performance and Cost Analysis of Sand Mitigation Measures: Toward a Hybrid Experimental-Computational Approach." Journal of Structural Engineering 148.7 (2022): 04022082.</p> <p>The study has been developed in the framework of the MSCA-IF-2019 research project Hybrid Performance Assessment of Sand Mitigation Measures (HyPer SMM, https://hypersmm.vki.ac.be/). This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant Agreement No. 885985</p>
Evaluation of a wind tunnel designed to investigate the response of evaporation to changes in the incoming longwave radiation at a water surface
<p>Experimental Record of a Longwave-Evaporation experiment. The record to be referenced in a forthcoming scientific paper.</p>
Wind tunnel test data for the evaluation of the aerodynamic coefficients of an antenna mast with ancillaries.
<p>This dataset comprises measured data and results from static wind tunnel tests conducted in April 2024 at the Giovanni Solari Wind Tunnel Facility (GS-WinDyn). The tests aim to assess the drag, lift, and moment coefficients <span>of an antenna mast designed as a triangular lattice tower, equipped with both linear and discrete ancillary components.</span> The wind tunnel experiments are carried out under both smooth and turbulent flow conditions using a scaled 3D model of the antenna mast. Five ancillary configurations, based on predominant patterns observed, are tested. Drag forces, lift forces and moments are measured using two six-component force balances attached to the ends of the model, while downstream three-component velocity data is captured by a Cobra probe. For each configuration, aerodynamic coefficients are determined for angles of attack ranging from 0° to 360°, with increments of up to 10°. The dataset provides the measured data and the obtained aerodynamic coefficients and it has significant reuse potential in several applications: comparison with experimental wind tunnel data, validation of analytical and numerical CFD models with similar configurations, estimation of wind loads due to ancillary structures, and characterization of wake effects.</p>
Icing Wind Tunnel Measurements of Supercooled Large Droplets Using the 12 mm Total Water Content Cone of the Nevzorov Probe: Measurement Data
<p>This repository contains the measurement data that was used for the publication "Icing Wind Tunnel Measurements of Supercooled Large Droplets Using the 12 mm Total Water Content Cone of the Nevzorov Probe".</p>
Wind tunnel experiment of a micro wind farm model
<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. </p> <p>For detailed information about the experimental setup and wind farm layouts see: </p> <p>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 .</p> <p>For more information about the experimental design of the porous disk models, see also:</p> <p>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</p> <p> 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</p> <p> </p> <p>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.</p> <p>--------------------------------------------------------<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');</p> <p>datasetID = H5D.open(fileID,'WF_U');<br> WF_U = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);</p> <p>datasetID = H5D.open(fileID,'fs');<br> fs = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);</p> <p>datasetID = H5D.open(fileID,'x');<br> x = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);</p> <p>datasetID = H5D.open(fileID,'y');<br> y = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);</p> <p>H5F.close(fileID);</p> <p>--------------------------------------------------------<br> Example code to load data in Python:<br> --------------------------------------------------------<br> import h5py<br> filename = 'U_C1_1.h5'<br> f = h5py.File(filename, 'r')</p> <p>U = f['WF_U'][()]<br> x = f['x'][()]<br> y = f['y'][()]<br> fs = f['fs'][0][0]<br> f.close()</p> <p>--------------------------------------------------------<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;</p> <p>folder = '/';% folder with files</p> <p>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</p> <p>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);</p> <p><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;</p> <p> % 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 --> 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)>0));<br> WF_data.shift{i} = 0:dy:(WF_cases_n(i)-1)*dy;<br> <br> end</p> <p>%%<br> line_tick = {'o-','*-','+-','d-','s-','^-','v-','<-','>-','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;</p> <p>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</p> <p>%% 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</p> <p>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</p> <p>% 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')</p> <p>%% 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</p> <p>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</p> <p>% 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')</p> <p>%% 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</p> <p>% 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} [\%]$','Interpreter','Latex')<br> box('on')<br> xlim([-0.1 2.6])<br> legend1 = legend(legend_items');<br> set(legend1,'Location','northeast');<br> print(f3, 'WF_TI_all','-dpng','-r300')</p> <p>%% plot average unsteady loading end of farm<br> row_start = 16;<br> row_end = 19;<br> f4 = figure;<br> set(gcf,'paperposition',[0,0,8.4,4.9])<br> hold on<br> for i = 1 : length(WF_cases_selected)<br> tmp_TI = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_TI(j) = mean(mean(WF_data.WF_U_rms{i}{j}(row_start:row_end,:)./WF_data.WF_Um{i}{j}(row_start:row_end,:)))*100;<br> end<br> plot( WF_data.shift{i} , tmp_TI , line_tick{i} ,'Color', line_color(i,:) ,'MarkerFaceColor', line_color(i,:))<br> end</p> <p>% manualy plot errorbars<br> for i = 1:length(WF_cases_selected)<br> tmp_TI = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_TI(j) = mean(mean(WF_data.WF_U_rms{i}{j}(row_start:row_end,:)./WF_data.WF_Um{i}{j}(row_start:row_end,:)))*100;<br> end<br> px = WF_data.shift{i} ;<br> py = tmp_TI;<br> pw = 0.05;<br> pe = zeros(size(px))+ 0.01*tmp_TI;%for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids. <br> for j = 1:WF_cases_n(i)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)+pe(j) py(j)+pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)-pe(j) py(j)-pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j) px(j)],[py(j)-pe(j) py(j)+pe(j)],':', 'Color', line_color(i,:),'LineWidth',0.5)<br> end<br> end<br> xlabel('\Delta_y [D]')<br> ylabel('$ \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')</p> <p> </p>
Twin Test 2: Wake interactions of a cluster of turbines and wake steering techniques. Wind tunnel data.
<p>The aerodynamic performance of two identical wind turbine models was characterized under various static and dynamic conditions in a synchronous configuration within the wind tunnel test section. Two experimental campaigns were performed at Technische Universität München (TUM) and at the National Technical University of Athens (NTUA) to investigate wake flow control techniques. This document contains the necessary information to understand the performed experiments and to access and use the available data. While both experimental set ups are detailed, only data from the TUM campaign are available at the time of writing, as the NTUA campaign results will form Phase II of an ongoing blind test campaign and cannot be published.</p>
"Wind turbine wakes on escarpments: A wind-tunnel study"
<p>Dar, Arslan Salim, and Fernando Porté-Agel. "Wind turbine wakes on escarpments: A wind-tunnel study." <em>Renewable Energy</em> 181 (2022): 1258-1275.</p>
Text-fig. 13. Zoophycos isp. a: BK 17, Layer No. 6; b: BK 27, Layer No. 8; c: lateral tunnel continuing from spreite side to the surrounding rock, BK 28, Layer No. 23; d: BK 22, Layer No. 1; e: "juvenile" stage of the structure on a horizontal winding tunnel, BK 21, Layer No. 26; f: BK 24, Layer No. 17; g: broad winding tunnel adjacent to spreite, BK 26, Layer No. 6; h: BK 15, Layer No. 18; i: BK 23, Layer No. 2. Scale bar = 1 cm. in Early Complex Tiering Pattern: Upper Ordovician, Barrandian Area, The Czech Republic
Text-fig. 13. Zoophycos isp. a: BK 17, Layer No. 6; b: BK 27, Layer No. 8; c: lateral tunnel continuing from spreite side to the surrounding rock, BK 28, Layer No. 23; d: BK 22, Layer No. 1; e: "juvenile" stage of the structure on a horizontal winding tunnel, BK 21, Layer No. 26; f: BK 24, Layer No. 17; g: broad winding tunnel adjacent to spreite, BK 26, Layer No. 6; h: BK 15, Layer No. 18; i: BK 23, Layer No. 2. Scale bar = 1 cm.
Wind Tunnel Testing of Tethered Inflatable Wings
<p>This dataset consists of all the data collected and presented in the AIAA Journal of Aircraft titled "Wind Tunnel Testing of Tethered Inflatable Wings". The attached zipped folder contains a README file that explains the dataset. </p>
Swansea University Wind Tunnel Gust Generator
<p>Initial experimental test of the gust generator at the Swansea University wind tunnel. As a preliminary study smoke test has been used to prove the concept. Experimental data collected from a cross-hot wire sensor indicate that the system is reliably capable of creating single and continuous gusts.</p> <p>More information:</p> <p>[1] D. Balatti, H. Haddad Khodaparast, M. I. Friswell, & M. Manolesos. Improving wind tunnel ‘1-cos’ gust profiles. Journal of Aircraft, https://doi.org/10.2514/1.C036772.</p> <p>[2] D. Balatti, H. Haddad Khodaparast, M. I. Friswell, & M. Manolesos. Improving wind tunnel ‘1-cos’ gust profiles. AIAA 2022-2485. <em>AIAA SCITECH 2022 Forum</em>. January 2022.</p>
Data example and code used in the publication "Is transport of microplastics different from that of mineral dust? Results from idealized wind tunnel studies"
<p>Background</p> <p>The code labels microspheres and counts them. Further, the code determines which microspheres are independent of microsphere-microsphere collisions by their relative position to the other microspheres in an image. Images were taken with a full-frame visual camera (Sony Alpha 7RII) with a long-distance-microscopy lens (K2 DistaMax).</p> <p>Description of the dataset</p> <ul> <li>image_data_all.zip contains 228 tif-format images taken in a single experiment <ul> <li>the images show borosilicate microspheres with diameters from 63 to 75 µm</li> <li>during the experiment, the microspheres are detached from the substrate and are transported out of the image</li> </ul> </li> <li>functions_particle_labeling.jl contains all necessary functions for particle labeling</li> <li>analysis_protocol.jl is an example, that first determines a color threshold, and then labels all microspheres in all images stored in "image_data_all/substrate_a/image_data_single_experiment"</li> <li>post_processing_visualisation.R is an r-script, that reads the output of analysis_protocol.jl and demonstrates how logistic functions were fitted to the data</li> </ul> <p> </p> <p>We used julia 1.8.5 and R 4.3.0.</p> <p> </p>
FarmConners Wind Farm Flow Control Benchmark: Blind Test with CL-WINDCON Wind Tunnel Data
<p>This is the dataset used for running the fourth Blind Test of the FarmConners Wind Farm Flow Control Benchmark. The Blind Test was performed with an extensive dataset gathered while testing a cluster of three scaled wind turbines within a large boundary layer wind tunnel. The experimental dataset has been compared against the predictions provided by 5 different control-oriented wind farm flow models. The resulting comparison is described in the paper "FarmConners Wind Farm Flow Control Benchmark: Blind Test Results, Part 2", by Campagnolo et al, (2023). The dataset consists of:</p> <ol> <li>measurements of the flow within the wake shed by one or two machines, as well as measurements of the power, loads (on the rotating shaft and at tower base), and pitch/yaw/torque actuators states of the three scaled machines. The measurements have been performed under a wide range of inflow and machines operating conditions. The time series of the measured data are provided in the format of Matlab structures saved in .mat files.</li> <li>Predictions provided by the models used by the Blind Test participants</li> <li>Matlab scripts used for comparing the experimental dataset and the numerical predictions provided by the Blind Test participants</li> <li>Additional data provided to the Blind Test participants. This includes a FAST model of the scaled wind turbine and the mapping of the inflow of the empty wind tunnel. </li> </ol>
Periodic dynamic induction control of wind farms: proving the potential in simulations and wind tunnel experiments - data sets
<p>Data sets of the wind tunnel experiments described in "Periodic dynamic induction control of wind farms: proving the potential in simulations and wind tunnel experiments". DOI: https://doi.org/10.5194/wes-2019-50</p>
Wind tunnel measurements of concentration and velocity in urban geometries with trees
<p><span>This dataset contains concentration and velocity mesurements performed in the aerodynamic wind tunnel of the Ecole Centrale de Lyon (France). <br>An idealized urban district was simulated by an array of blocks, and two rows of model trees were arranged inside a street. <br>Reduced scale trees were chosen to mimic a realistic shape and aerodynamic behaviour.<br>Three different spacings between the trees were considered: "zero" (no trees), "half" (14 cm distance between the tree trunks) and "full" (7 cm distance between the tree trunks).</span></p> <p><span>The dataset includes: <strong> </strong></span></p> <ul> <li><span>concentration and velocity measurements performed within a street canyon, under various geometries and wind directions; </span></li> <li><span>the characterization of the boundary layer above the buildings;</span></li> <li><span>the characterization of the tree drag.</span></li> </ul> <p><span>The detailed description of the dataset is contained in the document <code>Info_dataset.pdf</code></span></p> <p> </p> <p> </p>
High resolution wind speed measurements with multicopters of the SWUF-3D UAS fleet - calibration and verification in a wind tunnel with active grid
<p>This dataset contains aggregated measurements from multicopter UAS. The data were measured during the period from October 5, 2022 to October 12, 2022 in the ForWind wind tunnel at the University of Oldenburg with UAS of the SWUF-3D fleet against Constant Temperature Anemometer (CTA). </p><p>Recorded data are provided for measurement flights in different generated wind profiles, i.e. staircase profiles, gusts, velocity steps and statistical turbulence. The measurement data consist of the accelerations measured by the UAS in its longitudinal and lateral axes, as well as the wind speeds measured by the CTA. The latter data were sampled down to the sampling rate of the UAS wind measurement. For the measurements in statistical turbulence, additional files are provided which contain the wind speeds measured by the CTA in its original sampling rate. Each file contains the data for a single measurement flight, as well as information in the header about the ambient conditions in the wind tunnel. The file names contain the following metadata:</p><p>for "gust" files:</p><ul><li>V0 : inertial velocity [m/s]</li><li>V_g : gust velocity amplitude [m/s]</li></ul><p>for "staircase" files:</p><ul><li>uas : the ID of the UAS used [-]</li><li>heading : yaw angle of UAS in relation to longitudinal axes of wind tunnel</li></ul><p>for "turbulence" files:</p><ul><li>V0 : fan wind speed [m/s]</li><li>I : turbulence intensity [%]</li><li>f_cta : sampling frequency of reference sensor [Hz] (for files with original sampling rate)</li></ul><p>for "velocity step" files:</p><ul><li>V0 : lower wind speed</li><li>V_du : wind speed aimed for of upward and downward velocity step</li></ul><p>All filenames end with the test date in YYYY-MM-DD format.</p>
Supplemental Material to Doctoral Thesis "Engineering approach for predicting tunneling crack initiation in trailing-edge adhesive joints of wind turbine blades under mechanical fatigue and thermal residual stresses"
<p>This set supplements the figure data to the doctoral thesis "Engineering approach for predicting tunneling crack initiation in trailing-edge adhesive joints of wind turbine blades under mechanical fatigue and thermal residual stresses", DOI: <a href="https://doi.org/10.14279/depositonce-19144">10.14279/depositonce-19144</a></p>
Large-eddy simulation of yawed wind-turbine wakes: comparisons with wind tunnel measurements and analytical wake models
<p>Dataset of the paper "Large-eddy simulation of yawed wind-turbine wakes: comparisons with wind tunnel measurements and analytical wake models" published on Energies [1].</p> <p>[1] Lin, M., & Porté-Agel, F. (2019). Large-eddy simulation of yawed wind-turbine wakes: comparisons with wind tunnel measurements and analytical wake models. <em>Energies</em>, <em>12</em>(23), 4574.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.