Find research datasets worth reusing

opencc-by-4.0Nov 2020View details →

Multi-fidelity Generative Deep Learning Turbulent Flows

<p>Data sets for the two numerical examples in the paper <a href="https://arxiv.org/abs/2006.04731">Multi-fidelity Generative Deep Learning Turbulent Flows</a> as well as two pre-trained models.  In this work, a novel multi-fidelity deep generative model is introduced for the surrogate modeling of high-fidelity turbulent flow fields given the solution of a computationally inexpensive but inaccurate low-fidelity solver. The resulting surrogate is able to generate physically accurate turbulent realizations at a computational cost magnitudes lower than that of a high-fidelity simulation. The deep generative model developed is a conditional invertible neural network, built with normalizing flows, with recurrent LSTM connections that allow for stable training of transient systems with high predictive accuracy. Data is provided from OpenFOAM LES simulations for turbulent flow over backwards step and flow around an array of cylinders.</p> <p>Data-set Files:</p> <ul> <li><a href="https://zenodo.org/api/files/cf158661-2a30-4c8f-aa97-cd5c1600910c/backward_step_testing.tar.gz?versionId=320a523f-0015-4ba3-8c6e-66733ab5a1af">backward_step_testing.tar.gz</a> - Backward step testing data.</li> <li><a href="https://zenodo.org/api/files/cf158661-2a30-4c8f-aa97-cd5c1600910c/backward_step_training.tar.gz?versionId=ac34ac15-973d-4fb8-8881-faa17eced69f">backward_step_training.tar.gz</a> - Backward step training data.</li> <li><a href="https://zenodo.org/api/files/cf158661-2a30-4c8f-aa97-cd5c1600910c/cylinder_array_testing.tar.gz?versionId=ebbea725-c8b6-4338-978b-dc73f943552e">cylinder_array_testing.tar.gz</a> - Cylinder array testing data.</li> <li><a href="https://zenodo.org/api/files/cf158661-2a30-4c8f-aa97-cd5c1600910c/cylinder_array_training.tar.gz?versionId=b0409c34-fb19-45c4-bbba-6968fdcfc4d8">cylinder_array_training.tar.gz</a> - Cylinder array training data.</li> </ul> <p>Pre-trained Models:</p> <ul> <li><a href="https://zenodo.org/api/files/cf158661-2a30-4c8f-aa97-cd5c1600910c/bstepWorkspace400.zip">bstepWorkspace400.zip</a> - Backward step pre-trained model.</li> <li><a href="https://zenodo.org/api/files/cf158661-2a30-4c8f-aa97-cd5c1600910c/cylinderWorkspace400.zip">cylinderWorkspace400.zip</a> - Cylinder array pre-trained model.</li> </ul> <p> </p>

opencc-by-4.0Jun 2021View details →

Lagrangian statistics in turbulent channel flow

<p>A set of Lagrangian statistics of passive tracers in a turbulent channel flow. The particle trajectories are obtained by means of integration of a simulated flow field, computed via Direct Numerical Simulation, at four different Reynolds numbers. The Reynolds numbers here employed are <span class="math-tex">\(\mathrm{Re}_{\tau} = \frac{u_{\tau}\delta}{\nu} = 180,\,395,\,590,\,950\)</span>, where <span class="math-tex">\(u_{\tau}\)</span>is the frictional velocity, <span class="math-tex">\(\delta\)</span> is the channel half height and <span class="math-tex">\(\nu\)</span> is the kinematic viscosity.</p> <p>Additional information about the database is provided in the included documentation.</p> <p>v1.0 -> Added statistics at ReT = 950</p> <p>v1.1 -> Added statistics at ReT = [180, 395, 590]</p> <p>v1.2 -> Added statistics at ReT = 265</p>

opencc-by-4.0Jun 2024View details →

Dataset for Spectral scaling of unstably-stratified atmospheric flows: turbulence anisotropy and the low frequency spread

<p>30 min turbulence statistics and spectra of 13 datasets from flat to highy complex terrain. Data only cover unstable stratification. </p> <p>Dataset is a companion to the manuscript  Charrondiere, C., Stiperski, I., 2024: Spectral scaling of unstably-stratified atmospheric flows: turbulence anisotropy and the low frequency spread. Quarterly Journal of the Royal Meteorological Society,   https://doi.org/10.1002/qj.4811<strong><br></strong></p> <p> </p>

opencc-by-4.0Jun 2023View details →

Data and scripts for reproducing "Quantifying Uncertainties in Direct Numerical Simulations of a Turbulent Channel Flow"

<p>This is the accompanying data and Python scripts to reproduce the figures in "Quantifying Uncertainties in Direct Numerical Simulations of a Turbulent Channel Flow", currently under review.</p>

opencc-by-4.0May 2020View details →

ADCP data of ice-covered and open-channel (macro-turbulent) flow, Pulmanki River, 2016-2020

<p>README of ADCP_data_Lotsari_et_al_Water_opened.zip</p> <p><br> The ADCP data was the basis of the following paper:<br> Macro-turbulent flow and its impacts on sediment transport potential of a subarctic river during ice-covered and open-channel conditions <br> Eliisa Lotsari (1, 2), Michael Dietze (3), Maria Kämäri (4), Petteri Alho (2,5), Elina Kasvi (6,2)</p> <p>1 Department of Geographical and Historical Studies, University of Eastern Finland, Yliopistokatu 2, P.O. Box 111, FI-80101, Joensuu, Finland. eliisa.lotsari@uef.fi<br> 2 Department of Geography and Geology, University of Turku, FI-20014 Turun yliopisto, Turku, Finland.<br> 3 Section 4.6 Geomorphology, German Research Centre for Geosciences GFZ Potsdam, D-14473 Potsdam, Germany. mdietze@gfz-potsdam.de<br> 4 Finnish Environment Institute, Latokartanonkaari 11, FI-00790 Helsinki, Finland. maria.kamari@ymparisto.fi<br> 5 Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 2, FI-02430, Masala, Finland. mipeal@utu.fi<br> 6 Turku University of Applied Sciences, Joukahaisenkatu 3, FI-20520, Turku, Finland. elina.kasvi@turkuamk.fi</p> <p>(Note: During the time of data gathering, Maria Kämäri worked at the University of Eastern Finland, and Elina Kasvi at the University of Turku)</p> <p>The data set has been measured with Sontek M9 or S5 sensors, depending on the time step (Table 1).</p> <p>Table 1. The measurement times, their acronyms (applied in the above mentioned publication)<br> and applied sensors. In the acronyms of the measurement times W=winter low flow period, S=spring<br> (snow-melt flood period), A=autumn low flow period. These information are presented also<br> in the Table 1 of the above-mentioned publication.<br> Date          Acronym  Sensor<br> 17.2.2016    W2016    M9<br> 25.5.2016    S2016    S5     <br> 10.9.2016    A2016    M9<br> 16.2.2017    W2017    M9     <br> 31.5.2017    S2017    M9    <br> 9.9.2017       A2017    M9    <br> 9.2.2018        W2018    M9    <br> 23.5.2018        S2018    S5     <br> 8.9.2018        A2018    S5    <br> 8.2.2019        W2019    M9    <br> 21.5.2019        S2019    M9    <br> 6.2.2020        W2020    M9     </p> <p>RiverSurveyor Live software, and its most recent version, was used each time.<br> The measurements have been done at Pulmanki River (69°55'59.09" N;  28° 2'34.32" E), Northern Finland, during 2016 - 2020. <br> The data is in directories of corresponding measurement times. The measurement<br> locations cs1, cs2, cs3, cs4, csA, csB and csC can be found in the paper (Figs. 1 and 2). <br> The data is in raw Matlab file format, as exported from the RiverSurveyor Live software.<br> The coordinate system is ENU.</p> <p>When used, the referencing to the paper and DOI ( 10.5281/zenodo.3855035 ) are required.</p>

opencc-by-4.0Jan 2021View details →

The turbulent flow over the BARC rectangular cylinder: a DNS study

<p>This set of results stems from a database computed with a Direct Numerical simulation of the incompressible flow around a rectangular cylinder with chord-to-thickness ratio 5:1 (also known as BARC benchmark). The Reynolds number based on the cylinder thickness and the incoming velocity is set to Re=3000. We provide the two-dimensional mean field as well as the complete set of Reynolds stresses and the terms involved in their single-point budget equations. Further details can be found on an accompanying paper by Chiarini & Quadrio, Flow Turbul. Combust. 107, 875–899 (2021), available at https://doi.org/10.1007/s10494-021-00254-1.</p> <p>Data are provided in VTK file format, so that they can be visualized with several applications, as for example the open-source package ParaView.</p> <p>The file mean.vtk contains the mean flow in terms of the velocity components U and V and pressure field P in the x − y plane.<br> The files uu-Budget.vtk, vv-Budget.vtk, ww-Budget.vtk, uv-Budget.vtk, uw-Budget.vtk, vw-Budget.vtk and k-Budget.vtk contain the complete set of terms appearing in the budget equations for the components of the Reynolds stress tensor and for the turbulent kinetic energy.</p>

opencc-by-4.0Dec 2021View details →

Maps of depths are created for the site of 50 m length. Flow types are turbulent, broken standing waves, unbroken standing waves, and rippled. The average width was 8 m and varied from 5.5 to 12 m. Bed elements included bars, rocks, and step/pools. The average depth was 0.35 m, with a maximum of 0.6 m. The average velocity was 0.4 m/s, with a maximum of 1.2 m/s (figs 10). Distribution of bottom habitats at the locations with the crayfish are as follows: megalital — 5 %, macrolithal — 30 %, mesolithal — 25 %, microlithal — 15 %, psammal — 15 %, CPOM — 10 %. Assessment by hydrobiological parameters showed that the presence of Lyngbya and Oscillatoria, as well as the increase of the number of Oligochae- in New Findings Of White Clawed Crayfish, Austropotamobius Pallipes (Decapoda, Astacidae), And Peculiarities Of Its Spatial Distribution In Neretvica (Bosnia And Herzegovina)

Maps of depths are created for the site of 50 m length. Flow types are turbulent, broken standing waves, unbroken standing waves, and rippled. The average width was 8 m and varied from 5.5 to 12 m. Bed elements included bars, rocks, and step/pools. The average depth was 0.35 m, with a maximum of 0.6 m. The average velocity was 0.4 m/s, with a maximum of 1.2 m/s (figs 10). Distribution of bottom habitats at the locations with the crayfish are as follows: megalital — 5 %, macrolithal — 30 %, mesolithal — 25 %, microlithal — 15 %, psammal — 15 %, CPOM — 10 %. Assessment by hydrobiological parameters showed that the presence of Lyngbya and Oscillatoria, as well as the increase of the number of Oligochae-

opencc-by-4.0Jul 2024View details →

Database: Influence of large-scale freestream turbulence on bypass transition in air and organic vapour flows

<p>This is an open-access database of numerical simulations of freestream-turbulence induced transitions of zero-pressure-gradient flat plates boundary layers. The data have been collected at Arts et Metiers Institute of Technology, at DynFluid laboratory, in the period 2021-2024 within the REGAL-ORC project- REal GAs effects on Loss mechanisms of ORC turbine flows, a project in collaboration with FH Münster in Germany.</p> <p>Work related to the database is published in Journal of Fluid Mechanics, under the title: "Influence of large-scale freestream turbulence on bypass transition in air and organic vapour flows". The aims of this work was to investigate freestream-turbulence induced transitions, in particular under relatively large integral length scale, of air and organic vapors boundary layers.</p> <p>This version contains evolutions of averaged quantities in the streamwise direction, along with profiles and spectra in the boundary layer for all the different simulations performed. This database is supplemented with python codes to read the data and descriptions of the data in the headers and README files.</p>

opencc-by-sa-4.0Jun 2018View details →

Turbulence statistics in smooth wall oscillatory boundary layer flow

<p>Experimental and numerical datasets belonging to Van der A, D.A., Scandura, P., O’Donoghue, T. (2018). Turbulence statistics in smooth wall oscillatory boundary layer flow, Journal of Fluid Mechanics, 849, 192-230. </p> <p> </p>

opencc-by-4.0Oct 2023View details →

Dataset: Impact of lateral gap on flow distribution, backwater rise, and turbulence generated by a logjam

<p>This dataset includes flow measurements and wood accumulation characteristics of flume experiments conducted at the Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich.</p>

opencc-by-4.0Jun 2024View details →

Understanding Near-Ground Tornado Flows - Pressure, Shear and Turbulence, and their Importance in Structural Loading

<h1>Dataset Description</h1> <p>The research focuses on the velocity and pressure field of the near-ground region within one of the Wind Engineering, Energy, and Environment (WindEEE) Dome's tornadic-like flow simulations. This research also aims to provide valuable insight in the behaviour between tornado-building interactions. Measurement regions of interest range from the apparent centre of the tornado-like flow structure extending beyond twice the radius of maximum tangential velocity.</p> <p>Two vortex cases were simulated and measured:</p> <p>(1) TV0 - a stationary vortex where the bell mouth (updraft centre) aligns with the centre of the test chamber, and</p> <p>(2) TV1 - a vortex translating for a distance of 4.5 m at a nominal translation speed of about 1-1.2 m/s.</p> <div> <h2>S0. Documentation</h2> <p>Contains information documents regarding the instrumentation specifications, test plan, and other important diagrams.</p> <div> <h2>S1. Near-Ground Flow Model</h2> </div> <div> <p>TV0 and TV1 simulations were tested at the WindEEE Dome testing facility. Velocity measurement devices were used to characterize simulated vortex's horizontal flow field at heights of 3.2, and 7.2. Pressure measurement devices were used to characterize the vortex's ground surface pressure. These measurements aim to provide data for more accurate tornado wind field models.</p> <h3>E1. Near-Ground Velocity Field</h3> <p>During this test, 3-D point measurements were taken to aid in characterizing TV0 and TV1 simulations. Measurements were taken at locations R = 0, 60 and 105 cm measured perpendicularly from the bell mouth's path of translation. Two heights and four point positions were captured at each location. These measurements aim to provide detailed characteristics of the wind field.</p> <h3>E2. Ground Surface Instantaneous Pressure Field</h3> <p>A ground surface pressure model covering an area of 120 cm by 300 cm was used to characterize the ground pressure distribution of simulations: TV0 and TV1. Instantaneous pressure measurement devices captured the flow distribution throughout a 2-D grid of pressure taps mounted flush with the ground surface panels.</p> <div> <h2>S2. Low-Rise Building Model</h2> </div> <div> <p>A 3:12 roof slope, 1:100 scale, low-rise building model was instrumented with external surface pressure taps and tested under TV0 and TV1 flow simulations at WindEEE. The low-rise building model was measured in locations R = 0, 60, and 105 cm measured perpendicularly from the bell mouth translation pat, and at azimuth angles 0, 45, and 90 degrees relative to the path of the bell mouth.</p> <h3>E1. Low-Rise Building Aerodynamics</h3> <p>The effects of stationary and translating vortices on a low-rise building model were investigated.</p> <h2>S3. Small-Portable Temporary Building Model</h2> <div> <p>A temporary low-rise building was instrumented with external surface pressure taps and tested under TV0 and TV1 flow simulations at WindEEE. The small-portable building model of size 1.2 cm by 1.2 cm by 3.2 cm (1:70 scale) and ground surface model covering 120 cm by 300 cm. The small-portable building was measured in locations R = 0, 60, and 105 cm measured perpendicularly from the bell mouth translation path.</p> <h3>E1. Small-Portable Temporary Building Aerodynamics</h3> <p>The effects of stationary and translating vortices on a small-portable building model were investigated.</p> <div> <h2>S4. Near-Ground Particle Image Velocimetry (PIV)</h2> </div> <div> <p>A Continuum laser was focused and split through a cylindrical lens into a thin laser sheet, fog machines were used to seed the entire test chamber with long lasting fog fluid, and finally synchronized high-resolution cameras (flares) were used to capture laser light refraction. Flares send image data to the DVR Express Cores during test measurements. This PIV system was used to measure the horizontal velocity field of simulations: TV0 with an approximate total coverage of 80 cm by 240 cm.</p> <h3>E1. Near-Ground Horizontal Velocity Field</h3> <p>The PIV system was used to measure the 2-D velocity field at a height of 7.2 cm from the chamber floor. The plane of measure covers regions from the tornado-like flow's apparent centre to beyond the region of maximum tangential velocity.</p> <h3>E2. Low-Rise Building Wake Aerodynamics</h3> <p>A low-rise building model was measured at the centre of the test chamber, subject to the tornado vortex TV0. The model was made black to absorb energy and light from the PIV laser. The PIV system was used to measure the 2-D velocity field at a height of 7.2 cm from the chamber floor.</p> <p> </p> <p><strong>Note: </strong>Due to storage limitations we cannot include the entire PIV raw dataset, however data can be made available apon request. Please email the project contact person or the hosting instution (<a href="https://windeee.ca/contact/">Contact – WindEEE Research Facility</a>) for information or access to the complete dataset. </p> </div> </div> </div> </div> </div>

opencc-by-4.0Dec 2023View details →

Dataset: Flow and Turbulence due to Wood Contribute to Declogging of Gravel Bed

<div> <p>This dataset includes flow measurements and results from image analysis of channel bed and cores using flume experiments conducted at the Nepf Environmental Fluid Mechanics Laboratory at MIT.</p> <p> </p> </div>

opencc-by-4.0Jan 2022View details →

Microswimmers in turbulent fluid flow of Taylor-scale Reynolds number Re = 21 dataset

<p><strong>The data includes the trajectories and the swimming velocities of individual microswimmers embedded in a turbulent fluid flow of Taylor-scale Reynolds number <span class="math-tex">\(Re_{\lambda} = 21\)</span> .</strong></p> <p>The net swimming velocity of a microswimmer is the sum of the intrinsic swimming velocity and the fluid velocity. The intrinsic swimming velocity is dictated by the swimming parameters, that is, the swimming speed <span class="math-tex">\(v_s\)</span> and the reorientation time <span class="math-tex">\(B\)</span>.</p> <p>Different values of swimming parameters are explored.</p> <table> <tbody> <tr> <td><strong>Item</strong></td> <td><strong><span class="math-tex">\(v_s\)</span></strong></td> <td><strong><span class="math-tex">\(B\)</span></strong></td> </tr> <tr> <td>R1</td> <td>2.20</td> <td>10</td> </tr> <tr> <td>R2</td> <td>0</td> <td>0</td> </tr> <tr> <td>R3</td> <td>2.20</td> <td>30</td> </tr> <tr> <td>R4</td> <td>2.20</td> <td>50</td> </tr> <tr> <td>R5</td> <td>1.10</td> <td>10</td> </tr> <tr> <td>R6</td> <td>0.22</td> <td>10</td> </tr> <tr> <td>R7</td> <td>4.39</td> <td>10</td> </tr> <tr> <td>R8</td> <td>1.76</td> <td>10</td> </tr> <tr> <td>R9</td> <td>2.85</td> <td>10</td> </tr> <tr> <td>R10</td> <td>6.58</td> <td>10</td> </tr> <tr> <td>R11</td> <td>2.20</td> <td>20</td> </tr> <tr> <td>R12</td> <td>5.49</td> <td>10</td> </tr> <tr> <td>R13</td> <td>3.29</td> <td>10</td> </tr> <tr> <td>R14</td> <td>3.60</td> <td>10</td> </tr> </tbody> </table> <p>Each column in the file corresponds to the position and net velocity of particles, the fluid vorticity at particle location, particle id, and time step.</p> <table> <tbody> <tr> <td>column number</td> <td>item</td> </tr> <tr> <td>0</td> <td>x - position</td> </tr> <tr> <td>1</td> <td>y - position</td> </tr> <tr> <td>2</td> <td>z - position</td> </tr> <tr> <td>3</td> <td>x - velocity</td> </tr> <tr> <td>4</td> <td>y - velocity</td> </tr> <tr> <td>5</td> <td>z - velocity</td> </tr> <tr> <td>15</td> <td>x - vorticity</td> </tr> <tr> <td>16</td> <td>y - vorticity</td> </tr> <tr> <td>17</td> <td>z - vorticity</td> </tr> <tr> <td>19</td> <td>particle id</td> </tr> <tr> <td>20</td> <td>time step</td> </tr> </tbody> </table> <p> </p>

opencc-by-4.0Jan 2022View details →

Microswimmers in turbulent fluid flow of Taylor-scale Reynolds number Re = 59 dataset

<p><strong>The data includes the trajectories and the swimming velocities of individual microswimmers embedded in a turbulent fluid flow of Taylor-scale Reynolds number <span class="math-tex">\(Re_{\lambda} = 59\)</span> .</strong></p> <p>The net swimming velocity of a microswimmer is the sum of the intrinsic swimming velocity and the fluid velocity. The intrinsic swimming velocity is dictated by the swimming parameters, that is, the swimming speed <span class="math-tex">\(v_s\)</span> and the reorientation time <span class="math-tex">\(B\)</span>.</p> <p>Different values of swimming parameters are explored.</p> <table> <tbody> <tr> <td>Item</td> <td><span class="math-tex">\(v_s\)</span></td> <td>B</td> </tr> <tr> <td>Tr</td> <td>0</td> <td>0</td> </tr> <tr> <td>T13</td> <td>1</td> <td>10</td> </tr> <tr> <td>T16</td> <td>3</td> <td>10</td> </tr> <tr> <td>T18</td> <td>5</td> <td>10</td> </tr> <tr> <td>T19</td> <td>10</td> <td>0.3</td> </tr> <tr> <td>T25</td> <td>10</td> <td>1</td> </tr> <tr> <td>T27</td> <td>10</td> <td>3</td> </tr> <tr> <td>T29</td> <td>10</td> <td>10</td> </tr> <tr> <td>T30</td> <td>30</td> <td>10</td> </tr> <tr> <td>T31</td> <td>10</td> <td>30</td> </tr> <tr> <td>T33</td> <td>10</td> <td>50</td> </tr> <tr> <td>T38</td> <td>20</td> <td>10</td> </tr> <tr> <td>T43</td> <td>15</td> <td>10</td> </tr> <tr> <td>T50</td> <td>25</td> <td>10</td> </tr> <tr> <td>T56</td> <td>8</td> <td>10</td> </tr> </tbody> </table> <p>Each column in the file corresponds to the position and net velocity of particles, the fluid vorticity at particle location, particle id, and time step.</p> <p> </p> <table> <tbody> <tr> <td>column number</td> <td>item</td> </tr> <tr> <td>0</td> <td>x - position</td> </tr> <tr> <td>1</td> <td>y - position</td> </tr> <tr> <td>2</td> <td>z - position</td> </tr> <tr> <td>3</td> <td>x - velocity</td> </tr> <tr> <td>4</td> <td>y - velocity</td> </tr> <tr> <td>5</td> <td>z - velocity</td> </tr> <tr> <td>15</td> <td>x - vorticity</td> </tr> <tr> <td>16</td> <td>y - vorticity</td> </tr> <tr> <td>17</td> <td>z - vorticity</td> </tr> <tr> <td>19</td> <td>particle id</td> </tr> <tr> <td>20</td> <td>time step</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p>

opencc-by-4.0Apr 2024View details →

Dataset of "3D generative adversarial networks for turbulent flow estimation from wall measurements"

<p>Dataset of the article 'Three-dimensional generative adversarial networks for turbulent flow estimation from wall measurements' (https://doi.org/10.1017/jfm.2024.432). The codes processing data here are on https://github.com/erc-nextflow/3D-GAN.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 949085, NEXTFLOW). Views and opinions expressed are, however, those of the authors only, and do not necessarily reflect those of the European Union or the ERC. Neither the European Union nor the granting authority can be held responsible for them. A.C.M. acknowledges financial support from the Spanish Ministry of Universities under the Formación de Profesorado Universitario (FPU) programme 2020. R.V. acknowledges financial support from ERC (grant agreement no. 2021-CoG-101043998, DEEPCONTROL).</p>

opencc-by-4.0Aug 2021View details →

Supporting data for the article "Continuing invariant solutions towards the turbulent flow"

<p>Supporting data for the article "Continuing invariant solutions towards the turbulent flow": numerical code and computed invariant solutions</p>

opencc-by-4.0Sep 2021View details →

Corrigendum of "Assessment of the Elliptic Blending RSM Turbulence Model on a Wind Turbine-Type Rotor Under Mildly Compressible Blade Tip Flow Regime" and updated dataset

<p>This data accompanies the paper "Assessment of the Elliptic Blending RSM Turbulence Model on a Wind Turbine-Type Rotor Under Mildly Compressible Blade Tip Flow Regime", published in the 18th Brazilian Congress of Thermal Sciences and Engineering (2020): <a href="http://www.sistema.abcm.org.br/articleFiles/download/28958">http://www.sistema.abcm.org.br/articleFiles/download/28958</a>. It includes the corrected integral load values described in the PDF document.</p>

opencc-by-4.0Nov 2022View details →

An experimental and numerical study of turbulent oscillatory flow over an irregular rough wall

<p>Phase-averaged experimental data, phase- and spatial-averaged numerical data, and digitized roughness as described in: Dunbar, D., van der A, D.A., Scandura, T., O'Donoghue, T. (2023). An experimental and numerical study of turbulent oscillatory flow over an irregular rough wall. Journal of Fluid Mechanics, 955, A33, doi:10.1017/jfm.2022.1090.</p> <p>If interested in the time-series data and/or the spatially varying numerical data please contact the corresponding author of the paper. </p>

opencc-by-4.0Apr 2023View details →

PEPT Data - The effect of retrofit design modifications on the macro-turbulence of a three-phase flotation tank – Flow characterisation using positron emission particle tracking (PEPT).

<p>Supporting Information: for the paper "The effect of retrofit design modifications on the macro-turbulence of a three-phase flotation tank – Flow characterisation using positron emission particle tracking (PEPT)."</p> <p>The file contains the trajectory data and graph data for azimuthal slices obtained with PEPT.</p> <p> </p>