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17 results for “Reynolds number”
Low Reynolds number response of a symmetric airfoil to viscous vortical gusts
<p>The response of a NACA0012 airfoil impacted by viscous vortical gusts at low Reynolds numbers is investigated performing Direct Numerical Simulations of the two-dimensional incompressible flow. This database contains the time history of the aerodynamic force coefficients of the airfoil during the interaction with the vortical gust. The airfoil, set at a fixed angle of attack alpha, is impacted by Taylor/Lamb-Oseen vortical gust, which are characterized by a diameter <em>D</em>, a intensity <em>v<sub>0m</sub></em>, and a vertical separation <em>h</em>. Direct Numerical Simulations are run for a range of values for the angle of attack, the size and intensity of the vortical gust, and the vertical separations. All simulations are run at a fixed Reynolds number Re=1000, based on the airfoil chord <em>c</em> and the free-stream velocity <em>U<sub>∞</sub></em>.</p> <p>More details on the database and the corresponding simulations can be found in Martínez-Muriel & Flores (2020), Analysis of vortical gust impact on airfoils at low Reynolds number, J. Fluids and Struct, 99. </p> <p><strong>Contents</strong><br> The database consist on a single ASCII file for each case. After a short, self-explanatory header, each file has 7 columns with the following data: </p> <ul> <li>time, <em>t U<sub>∞</sub>/c</em></li> <li>cl: lift coefficient, <em>c<sub>l</sub></em></li> <li>cd: drag coefficient, <em>c<sub>d</sub></em></li> <li>cm: coefficient of moments with respect to c/4, <em>c<sub>m</sub></em></li> <li>dcl: perturbation of <em>c<sub>l</sub></em> with respect to steady state value, ∆<em>c<sub>l</sub></em> </li> <li>dcd: perturbation of <em>c<sub>d</sub></em> with respect to steady state value, ∆<em>c<sub>d</sub></em> </li> <li>dcm: perturbation of <em>c<sub>m</sub></em> with respect to steady state value, ∆<em>c<sub>m</sub></em> </li> </ul> <p>Reference time (t=0) is taken as the time at which the center of the vortical gust reaches the position of the leading edge of the airfoil (if advected at a velocity <em>U<sub>∞</sub></em>). <br> <br> <strong>Nomenclature </strong><br> The names of the files will follow the acronym t_AaYyDdVv.txt, where the lowecase letters are placeholders for: </p> <table> <tbody> <tr> <td> t </td> <td> Type of vortical gust</td> <td> T: Taylor, LO: Lamb-Oseen</td> </tr> <tr> <td> a </td> <td> Angle of attack </td> <td><em> α</em> = [+8,0,-8] deg</td> </tr> <tr> <td> y </td> <td> Initial vertical position of the centre of the vortex</td> <td><em> h/c </em>= [0,0.5,1]</td> </tr> <tr> <td> d </td> <td> Diameter of the core of the vortex </td> <td><em> D/c </em>= [0.5,1,2]</td> </tr> <tr> <td> v </td> <td> Circumferential velocity </td> <td><em> v<sub>0m</sub></em>/<em>U<sub>∞</sub></em> = [0.1,0.3,1]</td> </tr> </tbody> </table>
AVATAR HIGH REYNOLDS NUMBER TESTS ON AIRFOIL DU00-W-212
<p>Within EU FP7 AVATAR project (AdVanced Aerodynamic Tools of lArge Rotors), a high Reynolds number and low Mach number wind tunnel test has been performed with the aim to obtain reliable data that can be used to validate existing aerodynamic models for this operating range. The test has been performed at the DNW High Pressure Wind Tunnel in Göttingen (HDG).</p> <p> </p>
Dataset for "Helical dynamo growth at modest versus extreme magnetic Reynolds numbers"
<p>This directory contains the dataset (data.tar) and the post-processing script (post_processing.nb) of the manuscript "Helical dynamo growth at modest versus extreme magnetic Reynolds numbers" by Hongzhe Zhou and Eric Blackman.</p>
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>
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>
Effects of roughness Reynolds number on scalar transfer mechanisms at the sediment-water interface
<p>The data are used in the paper "Effects of roughness Reynolds number on scalar transfer mechanisms at the sediment-water interface" which was submitted to the journal-Water Resources Research. The paper used particles with different sizes, experimental bed is reproduced and the scalar transfer from water to sediment is modelled. We found that The transfer factor changes to turbulent diffusion at the sediment-water interface as the roughness Reynolds number increases. And the turbulent Schmidt number is higher near the SWI and decrease to 0.5 at the SWI, ranging from 0.5 to 1 within the water depth.</p>
Effects of Different Momentum Ratios and Reynolds Number in a T-junction with an Upstream Elbow
<p>In accordance with EPSRC funding requirements, this supplementary dataset provides all supporting data used to create figures in the following reference:</p> <p>Wong, Y. H., Lampunio, L., Duan, Y., Eaton, M. D., & Bluck, M. J. (2024). Effects of different momentum ratios and Reynolds number in a T-junction with an upstream elbow. <em>Nuclear Engineering and Design</em>, <em>428</em>. https://doi.org/10.1016/j.nucengdes.2024.113523</p> <div> </div> <p> </p>
COMSOL - Pumping well under high Reynolds number flow conditions
<p>This is a coupled multiphysics model that describes non-Darcy flow in porous media and turbulent flow in a wellbore during a pumping event. The numerical model was developed and constructed in COMSOL Multiphysics® 6.0, a commercial finite element analysis and solver software. See <a href="https://www.comsol.com/">https://www.comsol.com/</a>.</p> <p>Simulation data is provided. A 1D finite-difference approach is also developed on MATLAB to directly solve for the behavior of nonlinear flow in a pumping system. MAT file is provided.</p>
Reynolds Creek Experimental Watershed site, station Owyhee County, ID (FIPS 16073), study of number of farms in units of number on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Reynolds Creek Experimental Watershed (RCE) contains number of farms measurements in number units and were aggregated to a yearly timescale.
Reynolds Creek Experimental Watershed site, station Owyhee County, ID (FIPS 16073), study of human population (total) in units of number on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Reynolds Creek Experimental Watershed (RCE) contains human population (total) measurements in number units and were aggregated to a yearly timescale.
Reynolds Creek Experimental Watershed site, station Owyhee County, ID (FIPS 16073), study of population (urban) in units of number on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Reynolds Creek Experimental Watershed (RCE) contains population (urban) measurements in number units and were aggregated to a yearly timescale.
Data set: Grain Reynolds number scale effects in dry granular slides
<p>Scale series of velocity, flow depth and run out data for dry granular materials flowing down a slope with side walls. </p>
Data-driven subgrid-scale modeling of forced Burgers turbulence using deep learning with generalization to higher Reynolds numbers via transfer learning
<p>These are the data files for use with the codes in https://github.com/envfluids/Burgers_DDP_and_TL.</p>
Input data for estimating dimensionless number (Reynolds, Swimming and Strouhal number) of swimming penguin
<p>Propulsion performance of swimming and flying animals is often evaluated by using dimensionless numbers, such as the Strouhal and Reynolds numbers. They have been shown to allow better understanding of locomotion efficiency, using relatively simple approaches and avoiding overly complex computational models. Specifically, it has been reported that efficient propulsion is more likely to occur when Strouhal number values – estimated from propulsive frequencies and amplitudes – are within a relatively narrow range, depending on the corresponding Reynolds number, broadly expressing the fluid resistance to the animal motion. We have estimated both Strouhal and Reynolds numbers for seven species of penguins after analysing relevant kinematic data taken from the literature. The obtained values neatly indicate that, as expected, penguins employ efficient propulsion mechanisms. Additionally, by comparing these values with those for alcids – seabirds that can also fly – we have found that penguins swim at least as efficiently as alcids. However, we have also found that the swimming number – proportional to the product of Strouhal and Reynolds numbers – neatly correlates to the diving abilities of the considered species and apparently indicates, in a straightforward hierarchical manner, the gains in diving due to the loss of flying abilities. Within the penguin species, a clear correlation is also observed between diving performance and drag coefficient values.</p>
Microswimmers in turbulent fluid flow of Taylor-scale Reynolds number Re = 36 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">\(Re_{\lambda} = 36\)</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>S1</td> <td>5.24</td> <td>10</td> </tr> <tr> <td>S2</td> <td>0</td> <td>0</td> </tr> <tr> <td>S3</td> <td>5.24</td> <td>30</td> </tr> <tr> <td>S4</td> <td>5.24</td> <td>50</td> </tr> <tr> <td>S5</td> <td>2.62</td> <td>10</td> </tr> <tr> <td>S6</td> <td>0.53</td> <td>10</td> </tr> <tr> <td>S7</td> <td>4.39</td> <td>10</td> </tr> <tr> <td>S8</td> <td>4.18</td> <td>10</td> </tr> <tr> <td>S9</td> <td>6.80</td> <td>10</td> </tr> <tr> <td>S10</td> <td>15.47</td> <td>10</td> </tr> <tr> <td>S11</td> <td>5.24</td> <td>20</td> </tr> <tr> <td>S12</td> <td>13.08</td> <td>10</td> </tr> <tr> <td>S13</td> <td>8.25</td> <td>10</td> </tr> <tr> <td>S14</td> <td>9.49</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>
Head-on quenching of turbulent premixed NH3/H2/air flames in the channel flow with friction Reynolds number 300
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Input data for estimating dimensionless number (Reynolds, Swimming and Strouhal number) of swimming penguin
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