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170 results for “turbulent flow”
Data from: Investigation of turbulent flow in a Schwarz Diamond monolith using MRI and CFD
<p>MRI data from article: Clarke, Coe, Galvosas, and Holland. Investigation of turbulent flow in a Schwarz Diamond monolith using MRI and CFD (2025) Chemical Engineering Journal, 524, 168921</p> <p>Note that the data.zip file decompresses to about 7.5 GB.</p> <p>Version 2:</p> <p>- Fixed critical bugs in data processing scripts pertaining to how spiread.m was called (lines 37, 50 of spiVelProc3D_CS_5step.m, line 30 of turbDiffProc.m). </p> <ul> <li>Added spiread.m to the turbulentDiffusivity directory to fix issue where TopSpin data was read with a custom function not included in this dataset.</li> </ul> <p>Edited description on 2025.10.10 to include journal details.</p>
Data for "The variation of flow and turbulence across the sediment–water interface"
<p>See readme.</p> <p>Voermans, J. J., Ghisalberti, M., & Ivey, G. N. (2017). The variation of flow and turbulence across the sediment–water interface. Journal of Fluid Mechanics, 824, 413-437.</p> <p>Voermans, J. J., Ghisalberti, M., & Ivey, G. N. (2018). A model for mass transport across the sediment‐water interface. Water Resources Research, 54(4), 2799-2812.</p> <p>Voermans, J. J., Ghisalberti, M., & Ivey, G. N. (2018). The Hydrodynamic Response of the Sediment‐Water Interface to Coherent Turbulent Motions. Geophysical Research Letters, 45(19), 10-520.</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>
Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow
<p>Data for the six test cases of 2D turbulence explored in the paper, Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow.</p>
Biofilm flow data from: Roughness effects of diatomaceous slime fouling on turbulent boundary layer hydrodynamics
<p>This dataset contains the instantaneous velocity vector fields as well as the time averaged velocity and turbulence fields from PIV data taken over a large acrylic plate fouled with a relatively uniform diatomaceous biofilm. </p> <p>See associated article, Roughness effects of diatomaceous slime fouling on turbulent boundary layer hydrodynamics, for methods description.</p> <p>Time averaged velocity and turbulence fields data are stored in the file velocity_fields.mat, which contains the following variables: </p> <p>X: The streamwise distance of each column in the velocity field matrices [mm]</p> <p>Y: The vertical distance (from the bottom of the frame) of each row in the velocity field matrices [mm]</p> <p>U: Time averaged streamwise velocity [m s^-1]</p> <p>V: Time averaged vertical velocity [m s^-1]</p> <p>tke: Time averaged turbulent kinetic energy [m^2 s^-2]</p> <p>u': Time averaged streamwise Reynolds stress [m^2 s^-2]</p> <p>v': Time averaged vertical Reynolds stress [m^2 s^-2]</p> <p>u'v': Time averaged Reynolds shear stress [m^2 s^-2]</p> <p>The zip file biofilm_vector_fields contains the 4,000 statistically independent velocity vector fields used to compute the time averaged velocity and turbulence fields. The vector field data is in the variable labeled matr. Size calibration: 2302 pixels/ inch (906.3 pixels/ cm). </p> <p>Column 1: X (streamwise distance in [pixels]) </p> <p>Column 2: Y (wall-normal distance from bottom of frame in [pixels])</p> <p>Column 3: U (streamwise velocity vector in [pixels / 250 microseconds]) </p> <p>Column 4: V (vertical velocity vector in [pixels / 250 microseconds]) </p> <p>Column 5: CHC (number of tracked particles. A value < 1 gives the location of the biofilm, which was masked out)</p> <p>Column 6: U2 </p> <p>Column 7: V2</p> <p>Column 8: U3</p> <p>Column 9: V3</p> <p>Column 10: U4</p> <p>Column 11: V4</p>
Turbulent and laminar flow in karst conduits under unsteady flow conditions – interpretation of pumping tests by discrete conduit-continuum modeling
<p>Due to the duality in terms of (1) the groundwater flow field and (2) the discharge conditions, flow patterns of karst aquifer systems are complex. Estimated aquifer parameters may differ by several orders of magnitude from local (borehole) to regional (catchment) scale because of the large aquifer heterogeneity and anisotropy. One approach to deal with this scale problem in karst aquifers is the application of large-scale experiments such as long-term high-abstraction conduit pumping tests, stimulating measurable groundwater drawdown in both, the karst conduit system as well as the fissured and fractured aquifer matrix. The numerical discrete conduit-continuum modeling approach MODFLOW-2005 Conduit Flow Process Mode1 (CFPM1) is employed to simulate laminar and non-laminar conduit flow, induced by large-scale experiments, in combination with Darcian matrix flow. Effects of large-scale experiments were simulated for idealized settings. Subsequently, diagnostic plots and analyses of different fluxes are applied to interpret differences in the simulated conduit drawdown and general flow patterns.</p> <p>The main focus is set on the question to which extent different conduit flow regimes will affect the conduit as well as the matrix drawdown depending on the hydraulic properties of the conduit system, i.e. conduit diameter and relative roughness In this context, CFPM1 is applied to investigate the significance of turbulent flow consideration for the simulation of karst conduit flow. This work quantifies the relative error that results from assuming laminar conduit flow for the interpretation of a synthetic large-scale pumping test in karst.</p> <p>https://doi.org/10.1002/2017WR020658</p> <p> </p>
Head-on quenching of turbulent premixed NH3/H2/air flames in the channel flow with friction Reynolds number 300
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Data from: A bird’s eye view on turbulence: Seabird foraging associations with evolving surface flow features
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State estimation of surface and deep flows from sparse SSH observations of geostrophic ocean turbulence using Deep Learning
<p>This is a data repository in support of the publication "State estimation of surface and deep flows from sparse SSH observations of geostrophic ocean turbulence using Deep Learning" by Manucharyan et al. (2020), Journal of Advances in Modeling Earth Systems. The zipped file contains 10-day-separated snapshots of surface and deep ocean streamfunctions from the two-layer quasigeostrophic model of ocean turbulence. The included Python scripts demonstrate the efficacy of Deep Learning in temporal interpolation and state estimation given partial observations of the surface ocean turbulence.</p> <p> </p>
Turbulence Structure and Momentum Exchange in Compound Channel Flows with Shore Ice covered on the Floodplains
<p>This data deposit contains all the datasets needed to draw 9 Figures (4, 8-10, 12-16) in the paper "Turbulence Structure and Momentum Exchange in Compound Channel Flows with Shore Ice covered on the Floodplains", which is now under consideration for potential publication in Water Resources Research. </p>
Pathways, form drag, and turbulence in a simulated ocean flowing through through an ice melange: datasets
<p>Data associated with Hughes (2022). <em>J. Geophys. Res. </em><a href="https://doi.org/10.1029/2021JC018228">doi:10.1029/2021JC018228</a></p> <p>This archive contains</p> <ol> <li>Output from all model simulations together with a summary csv file. Full details in <strong>melange_flow_simulations_readme.pdf</strong></li> <li>A directory (<strong>icebergs.zip</strong>) containing the scripts necessary to reproduce model outputs described in the paper.</li> </ol> <p> </p>
Data for "Turbulence modulation in non-uniform open-channel clay suspension flows"
<p>This dataset details experimental results on the flow structure of clay-laden flows, for the first time isolating the effect of non-uniformity on spatial deceleration and acceleration in open-channel flows. </p>
Data from: Palaeoecological implications of the preservation potential of soft-bodied organisms in sediment-density flows: testing turbulent waters
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Aorta intimal macrophages prevent the formation of intravascular clots in regions of turbulent flow
GEO Series GSE161787. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Turbulent pipe flow up to Re_tau = 1000
<p>Data from the paper:</p> <p><a href="https://scholar.google.com/citations?view_op=view_citation&hl=no&user=a-0MJGMAAAAJ&citation_for_view=a-0MJGMAAAAJ:qjMakFHDy7sC">Direct numerical simulation of turbulent pipe flow at moderately high Reynolds numbers</a></p> <p>GK El Khoury, P Schlatter, A Noorani, PF Fischer, G Brethouwer, ...</p> <p>Flow, turbulence and combustion 91 (3), 475-495</p>
Rigid fiber transport in a periodic non-homogeneous geophysical turbulent flow
<p>From anthropogenic litter carried by ocean currents to plant stems traveling through the atmosphere, geophysical flows are often seeded with elongated, fiber-like particles. In this study, we used a large-scale laboratory model of a tidal current -- representative of a widespread class of geophysical flows -- to investigate the tumbling motion of long, slender, and floating fibers in the complex turbulence generated by flow interactions with a tidal inlet. Despite the non-stationary, non-homogeneous, and anisotropic nature of this turbulence, we find that long fibers statistically rotate at the same frequency as eddies of similar size, a phenomenon called \textit{scale selection}, which is known to occur in ideal turbulence. Furthermore, we report that the signal of the instantaneous transverse velocity difference between the fiber ends differs significantly from the signal produced by the flow in the fiber surroundings, although the two are statistically equivalent. These observations have twofold implications. On the one hand, they confirm the reliability of using the end-to-end velocity signal of rigid fibers to probe the two-point transverse statistics of the flow, even under realistic conditions: oceanographers could exploit this observation to measure transverse velocity differences through elongated floats in the field, where superdiffusion complicates collecting sufficient data to probe two-point turbulence statistics at a fixed separation effectively. On the other hand, by addressing the dynamics of inertial range particles floating in the coastal zone, these observations are crucial to improving our ability to predict the fate of meso- and macro-litter, a size class currently understudied.</p>
Differentiating erythroblasts adapt to turbulent flow by accelerating maturation and activating cholesterol biosynthesis.
GEO Series GSE247121. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing.
Collaborative Testing of Turbulence Models: Incompressible Flow Cases from 1980-81 Data Library
This grouping contains the incompressible-flow cases from the 1980-81 Data Library.
Collaborative Testing of Turbulence Models: Incompressible Flow Cases from 1980-81 Data Library
This grouping contains the incompressible-flow cases from the 1980-81 Data Library.
Collaborative Testing of Turbulence Models: Incompressible Flow Cases from 1980-81 Data Library
This grouping contains the incompressible-flow cases from the 1980-81 Data Library.
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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.