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5,371 results for “Flow”
Investigating the Unsteady Dynamics of a Multi-Jet Impingement Cooling Flow Using Large Eddy Simulation - Promotional Video and Image
<p>Video: Volume rendering of temperature field obtained from a large eddy simulation of 9 inline impinging jets in a narrow channel.</p> <p>Image: Turbulent structures as isosurface of Q-criterion with an illustration of laser sheet used for PIV measurement.</p> <p>The results were presented at the ASME Turbo Expo 2024 (paper number GT2024-122465) and published in the ASME Journal of Turbomachinery (<a href="https://doi.org/10.1115/1.4066508">https://doi.org/10.1115/1.4066508</a>). The accepted manuscript of the paper is available under <a href="https://elib.dlr.de/207257/">https://elib.dlr.de/207257/</a>.</p> <p>The simulations were performed on DLR's HPC system <a href="https://www.dlr.de/en/research-and-transfer/research-infrastructure/hpc-cluster/cara">CARA</a> within the DLR project InnoCool.</p>
Heat Flow Density in Europe - GeoDH project
<p>The dataset includes a shapefile showing areas in Europe where the Heat Flow Density is greater than 90 mW/m². <br><br>This dataset was developed for assessing the potential of Geothermal District Heating in Europe as part of the <strong>GeoDH project</strong> (<a href="http://geodh.eu/" target="_new" rel="noopener">http://geodh.eu/</a>). Please note that this represents the<strong> state of the art as of 2014</strong> and that geological, technological, and regulatory developments may have occurred since its creation, and users should verify if more recent data is available for their purposes.</p>
Data for "Archetypal flow regime change classes and their associations with anthropogenic drivers of global streamflow alterations"
<p>Data repository for "Archetypal flow regime change classes and their associations with anthropogenic drivers of global streamflow alterations"</p>
Movies: herringbone flows and flapping waves, 2d inclined inviscid Saint-Venant equations
<p>Supplementary movies for <em><strong>Multidimensional stability and transverse bifurcation of hydraulic shocks and roll waves in open channel flow</strong>.</em></p> <p>See <a href="https://github.com/zyang-pde/Multi-d_inviscid_Saint-Venant_eqs">https://github.com/zyang-pde/Multi-d_inviscid_Saint-Venant_eqs</a> for source codes used to generate these movies.</p> <p><strong>dam_break_F_equals_2_point_25.mp4</strong></p> <p>Dam-break initial data with left fluid height 1, right fluid height 0.7, and Froude number 2.25. The discontinuous hydraulic shock is 2d convectively unstable. Herringbone flows are seen at the end.</p> <p><strong>flat_F_equals_2_point_25.mp4</strong></p> <p>Flat initial data with co-moving speed set to be that of discontinuous hydraulic shock with left limiting fluid height 1, right limiting fluid height 0.7, and Froude number 2.25. Herringbone flows, parabola, and roll waves are seen at the end.</p> <p><strong>dam_break_comparison.mp4</strong></p> <p>Comparison of simulations with dam break initial data and with Froude numbers 2.13,2.14,2.15.</p> <p><strong>flat_comparison.mp4</strong></p> <p>Comparison of flat simulations with flat initial data and with Froude numbers 2.13,2.14,2.15.</p> <p> </p> <p>Movies below are simulated with perturbed roll waves initial data. The Froude number is set to be 6 and the minimum fluid height is set to be 0.28 and the channel width and the y-boundary condition are varied.</p> <p><strong>roll_width_point15.mp4</strong></p> <p>With <strong>wall</strong> boundary condition, the width of the channel is set to be <strong>0.15</strong>. No flapping front is seen at the end. Channel roll waves are stable.</p> <p><strong>roll_width_point16.mp4</strong></p> <p>With <strong>wall</strong> boundary condition, the width of the channel is set to be <strong>0.16</strong>. No flapping front is seen at the end. Channel roll waves are stable.</p> <p><strong>roll_width_point17.mp4</strong></p> <p>With <strong>wall</strong> boundary condition, the width of the channel is set to be <strong>0.17</strong>. No flapping front is seen at the end. Channel roll waves are stable.</p> <p><strong>roll_width_point18.mp4</strong></p> <p>With <strong>wall</strong> boundary condition, the width of the channel is set to be <strong>0.18</strong>. Wave fronts start to flap after a while. The flapping waves are persistent and do not become chaotic.</p> <p><strong>roll_width_point18_refined.mp4</strong></p> <p>With <strong>wall</strong> boundary condition, the width of the channel is set to be <strong>0.18</strong>. Wave fronts start to flap after a while. The flapping waves are persistent and do not become chaotic. Finer mesh grid is used compared with roll_width_point18.py. Raw data files are used to generate figures in the paper.</p> <p><strong>roll_width_point18_periodic.mp4</strong></p> <p>With <strong>periodic</strong> y-boundary condition, the width of the channel is set to be <strong>0.18</strong>. No flapping front is seen at the end. Channel roll waves are stable.</p> <p><strong>roll_width_point36_periodic.mp4</strong></p> <p>With <strong>periodic</strong> y-boundary condition, the width of the channel is set to be <strong>0.36</strong>. Wave fronts start to flap after a while. The flapping waves are persistent and do not become chaotic.</p> <p><strong>roll_width_point19.mp4</strong></p> <p>With <strong>wall</strong> boundary condition, the width of the channel is set to be <strong>0.19</strong>. Wave fronts start to flap after a while. The flapping waves are persistent and do not become chaotic.</p> <p><strong>roll_width_point2.mp4</strong></p> <p>With <strong>wall</strong> boundary condition, the width of the channel is set to be <strong>0.2</strong>. Wave fronts start to flap after a while. The flapping waves are also unstable, transitioning to chaotic flow at the end.</p> <p><strong>roll_width_1.mp4</strong></p> <p>With <strong>wall</strong> boundary condition, the width of the channel is set to be <strong>1</strong>. Wave fronts start to flap after a while. The flapping waves are also unstable, transitioning to chaotic flow at the end.</p> <p> </p>
Derived daily timeseries of weather, soil moisture and temperature, flow and nitrogen species (nitrate and nitrite, ammonium) concentrations data for the North Wyke Farm Platform National Biosciences Research Infrastructure, England
<p>For a selection of catchments from the North Wyke Farm Platform in southwest England, where land use conversions have been introduced, daily time series data covering weather conditions (minimum temperature, maximum temperature, total rainfall, wind speed and solar radiation), near-surface soil status (moisture content and temperature), flow and concentrations of key nitrogen species (nitrate and nitrite, ammonium) have been filtered based on attached data quality tags . The datasets run between 2013 and March 2024. For the main climate variables, data gaps were infilled with preceding- and following-on daily data, observations from a nearby weather station or existing national datasets to generate a continuous data series for modelling. For the other data series, annual and seasonal summary statistics on data coverage are provided. Information on significant field events, such as ploughing, drilling and harvest, fertiliser applications and manure spreading were also tabulated.</p>
A benchmark dataset for the grazing flow over porous materials
<p>Wind-tunnel data of a grazing flow over porous wall-inserts to be used as a benchmark dataset for the development and validation of numerical modeling approaches of flows over and through porous media. </p> <p>The dataset contains several profiles along the streamwise extent of the wall-insert of the mean velocity magnitude, the turbulent intensity, and the turbulent length scale. Also included are some boundary-layer parameters of these profiles. These have been derived from single-component constant temperature hot-wire measurements.</p> <p>Additionally, the spectra of the unsteady wall-pressure fluctuations at several locations on the upper and lower surfaces of the porous wall-inserts are provided. These unsteady pressure measurements have been acquired using semi-infinite waveguide-type remote-microphone probes.</p> <p>Tested are two porous media with the same <em>diamond-lattice</em> pattern structure but different permeabilities and a reference solid-walled case. All three cases are tested at three inflow velocities: 15 m/s, 20 m/s, and 25 m/s.</p> <p> </p> <p>Modification in v3: Correction of permeability values in Table 1 on page 3 of <em>AIAA_Manuscript_GrazingFlowPorousMaterials_v3.pdf</em>.</p>
Country-ocean-moisture-flows-reconciled-with-ERA5-reanalysis obtained processing Lagrangian moisture connections
<p>The dataset "Reconciled global atmospheric moisture flows between countries/oceans and subcontinents" presents tracked volumes of precipitation and evaporation reconciled with reanalysis data, closing the annual hydrological balance, and provides robust estimates of terrestrial moisture recycling and net moisture flows to support global water governance analysis.</p> <p>This repository is supplement to a study by De Petrillo & Fahrländer et al. (2025), which describes the development of the reconciliation framework, includes a perfromance analysis of the method and shows an exemplary case study on the published data. </p> <p>The atmospheric moisture flows are sourced from the UTrack atmospheric moisture flow dataset by Tuinenburg et al. (2020a) (dataset access: Tuinenburg et al., 2020b) and reconciled with ERA5 precipitation and evaporation data (Hersbach et al., 2020) on the mean annual basis in the period 2008-2017, by means of a post-processing framework, based on the Iterative Proportional Fitting (IPF) algorithm.</p> <p>NOTE: The final dataset is available in form of bilateral matrices (country/ocean and subcontinent/ocean) and in form of direct flows (flow edges). Supporting material to read the dataset is in the folder "List" . Processed ERA5 data (where the precipitation-evaporation annual balance is met) and input data to generate the figures are also available.</p> <p>References:</p> <p>De Petrillo, E., Fahrländer, S., Tuninetti, M., Andersen, L.S., Monaco, L., Ridolfi, L., Laio, F. (2025). Reconciling tracked atmospheric moisture flows to close the global freshwater cycle.<em> </em><em>Commun Earth Environ <strong>6</strong>, 347 (2025). </em><a href="https://doi.org/10.1038/s43247-025-02289-y">https://doi.org/10.1038/s43247-025-02289-y</a></p> <p>Tuinenburg, O. A., Theeuwen, J. J. E., & Staal, A. (2020a). High-resolution global atmospheric moisture connections from evaporation to precipitation. <em>Earth System Science Data</em>, <em>12</em>(4), 3177–3188. <a href="https://doi.org/10.5194/essd-12-3177-2020">https://doi.org/10.5194/essd-12-3177-2020</a></p> <p>Tuinenburg, O. A., Theeuwen, J. J. E., Staal, A. (2020b): Global evaporation to precipitation flows obtained with Lagrangian atmospheric moisture tracking. PANGAEA, <a href="https://github.com/ObbeTuinenburg/UTrack_global_database">https://doi.pangaea.de/10.1594/PANGAEA.912710</a></p> <p>Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., et al. (2020). The ERA5 global reanalysis. <em>Quarterly Journal of the Royal Meteorological Society</em>, <em>146</em>(730), 1999–2049. <a href="https://doi.org/10.1002/qj.3803">https://doi.org/10.1002/qj.3803</a></p> <p> </p> <p> </p>
Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance - Data
<p>Datasets and R code related to manuscript entitled, "Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance". See '0_READ_ME.rtf' file for additional description of available files.</p>
The impulse response of optic flow sensitive descending neurons to roll m-sequences
<p>When animals move through the world, their own movements generate widefield optic flow across their eyes. In insects, such widefield motion is encoded by optic lobe neurons. These lobula plate tangential cells (LPTCs) synapse with optic flow sensitive descending neurons, which in turn project to areas that control neck, wing and leg movements. As the descending neurons play a role in sensori-motor transformation, it is important to understand their spatio-temporal response properties. Recent work shows that a relatively fast and efficient way to quantify such response properties is to use m-sequences or other white noise techniques. We therefore here used m-sequences to quantify the impulse responses of optic flow sensitive descending neurons in male <i>Eristalis tenax </i>hoverflies. We focused on roll impulse responses as hoverflies perform exquisite head roll stabilizing reflexes, and the descending neurons respond particularly well to roll. We found that the roll impulse responses were fast, peaking after 16.5-18.0 ms. This is similar to the impulse response time-to-peak (18.3 ms) to widefield horizontal motion recorded in hoverfly LPTCs. We found that the roll impulse response amplitude scaled with the size of the stimulus impulse, and that its shape could be affected by the addition of constant velocity roll or lift. For example, the roll impulse response became faster and stronger with the addition of excitatory stimuli, and vice versa. We also found that the roll impulse response had a long return to baseline, which was significantly and substantially reduced by the addition of either roll or lift.</p>
Data for Roles of Granularity and Timescales in Debris Flow Hazards on Alluvial Fans
<p>This dataset includes the digital elevation models (DEM) for the 9 debris flow fan experiments and the slope map data for the 9 debris flow fan experiments and 2 field cases (the Straight Fan and Piute Fan in White Mountain, CA). These data are stored as GeoTIFF files that include information on mesh coordinates. Please read the Data_Information.pdf for the details of the data file contents, duration, sediment contents, flow/discharge/input rates, and mesh size. </p>
Synthetic dataset for the flow-shop problem with sequence-dependent setup times (FSsdsu_DS_01)
<p>Synthetic dataset containing setup and processing times for a flow-shop problem of 1.000 parts and 200 machines. The setup times are specified for each machine taking into account the previous part produced on the machine.</p>
Synthetic dataset for the flow-shop problem (FS_DS_01)
<p>Synthetic dataset containing setup and processing times for a flow-shop problem of 1.000 parts and 200 machines.</p>
Data published in manuscript "Effects of reversal of water flow in an Arctic floodplain river on fluvial emissions of CO2 and CH4" by Castro-Morales et al.
<p>This data is published in the manuscript<strong>:</strong></p> <p>Castro-Morales, K., Canning, A., Körtzinger, A., Göckede, M., Küsel, K., et al. (2022). Effects of reversal of water flow in an Arctic floodplain river on fluvial emissions of CO<sub>2</sub> and CH<sub>4</sub>. <em>Journal of Geophysical Research: Biogeosciences</em>, 127, e2021JG006485. <a href="https://doi.org/10.1029/2021JG006485">https://doi.org/10.1029/2021JG006485</a>.</p> <p>The data contains the water properties and gases data measured at a site in Ambolikha River, meteorological data measured at an eddy covariance tower located in the neighbor floodplain, and data from the analysis of dissolved organic matter in river water samples. The data was collected between 26 June, 2019 and 02 August, 2019.<strong> </strong></p> <p>This folder contains four data files and the file "README_Data_access_Castro-Morales_etal_Ambolikha_River.txt" should be read before accessing the data. The authors recommend downloading Version 2.0 because it is the most up to date data.</p> <p>For questions contact the main and corresponding author Dr. Karel Castro-Morales at: karel.castro.morales@uni-jena.de</p>
Datasets, codes and video clips for the laboratory flume tests of granular flow
<p>Datasets, video clips, and codes related to the paper 'Insight into granular flow dynamics relying on basal stress measurements: from experimental flume tests', submitted to the Journal of Geophysical Research: Solid Earth.</p> <p>The datasets provides the raw and processed data for the laboratory flume tests of granular flow including parameters reflecting the granular flow behavior, basal normal stresses measured by a force plate, and deposit parameters of the granular flows.</p> <p><strong>S1_data_granular flow_velocity</strong> provides data of the velocity profiles with a 0.1 second time interval, the depth-averaged velocities, the depth-averaged shear rates, and the solid inertial stresses of the granular flows under different experimental conditions. The original data were calculated through particle image velocimetry (PIV) method. The images for PIV analysis were recorded by a high-speed camera.</p> <p><strong>S2_data_granular flow_stress</strong> provides the raw data of the measured basal normal stresses of the granular flows for all tests. The mean and fluctuating stress components extracted by applying a moving window average filter are also listed in the Table.</p> <p><strong>S3_data_granular flow_flow depth</strong> provides the data of the granular flow depth extracted every 0,02 s through a image processing method based on the high-speed photographs. </p> <p><strong>S4_data_granular flow_deposit</strong> provides the parameters of the granular flow deposits for all tests including the apparent friction coefficient and equivalent friction coefficient. The deposit parameters were calculated based on the digital surface model (DSM) of deposit, which were obtained through a oblique photogrammetry method.</p> <p><strong>S5_data_granular flow_density</strong> gives the data of the dynamic bulk flow densities of the granular flows under all experimental conditions. The dynamic bulk densities were calculated according to the measured and calculated normal stresses.</p> <p>The videos of the granular flows under different experimental conditions during their propagation are provided in <strong>'S6_video_granular flow.zip'</strong> to show the granular flow behavior and its evolution. <strong>S6_video_granular flow</strong> includes the side-view of the granular flows under all experimental conditions and front-view of the IMF-223 granular flow . </p> <p><strong>S7_codes_data analysis</strong> provides the computer codes for the extraction of mean and fluctuating components and the calculation of granular flow depth. The former includes one file for conducting moving average filter. The latter contains four files, which are used for median filter, image erosion, threshold segmentation and floodfill, extracting flow depth. </p>
Chaos and irreversibility of a flexible filament in periodically--driven Stokes flow
<pre>This directory contains a jupyter notebook file, generate_plots.ipynb which generate all the plots for paper "Chaos and irreversibility of a flexible filament in periodically driven Stokes flow" (arXiv:2111.14638) by Vipin Agrawal and Dhrubaditya Mitra. Readme.md file contains the information on how to use the data. If anything turns out to be incomplete, please reach to vipinagrawal25@gmail.com.</pre>
Gradient winds and neutral flow dawn-dusk asymmetry in the auroral oval during geomagnetically disturbed conditions (data files)
<p>Data files with wind profiles used to generate figures in the paper entitled "Gradient winds and neutral flow dawn-dusk asymmetry in the auroral oval during geomagnetically disturbed conditions"</p>
Near-inertial wave energetics modulated by background flows in a global model simulation
<p>This data set contains data from a forward global HYCOM simulation (EXPT 19.2) with realistic atmospheric forcing. This is a 4-km simulation with 41 layers. All data is on the native tri-polar grid. Data is stored as netcdf4 classic. The 2D data sets are 7055 x 9000 (lat x lon).</p> <p>More details and context of this data can be found in the article: <a href="https://doi.org/10.1175/JPO-D-21-0130.1">https://doi.org/10.1175/JPO-D-21-0130.1</a>. Please cite this article along with any use of this data.<br> Raja, K. J., Buijsman, M. C., Shriver, J. F., Arbic, B. K., & Siyanbola, O. (2022). Near-Inertial Wave Energetics Modulated by Background Flows in a Global Model Simulation, <em>Journal of Physical Oceanography</em>, <em>52</em>(5), 823-840.</p>
Bening Flow Data Train (D1)
<p>Train dataset only contains benign traffic, this dataset has been collected using Netflow and implementing a sampling rate of 1 packet out of each 1000 to generate the flows, simulating the conditions of the RedCayle’s routers.</p> <p>The traffic has been generated using three Python scripts. The first one simulates email sending using SMTP protocol. The second script simulates SSH connections. The third script simulates a user browsing the internet using different search engines and different protocols like HTTP and HTTPS.</p>
Synthesizing Multiple Long-Term Datasets to Test Flow Ecology Relationships
<p>This database is comprised of time series of fish abundances at stream and river sites across the contiguous United States, along with associated hydrologic and land cover metrics. The database has been assembled in order to test hypothesized fish flow-ecology relationships across diverse species assemblages and riverine contexts. Methods for compilation of the database and additional information can be found in two accompanying files: "SynthesizingDatasetsForFlowEcology_v1_0_0.pdf" contains detailed methodology and results, and "ReadMe_FishFlow_v1_0_0.csv" contains information about the variables found within each database file. This project was funded by a US Army Corps of Engineers contract to the US Geological Survey, with a subcontract to the University of Georgia. Please note, the contract number for this project is G21AC10482, not G21AS00529 as is stated in the summary document (SynthesizingDatasetsForFlowEcology_v1_0_0.pdf).</p>
Laboratory Open Channel Flow: Video, Waterlevel and Surface Velocity
<p>Video footage of an open channel flow in a laboratory setting, associated with the surface velocity and water level.</p> <p><br> - Video footage was collected using a Raspberry Pi Camera Module v2 attached to a Raspberry Pi 4 at 25fps from three positions and split into roughly 15s chunks.<br> - A "mic+35/IU/TC" ultrasonic sensor (accuracy: ±1%) measured the water level<br> - A "Nortek Vectrino" (accuracy: ±1% ±1mm/s) velocimeter measured the velocity at the surface</p> <p> </p> <p>- The video files can be found in the folders position1, position2 and position3, each file name contains the initial timestamp to match frames to the measurements<br> - The file "waterlevel.csv" contains the timestamps, Waterlevel [mm] and Percentage Full [%]. The waterlevel column is reversed, as the distance between the sensor and the surface was measured. This means, that lower values correspond to higher water levels.<br> - The file "velocity.csv" contains the timestamps and surface velocity measurements [m/s]</p>
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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.