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112 results for “fluid dynamics”
An experimental data set on the thermal and fluid dynamic performance of double skin facades (DSFs) subjected to various controlled boundary conditions through the use of a climate simulator facility
<p>Double skin facades (DSFs) are building envelope systems defined by complex phenomena and non-linear-processes that make characterizing their performance a non-trivial task. In an effort to enable the scientific community to access experimental data for further analysis or model validation purposes, we release together with the open-access paper entitled “<strong><em>Laboratory testbed and methods for flexible characterization of the thermal and fluid dynamic behavior of double skin facades” (</em></strong><a href="https://doi.org/10.1016/j.buildenv.2021.108700"><strong><em>https://doi.org/10.1016/j.buildenv.2021.108700</em></strong></a><strong><em>)</em></strong>, a set of experimental data collected during tests carried out with the use of the newly developed testbed. The data contains the results of a series of tests where various configurations of a full-scale DSF mock-up that have been subjected to different boundary conditions replicated in a climate simulator. The database contains a guide in the form of the file ‘Guide.pdf’, which explains how to read data, presents a schematic drawing of sensor layout, and provides more information on sensors’ positions. Further information on the original aims of the experiments, methods, and other data can be found in the article mentioned above, which becomes an essential tool to understand how to read and interpret the experimental data fully. The following collection of experimental data are provided:</p> <ul> <li>32 steady-state measurements where the following factors were changed: ventilation mode (indoor and outdoor air curtain), solar irradiance (0, 400, 600, and 800 Wm<sup>-2</sup>), outdoor chamber temperature (10, 20, 30, and 40 ℃), cavity depth (20, 30, 40 and 60 cm) and venetian blinds position (no blinds, closed blinds, θ=45 º, and open blinds) [file names: ‘Taguchi_4Lx4F_L16_I-I.csv’ and ‘Taguchi 4Lx4F_L16_O-O.csv’],</li> <li>Dynamic profile measurements corresponding to a typical hot summer day [Dynamic_profile_measurements.csv] and</li> <li>Calibration data [Callibration.csv].</li> </ul> <p>Any inquires on the experimental data<em> can be sent </em>to: aleksandar.jankovic@ntnu.no</p>
Advection datasets from "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"
<p>Advection datasets from the paper:<br> Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics (https://doi.org/10.1063/5.0097679)</p> <p>The datasets are:<br> - AdvBox<br> - AdvInBox<br> - AdvTaylor<br> - AdvCircle<br> - AdvCircleAng<br> - AdvSquare<br> - AdvEllipseH<br> - AdvEllipseV<br> - AdvSpline<br> - AdvSquareAndCircle<br> - Adv3Circles</p> <p>Check the "README.txt" file for information on how the simulations are organised. The features of each dataset and how they were generated are explained in the journal publication.</p> <p> </p> <p>To cite these datasets, use the following reference:</p> <p>Mario Lino, Stathi Fotiadis, Anil A. Bharath, and Chris Cantwell. "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics". Physics of Fluids, 34 (2022).</p> <pre><code>@article{lino2022multi, author = {Lino, Mario and Fotiadis, Stathi and Bharath, Anil A. and Cantwell, Chris}, title = {{Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics}}, journal = {Physics of Fluids}, volume = {34}, year = {2022}, url = {https://doi.org/10.1063/5.0097679}, }</code></pre> <p><br> </p>
Butcher's tableaux of the optimized explicit Runge-Kutta schemes for high-order collocated discontinuous Galerkin methods for compressible fluid dynamics
<p>This folder contains the Butcher's tableaux of the optimized explicit Runge-Kutta schemes for high-order collocated discontinuous Galerkin methods for compressible fluid dynamics presented in Al Jahdali et al., "Optimized explicit Runge--Kutta schemes for high-order collocated discontinuous Galerkin methods for compressible fluid dynamics," Computers & Mathematics with Applications, 2022.</p> <p>Specifically,</p> <p><a href="https://zenodo.org/api/files/754318a5-0881-4252-9059-086da4607b49/Butcher_coefficients_ADV.txt">Butcher_coefficients_ADV.txt</a> contains the Butcher's tableaux of the explicit Runge-Kutta schemes optimized using the spectra of the 2D advection equation.</p> <p><a href="https://zenodo.org/api/files/754318a5-0881-4252-9059-086da4607b49/Butcher_coefficients_IEV.txt">Butcher_coefficients_IEV.txt</a> contains the Butcher's tableaux of the explicit Runge-Kutta schemes optimized using the spectra of the isentropic vortex propagation for the compressible Euler equations.</p> <p> </p> <p> </p> <p> </p>
Dataset for "A computational fluid dynamics—Population balance equation approach for evaporating cough droplets transport"
<p>Dataset for figures and tables of the article "A computational fluid dynamics—Population balance equation approach for evaporating cough droplets transport" submitted to "International Journal of Multiphase Flow".</p>
Dataset for 'Comment on "Glassy Dynamics in Chiral Fluids" '
<p>Data sets from which figures of Dataset for 'Comment on "Glassy Dynamics in Chiral Fluids" ' were obtained, along with python code for produing the figures (in file 'correlations_matrix.ipynb').</p>
FIGURE 12 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 12. Simulated Nautilus data plotted alongside live Nautilus behavior data from Niel and Askew (2018).
FIGURE 8 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 8. Plot of the coefficient of drag versus Reynolds number for each of the 10 morphotypes in this study. Drag coefficient and Reynolds number were calculated following the equations of Jacobs (1992). Only shells that had a uniform diameter of approx. 5 cm from aperture to venter are shown.
FIGURE 7 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 7. Plot of drag force versus velocity for each of the 10 different morphotypes used in this study. Only shells that had a uniform diameter of approx. 5 cm from aperture to venter are shown.
FIGURE 5 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 5. Coefficient of drag results from Scheme 1 (green) and Scheme 3 (blue) plotted against Re compared against the data from Jacobs (1992; black). Comparisons shown are for Sphenodiscus (left) and Oppelia (right).
FIGURE 3 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 3. An illustration of the computational domain of the simulation. The model target (an ammonoid in this case) is shown as a circle. Each arrow indicates a distance from the shell to a target face of the computational domain. These arrows represent the straight-line distance between the nearest edge of the shell (not the shell's midpoint) and the corresponding wall as per the methods of Shiino, Kuwazuru, and Yoshikawa (2009). Dimensions in the figured example correspond to those of Scheme 3 (see Table 1)
FIGURE 4 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 4. Hemisphere simulation data plotted as velocity versus % difference from the literature baseline (Blevins 1984). Velocities shown are within a range in which the drag coefficient of a hemisphere is relatively stable around a value of 1.17 (Blevins, 1984). The drag values used to derive this plot are given in Appendix 3.
FIGURE 1 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 1. An outline of the workflow from model creation to completed simulation. Boxes are colored based on the general process they are included in: Case generation (blue), Mesh generation (purple), and numerical set-up (green). Two tracks are shown for case generation: one in which a model is created in blender from measurement data (below the dotted line) and the other where the model is created using a Structure from Motion technique such as laser scanning or photogrammetry (above the dotted line). Software used in each process is noted in "()" outside its respective step.
FIGURE 11 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 11. Plots of pressure overlain with water velocity vectors for the Serpenticone and Oxycone shells at both 15 cm/s (A) and 5 cm/s (B) inlet velocities. At 15 cm/s the flow around the Serpenticone shell is more chaotic and there is a buildup of pressure at around the trailing coils compared to the Oxycone shell. This difference mostly disappears at 5 cm/s.
FIGURE 9 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 9. Water velocity around the Sphenodiscus shell at inlet velocities of 15 cm/s (A) and 5 cm/s (B). Areas of slow water velocity caused by viscous interactions are larger to the sides and immediately behind the shell at the lower velocity because water is less readily shed.
Cloud-Repro: Reproducible Workflow on a Public Cloud for Computational Fluid Dynamics
<p>In a new effort to make our research transparent and reproducible by others, we developed a workflow to run computational studies on a public cloud. It uses Docker containers to create an image of the application software stack. We also adopt several tools that facilitate creating and managing virtual machines on compute nodes and submitting jobs to these nodes. The configuration files for these tools are part of an expanded "reproducibility package" that includes workflow definitions for cloud computing, in addition to input files and instructions. This facilitates re-creating the cloud environment to re-run the computations under the same conditions.</p> <p>The present Zenodo dataset contains all secondary data required to reproduce the figures of the manuscript ("Reproducible Workflow on a Public Cloud for Computational Fluid Dynamics") without running the CFD simulations again.</p>
Fig. 1 in Hydrodynamic performance of psammosteids: new insights from computational fluid dynamics simulations
Fig. 1. Box-shaped flow domain, mesh and coordinate system used for computational fluid dynamics (A ); A , enlargement view on the mesh.
Fig. 2 in Hydrodynamic performance of psammosteids: new insights from computational fluid dynamics simulations
Fig. 2. Distribution of pressure on the fish bodies at flow velocity 1.5 ms-1, in anterior (A) and lateral (A) views.
Fig. 4. Vorticity patterns using Q-criterion for 0 in Hydrodynamic performance of psammosteids: new insights from computational fluid dynamics simulations
Fig. 4. Vorticity patterns using Q-criterion for 0 angles of attack and flow velocity 1.5 ms-1 in Errivaspis (A), Guerichosteus (B), and Tartuosteus (C). Models displayed in left lateral (A 1 –C 1) and top (A 2 –C 2) views. Iso-vorticity surface is colored by the magnitude of velocity.
Micro-urban environment experimental dataset to validate performance of different Computational Fluid Dynamics methodologies.
<p><span><span>This dataset enclosed wind 3D geolocated wind flow and air concentrations </span><span>5-minutal </span><span>data </span><span>collected in </span><span>El Prat del Llobregat (Spain) </span><span>between January and August 2022 in the context of the experiment 1012-ibam of the FF4EuroHPC European project. The intention of this dataset is to provide a </span><span>resource to do performance benchmark of micro-urban chemical – dispersion models to assess their performance</span><span>. To do so, we enclose experimental data collected by </span><span>Bettair</span><span> Mk2 Series Air quality monitors, 2 Air Quality Monitoring stations equipped with reference instruments f</span><span>rom “La </span><span>Xarxa</span><span> de </span><span>Vigilància</span> <span>i</span> <span>Previsió</span><span> de la </span><span>Contaminació</span> <span>Atmosfèrica</span><span> (XVPCA)”</span><span>, and different data from the repository of the ECMWF Era-5 land and CAMS. We also provide the </span><span>3D watertight geometry model of the </span><span>el</span><span> Prat de Llobregat (Spain) in step file format</span><span> (layout from 2020)</span><span>.<br></span></span></p>
NsCircle datasets from "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"
<p>Datasets with the simulations of the incompressible flow around an elliptical as described by the incompressible Navier-Stokes equations. These simulations were used to train and test the MuS-GNN models in the paper:<br> Multi-scale rotation-equivariant graph neural networks for<br> unsteady Eulerian fluid dynamics (https://doi.org/10.1063/5.0097679)</p> <p>The datasets are:<br> - train/NsEllipse<br> - test/NsEllipseLowRe<br> - test/NsEllipseHighRe<br> - test/NsEllipseThin<br> - test/NsEllipseThick<br> - test/NsEllipseNarrow<br> - test/NsEllipseWide<br> - test/NsEllipseAoA</p> <p> </p> <p> </p> <p>To cite these datasets, use the following reference:</p> <p>Mario Lino, Stathi Fotiadis, Anil A. Bharath, and Chris Cantwell. "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics". Physics of Fluids, 34 (2022).</p> <p>@article{lino2022multi,<br> author = {Lino, Mario and Fotiadis, Stathi and Bharath, Anil A. and Cantwell, Chris},<br> title = {{Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics}},<br> journal = {Physics of Fluids},<br> volume = {34},<br> year = {2022},<br> url = {https://doi.org/10.1063/5.0097679},<br> }<br> </p>
ScienceDex guides
Understand access before you commit
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.