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79 results for “CFD”
OpenFOAM cases of the paper "Development and validation of an open-source CFD model for the efficiency assessment of data centers"
<p>This dataset contains the<em> underling data</em> for the paper "Development and validation of an open-source CFD model for the efficiency assessment of data centers”, submitted for the consideration and open review in Open Research Europe (ORE).</p> <p><strong>Validation1.tar.xz:</strong> OpenFOAM files and scripts for the simulation of flow and thermal structures in an enclosed environment (Wang and Chen, 2009).</p> <p><em>Wang, Miao; Chen, Qingyan (2009). Assessment of Various Turbulence Models for Transitional Flows in an Enclosed Environment (RP-1271). HVAC&R Research, 15(6), 1099–1119. doi:10.1080/10789669.2009.10390881</em></p> <p><strong>Validation2-kOmegaSSTModel.tar.xz:</strong> OpenFOAM files and scripts for the simulation of forced convection in a room (Zhang et al. 2007) using k-omega SST turbulence model. </p> <p><em>Zhao Zhang, Wei Zhang, Zhiqiang John Zhai & Qingyan Yan Chen (2007) Evaluation of Various Turbulence Models in Predicting Airflow and Turbulence in Enclosed Environments by CFD: Part 2—Comparison with Experimental Data from Literature, HVAC&R Research, 13:6, 871-886, DOI: 10.1080/10789669.2007.10391460</em></p> <p><strong>Validation2-RNGkEpsilonModel.tar.xz:</strong> OpenFOAM files and scripts for the simulation of forced convection in a room (Zhang et al. 2007) using RNG k-epsilon turbulence model. </p> <p><em>Zhao Zhang, Wei Zhang, Zhiqiang John Zhai & Qingyan Yan Chen (2007) Evaluation of Various Turbulence Models in Predicting Airflow and Turbulence in Enclosed Environments by CFD: Part 2—Comparison with Experimental Data from Literature, HVAC&R Research, 13:6, 871-886, DOI: 10.1080/10789669.2007.10391460</em></p> <p><strong>Validation3.tar.xz:</strong> OpenFOAM files and scripts for the simulation of strong natural convection in a model fire room (Murakami et al. 1995).</p> <p><em>Murakami, S., S. Kato, and R. Yoshie. 1995. Measurement of turbulence statistics in a model fire room by LDV. ASHRAE Transactions 101(2):287–301.</em></p> <p><strong>Validation4.tar.xz:</strong> OpenFOAM files and scripts for the simulation of thermal distribution in an open-aisle data center (Abdelmaksoud et al. 2013).</p> <p><em>W.A. Abdelmaksoud, T.Q. Dang, H. Ezzat Khalifa, R.R. Schmidt Improved computational fluid dynamics model for open-aisle air-cooled data center simulations J. Electron. Packag., 135 (2013), pp. 030901-30913</em></p> <p><strong>Results_Validation1.tar.xz:</strong> Simulation results of the Validation case 1.</p> <p><strong>Results_Validation2.tar.xz:</strong> Simulation results of the Validation case 2.</p> <p><strong>Results_Validation3.tar.xz:</strong> Simulation results of the Validation case 3.</p> <p><strong>Results_Validation4.tar.xz:</strong> Simulation results of the Validation case 4.</p> <p><strong>layout.csv:</strong> Input file for the Validation case 4.</p>
CFD simulation and measurements of effect of wind on non-catching rain gauge
<p>Simulation_dataset file shows the results of a CFD simulation of the measurements of a Thies laser precipitation monitor under different conditions of wind.</p> <p>Wind_tunnel_dataset shows the results of the model validation using an actual wind tunnel.</p>
CFD airfoil simulations
<p>Arfoil CFD simulation dataset. Each example has the flow at the back of the airfoil. The ground truth is the drag and lift forces on the airfoil. The aim of this dataset is to compare feature extraction techniques from flow fields for machine learning.</p>
OpenFOAM cases of the paper "CFD modeling of pressure drop through an OCP server for data center applications"
<p>This dataset contains the<em> underling data</em> for the paper "<em>CFD modeling of pressure drop through an OCP server for data center applications</em>” in Energies Journal.</p> <p>Numerical simulations are performed using open-source CFD code OpenFOAM. Features of the numerical model described in the paper can be summarized as:</p> <ul> <li> A hexahedra (hex) and split-hex mesh was created using <em>snappyHexMesh</em> utility from the STL (Stereolithography) model of the server</li> <li><em>Allrun</em> script runs steady-state simulations for different inlet flow rates given in the <em>flowrates</em> file.</li> <li><em>k-omegaSST</em> turbulence closure model is used</li> <li>Convective and diffusive fluxes are computed using second-order accurate numerical schemes.</li> <li>Resultant matrices are solved using <em>GAMG</em> and <em>smoothSolver</em> methods.</li> </ul> <p>New libraries are developed for the detection of whether the solution reaches state-state and for the calculation of average pressures at the inlet and outlet of the server. These libraries need to be download via the following link and compiled before the cases run: </p> <p><a href="https://github.com/DSTECHNO/OCPFoam">https://github.com/DSTECHNO/OCPFoam</a></p> <p><strong>active_Server.tar.gz:</strong> OpenFOAM files and scripts for the steady-state simulation of turbulent flow flow over Leopard V3.1 model of OCP server neglecting fan blades.</p> <p><strong>inactive_Server.tar.gz:</strong> OpenFOAM files and scripts for the steady-state simulation of turbulent flow over Leopard V3.1 model of OCP server considering fan blades.</p> <p><strong>Results_active_Server.tar.gz:</strong> Simulation results for active server.</p> <p><strong>Results_inactiveServer.tar.gz: </strong>Simulation results for inactive server.</p>
Ice Throw from Wind Turbines: Experimental Data, 6DOF Model, CFD results, 3D Scans
<p>Compiled data and code from the Eisball Project (funded by the Austrian Research Promotion Agency FFG, project number 865060)</p> <p> </p> <p> </p> <p>6DOF_model_octave.zip - reference implementation of the six-degree-of-freedom model in MathML (Octave or MATLAB)</p> <p>experimental_data.csv - Experimental Data from dropping artificial ice fragments from wind turbines, recording drop distance and direction, details in experimental_data_column_description.txt</p> <p>???_forces_and_moments.csv - forces and moments tables for the use in the 6DOF model, specific per specimen type</p> <p> </p> <p>Data was first published in Nov 2021 at https://boku.ac.at/wau/risk/abgeschlossene-projekte/eisball-1 (may not persist)</p>
Simulation data and scripts for CFD-DEM simulation of saturated bi-disperse granular flows
<ul> <li>Data set 1 - contains the raw data required to replicate and validate all plots in the main article. </li> <li>Sample case - a .zip file which includes codes which are needed to simulate a CFD-DEM case of a steady granular flow in water with cyclic boundaries in the stream wise direction. Also enclosed is a ReadMe.txt file detailing the implementation instructions for both Esys particle and OpenFOAM codes. Download links for Esys particle and OpenFOAM are also included</li> <li>Geo file generator - a .zip file which includes Esys particle codes that can be used to generate a .geo file specifying the initial position of the particles used in the test simulations. A ReadMe.txt file is enclosed with more detailed implementation instructions.</li> </ul>
The supplemental data for the paper: "Methodology of generation of CFD meshes and 4D shape reconstruction of coronary arteries from patient-specific dynamic CT"
<p>The supplemental data for the paper: "Methodology of generation of CFD meshes and 4D shape reconstruction of coronary arteries from patient-specific dynamic CT"</p><p>A video file (minimum play resolution is HD to see the mesh) showing the movement of the LCA throughout the heart cycle and .STL files for 10--100% (increment of 10\%) of the heart cycle phase.</p>
Rotor37: a 3D CFD RANS dataset, under geometrical variations of a compressor blade
<p>This dataset contains 3D CFD RANS solutions, under geometrical variations of a compressor blade.</p> <p>A Description is provided in <a href="https://arxiv.org/pdf/2305.12871.pdf">2305.12871.pdf (arxiv.org)</a> Sections 4.1 and Appendix A.1.</p> <p>The file format is PLAID, see the <a href="https://plaid-lib.readthedocs.io/">plaid documentation</a>.</p> <p>The variablity in the samples are 2 input scalars and the geometry (mesh). Outputs of interest are 3 scalars and 3 fields.</p> <p>Eight nested training sets of sizes 8 to 1000 are provided, with complete input-output data. A testing set of size 200 is provided, for which outputs are not provided. </p> <p> </p> <p>Tips to access the data:</p> <p>After decompressing the downloaded file:</p> <p>from plaid.containers.dataset import Dataset<br>from plaid.problem_definition import ProblemDefinition</p> <p>dataset = Dataset()<br>problem = ProblemDefinition()</p> <p>problem._load_from_dir_(os.path.join(/path/to/data,'problem_definition'))<br>dataset._load_from_dir_(os.path.join(/path/to/data,'dataset'), verbose = True)</p> <p>print("problem =", problem)<br>print("dataset =", dataset)</p> <p>sample = dataset[0]<br>print("sample =", sample)</p> <p>for fn in sample.get_field_names():<br> print(f"{fn} =", sample.get_field(fn))<br>for sn in sample.get_scalar_names():<br> print(f"{sn} =", sample.get_scalar(sn))</p> <p>print("nodes =", sample.get_nodes())<br>print("elements =", sample.get_elements())</p>
CFD and Testbench Measurements of the Deutz TCD12 V6 Diesel Engine
<p><strong>Motivation</strong><br> The data set was used to fit and validate an air path model of the TCD12 V6 diesel engine. </p> <p><strong>Research article</strong><br> Thiel, Michael, and Bernd Tibken. "A diesel engine air path model with a WG and an ETV for exhaust temperature controller design and embedded control." <em>Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering</em> (2022): 09544070211069216.</p> <p><strong>Description</strong></p> <ul> <li><strong>airpath_tcd12_true.pdf</strong>: Overview of the engine</li> <li><strong>airpath_tcd12.pdf: </strong>Overview of labels used</li> <li><strong>gt_doe.csv:</strong> Stationary data (CFD simulation)</li> <li><strong>gt_nrtc.csv:</strong> A section of NRTC test cycle (CFD simulation)</li> <li><strong>tb_nrtc.csv:</strong> A section of the NRTC (Egnine test bench)</li> </ul> <p> </p>
Figure 4. CFD simulation results for Models A in Drag of suction cup tags on swimming animals: Modeling and measurement
Figure 4. CFD simulation results for Models A (panels A and C) and B (panels B and D) in steady 5.6 m/s flow. The blue, yellow, and green regions are areas of reduced flow speed that generate forces on the tags. The upper panels show the flow speed over a horizontal cross-section at the tag midline. The lower panels show flow speed over a vertical cross-section at the centerline of the tag. The improved flow around Model B is evident in the smaller magnitude of blue coloration in the wake behind the tag.
Companion for "Design, Implementation and Performance Analysis of a CFD task-based Application for Heterogeneous CPU/GPU Resources"
<p>This is the companion data for the VECPAR2018 submission paper entitled: Design, Implementation and Performance Analysis of a CFD task-based Application for Heterogeneous CPU/GPU Resources by Lucas Leandro Nesi, Lucas Mello Schnorr, and Philippe Olivier Alexandre Navaux. All the data, source code, and images generation scripts used in the paper are present here.</p>
CFD dataset for CityTransformer
<p>This dataset contains the simulated plume concentration data in the urban area for CityTransformer, where all the data are created by 3D Large Eddy Simulations dedicated to the Oklahoma city. A single netcdf file in the dataset consists of the 2D image-like data on the ground (e.g building shapes, source locations, plume concentration) and 1D time-series data (e.g flow velocities, plume concentration) from a few monitoring stations. CityTransformer is designed to give the emergence contaminant dispersion prediction in a realistic urban area based on the deep learning model.</p> <p>We simulate a 4 km x 4 km x 2.5 km area of Oklahoma city under the uniform flow conditions. In each simulation, 25 tracer particles are released from random locations in the city center. We consider three distinct 1.5 hour long time windows for data augmentation, wherein we sampled 1-hour long time varying 1D data and time-averaged 2D ground data for the next 30-minutes. The details of the dataset is described in the <a href="https://github.com/yasahi-hpc/CityTransformer">Github page</a>.</p>
Skydiving Salamander CFD
<p>The following is a written description of all data and files related to the manuscript submitted for publication titled: How Salamanders Skydive: Characterizing Lift, Drag, and Pressure Differences across Wandering Salamanders (Aneides vagrans) with Computational Fluid Dynamics. This data was collected by Christian E. Brown and Alexander M. Kirk at the University of South Florida using the Access 3D Lab between January and August of 2022.</p>
Server room CFD analyses results
<p>Files included in this dataset contain transient records of the most important variables of results of 9 scenarios of Poznan Supercomputing and Networking Center testbed CFD analysis.</p> <p>Modeled testbed included server room equipped with two heterogeneously occupied racks (with each server modeled separately) and one roof-mounted cooling device. Setup was examined virtually for three servers power usage scenarios (50%, 75% and 100% of maximum value). For major part of active servers the power usage was changed respectively in each scenario, while some of them preserved daily-averaged values (inactive devices are also included). For each power usage level also three different air conditioning temperature setups (24, 27 and 30 Celsius degrees) were tested. The most relevant information about the model can be found below:</p> <p> </p> <ul> <li>server room dimensions along x, y and z axis (with one of the corners placed in the origin): (4.86, 4.00, 3.00) [m]</li> <li>racks dimensions: (0.6,1.18,2.1),(0.8,1.18,2.2) [m]</li> <li>racks position (translation from 0,0,0): (1.4,1.95,0), (2.08,1.95,0) [m]</li> <li>first rack equipment (position [-], size [-], airflow [m<sup>3</sup>/s], power usage (50%/75%/100% scenario) [W], flow orientation [-]): <ul> <li>0, plug, -, -/-/-, -,</li> <li>1, plug, -, -/-/-, -,</li> <li>2, 4U, 0.0, 0.0/0.0/0.0, y,</li> <li>6, 2U, 0.0, 0.0/0.0/0.0, y,</li> <li>9, plug, -, -/-/-, -,</li> <li>10, 2U, 0.05, 230/345/460, y,</li> <li>20, 2U, 0.025, 90/90/90, y,</li> <li>22, 2U, 0.05, 450/675/900, y,</li> <li>24, 4U, 0.12, 1000/1500/2000, y,</li> <li>28, 2U, 0.05, 230/345/460, y,</li> <li>30, 4U, 0.12, 1000/1500/2000, y,</li> <li>34, 2U, 0.05, 400/600/800, y,</li> <li>36, 2U, 0.05, 400/600/800, y,</li> </ul> </li> <li>second rack equipment (position [-], size [-], airflow [m<sup>3</sup>/s], power usage (50%/75%/100% scenario) [W], flow orientation [-]): <ul> <li>0, 3U, 0.0, 0.0/0.0/0.0, y,</li> <li>3, 3U, 0.0, 0.0/0.0/0.0, y,</li> <li>6, plug, -, -/-/-, -,</li> <li>7, plug, -, -/-/-, -,</li> <li>8, 3U, 0.0, 0.0/0.0/0.0, y,</li> <li>11, 2U, 0.0, 0.0/0.0/0.0, y,</li> <li>13, 2U, 0.025, 69/69/69, x,</li> <li>15, 3U, 0.0/0.0/0.0, 0.0/0.0/0.0, y,</li> <li>18, 3U, 0.15, 1350/2025/2700, x,</li> <li>22, 2U, 0.05, 161/161/161, y,</li> <li>24, 2U, 0.025, 69/69/69, x,</li> <li>26, 2U, 0.0, 0.0/0.0/0.0, y,</li> <li>28, 2U, 0.025, 23/23/23, x,</li> <li>30, 2U, 0.05, 258.5/258.5/258.5, y,</li> <li>34, plug, -, -/-/-, -,</li> <li>35, plug, -, -/-/-, -,</li> <li>36, plug, -, -/-/-, -,</li> <li>37, plug, -, -/-/-, -,</li> <li>38, plug, -, -/-/-, -,</li> <li>40, plug, -, -/-/-, -,</li> <li>41, plug, -, -/-/-, -,</li> <li>air conditioner max. cooling capacity: 10 [kW]</li> <li>air conditioner airflow: 0.533 [m3/s]</li> <li>air conditioner inlet center: (3.76, 1.3, 3.0)</li> <li>air conditioner outlet center: (3.76, 2.8, 3.0)</li> <li>ventilation airflow: 0.0252 [m3/s]</li> <li>ventilation inlet center: (2.8125, 3.6475, 3.0)</li> <li>ventilation outlet center: (2.95, 0.605, 3.0)</li> </ul> </li> <li>domain cuboid cavities (start point, end point): <ul> <li>(0, 0, 3.0), (0.72, 4.1, 3.27),</li> <li>(0.32, 0, 3.27), (0.72, 3.78, 3.5)</li> </ul> </li> <li>domain cuboid obstacles (start point, end point): <ul> <li>(3.61, 0, 0), (4.86, 0.44, 1.89)</li> <li>(0.32, 3.78, 0), (0.72, 4.1, 3.0)</li> </ul> </li> </ul> <p> </p> <p>The dataset contains records of below variables:</p> <ul> <li>temperature from four virtual probes placed at the same locations as real sensors in the testbed: <ul> <li> (1.8, 2.12, 1.42) - close to inlet of a server from the first rack</li> <li> (4.8, 1.65, 1.5) - close to air conditioner wall-mounted control panel</li> <li> (2.5, 2.09, 0.95) - close to inlet of a server from the second rack</li> <li> (2.8, 0.65, 0.75) - probe distanced from both racks and air conditioner, imitating "neutral" point</li> </ul> </li> <li>surface-averaged temperature at inlet of each server</li> <li>surface-averaged temperature at outlet of each server</li> <li>surface-averaged temperature at air conditioner inlet</li> <li>surface-averaged temperature at air conditioner outlet</li> </ul>
CFD analysis of natural convection cooling of the in-vessel components during a shutdown of the EU DEMO fusion reactor (dataset)
<p>Simulation file for the publication "CFD analysis of natural convection cooling of the in-vessel components during a shutdown of the EU DEMO fusion reactor" submitted to <em>Fusion Engineering and Design</em>.</p> <p>Work carried out within the framework of the EUROfusion Consortium.</p> <p>Needs Simcenter STAR-CCM+ v2019.3 to be viewed.</p>
Simulation cases of a lab-scale wet-operated stirred media mill using coupled CFD-DEM
<p>Simulation cases described in the article, "Coupled CFD-DEM simulation of pin-type wet stirred media mills using immersed boundary approach and hydrodynamic lubrication force", DOI: <a href="https://doi.org/10.1016/j.powtec.2024.120060" rel="nofollow">https://doi.org/10.1016/j.powtec.2024.120060</a></p> <p><strong>Pre-requisites:</strong> LIGGGHTS, OpenFOAM-6, cfdemCoupling, and their corresponding dependencies, Python (>3.6)</p> <p>*The versions of simulation softwares used in the simulation cases are taken from Institute for Particle Technology's (iPAT) GitLab repository: <a href="https://git.rz.tu-bs.de/partikeltechnik/" rel="nofollow">https://git.rz.tu-bs.de/partikeltechnik/</a></p> <p>To run the simulations in this repository, one should first install the pre-requisites i.e., LIGGGHTS, OpenFOAM-6 and cfdemCoupling. The repositiries can be found at Institute for Particle Technology's GitLab (<a href="https://git.rz.tu-bs.de/partikeltechnik/" rel="nofollow">https://git.rz.tu-bs.de/partikeltechnik/</a>) if not, they shall be requested.</p> <p>Running the simulations in the repositories includes, generation of the cases in "Base_Cases_Init", using the "generateCases.py" file (Python3), then run the "variables_Modify.py" file. Running of the "jobfile_Modify.py" and "jrun.py", sequentially, will submit the simulations to a HPC cluster. After the successful run of these simulations, the cases in the folders "Base_Cases_Stable" and "Base_Cases_Stable_Lubrication" can be launched in the same manner as described above, i.e., sequentially running "generateCases.py", "variables_Modify.py", "jobfile_Modify.py" and "jrun.py" (one needs to check if the corresponding restart files are existing in the Base_Cases_Stable*/Base_Case_Stable/Restart folder, which are generated from the "Base_Cases_Init" runs). Following this, the cases in "Base_Cases_Run_800_um", "Base_Cases_Run_1100_um", and "Base_Cases_Run_Lubrication" can be run using the same method as described above (one needs to check if the corresponding restart files are existing in the Base_Cases_Run*/Base_Case_Run/Restart folder, which are generated from the "Base_Cases_Stable" runs). After successfully running of the simulations the python file "generateAndRunPostFiles.py", in each of the corresponding "Base_Cases_Run_800_um", "Base_Cases_Run_1100_um", and "Base_Cases_Run_Lubrication" folders should be run.</p> <p> </p> <p> </p> <p> </p> <p><strong>Description:</strong> This repository provides the simulation cases to generate and run the simulation cases of the "stirred media mill" (MiniCeR). The simulations are setup to couple the CFD and DEM via two-way coupling and the corresponding files in the "Run" folder contain the post-processing scripts to extract the "collision/stress energies" and assemble them into a "collision/stress energy distribution". The simulations are setup in three stages, namely, "Init", "Stable", and "Run". The combinations of operating settings can be easily modified and the respective cases can be generated using the python scripts in the corresponding repositories. The scripts to run the simulations on the HPC-cluster systems are also added.</p> <p><strong><em>a. Init:</em></strong> This stage is to initialize the system with the particles. Three insertion faces are used to generate and insert the required number of particles (calculated according to their size and filling degree) into the system. The "base case" folder contains the necessary DEM scripts of the case setup and the required CAD (geometry) files. The python script "generateCases.py" generates the requested simulation cases according to the specified operating settings. It uses the help of "MakeCases.sh". The "variables_Modify.py" file modifies the variables in the generated folders of the simulation cases to alter the operation setting values. The "jobfile_Modify.py", and the "jrun.py" are used to modify the cluster job files and run the submit the simulation jobs onto the cluster, respectively.</p> <p><strong><em>b. Stable:</em></strong> This is the first stage couples the CFD and DEM. The restart files generated in the "Init" stage are used to start the coupling and run for a specified time. It follows the similar system as init, i.e., to generate the cases and modify the variables, but with additional generation and modifications in the CFD folder i.e., the mesh generation, etc. The simulations are launched in the same way as described above and the corresponding restart files are extracted.</p> <p><strong><em>c. Run:</em></strong> This second stage of the coupling of CFD and DEM launches the srabilized system and extracts the collision energies and stores them in ".txt" files which are postprocessed later to assemble the stress energy distribution. The post-processing to extract the stress energy distribution is done using the "Stress_Energy_Calculation.py" and "generateAndRunPostFiles.py", which generate corresponding folders of post-processing in each of the corresponding case folders.</p>
KMC Models and CFD Simulations for Catalytic CO Oxidation over RuO2
<p>The large tar.gz file (~15GB) has the CFD Data Set for CFD Simulations for Catalytic CO Oxidation over RuO2 associated with the publication: doi: 10.1021/acscatal.8b00713 , https://pubs.acs.org/doi/abs/10.1021/acscatal.8b00713</p> <p>The smaller zip file(s) have the KMC model creation files, which are actually from this publication: https://doi.org/10.1088/1361-648X/aacb6d https://iopscience.iop.org/article/10.1088/1361-648X/aacb6d</p> <p>For the 110 KMC file, it must be compiled using the lat_int backend of kmos. The xml file generated takes less than an hour, though the exporting (compiling) may take 5 to 10 hours. With lateral interactions, the 110 xml file is >25MB. Without lateral interactions, the xml file is very small ( << 1 MB). Setting the "upToDistance" to 0 for each reaction in the python file called CO_O_RuO2_110.py will create a 110 model without lateral interactions (currently the "upToDistance" is set at 1 for each reaction, and thus includes nearest neighbor interactions). How to compile the model requires reading the documentation for the KMC software. The final syntax is " kmos export -b lat_int CO_O_RuO2_110.xml "</p> <p>For the 111 KMC file, it should be compiled with the default ( local_smart ) backend of kmos. The lateral interactions are already included and cannot be easily turned off. The resulting files are small. How to compile the model requires reading the documentation for the KMC software. The final syntax is " kmos export -b local_smart CO_O_RuO2_111.xml "</p> <p>Primitive example KMC runfiles are included, which would need to be run in the compiled KMC model's directories, though current conventions of kmos simulations use more advanced runfiles.</p> <p> </p>
Ship Plume Dispersion in Marseille Port: A High-Resolution CFD Visualization
<p><strong><u>Description</u></strong></p> <p>This video visualizes the results of a high-resolution Computational Fluid Dynamics (CFD) model applied to study ship plume dispersion in the passenger port of Marseille, France. The video focuses on a scenario where a small cruise/pleasure ship docked at the port turns on its main engines in preparation for departure, considering seaborn prevailing wind conditions.</p> <p>The visualization concentrates on two areas. The first is the area around La Major Cathedral, which is close to the waterfront. The second is a street canyon approximately 650 meters downwind of the ship's location. Due to the local urban layout, the plume becomes entrapped within this distant street canyon. Here, the spatial distribution of pollutants can vary even between neighboring floors of a single building. Finally, a rendered video of the plume dispersion is presented, where the urban landscape is visualized to make the dispersion more comprehensible for non-technical and non-scientific audiences.</p> <p>This video was previously published on YouTube at the following link: <a href="https://www.youtube.com/watch?v=HwLFTj1D4LU&ab_channel=SCIPPERProject" target="_blank" rel="noopener">YouTube</a>. </p> <p>All data used to create this video were made available through the EU Horizon 2020 project <a href="https://www.scipper-project.eu/">SCIPPER</a>.</p> <p>The SCIPPER project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement <a href="https://doi.org/10.3030/814893">Nr. 814893</a>. </p>
CFD dataset for AMR-Net
<p>This dataset contains the two dimensional fluid simulation data for AMR-Net.</p> <p>The inputs of simulations are signed distance functions (SDFs) and the outputs are 2D flow fields u and v. In each simulation, 1-5 objects are placed randomly at the center of the computational domain. The objects are randomly chosen from circles, ellipses, rectangles and rounded rectangles. Each hdf5 file includes the pair of input and output data. The details of the dataset is described in the <a href="https://github.com/yasahi-hpc/AMRNet">Github page</a>.</p>
Dataset for "DEM-CFD analysis of swelling behaviours of binary particle systems with a microscopic diffusion model"
<p>This dataset contains both numerical simulation data and experimental data describing the swelling behaviour of binary particle systems. Detailed study on the swelling behaviour is presented in a journal paper entitled "DEM-CFD analysis of swelling behaviours of binary particle systems with a microscopic diffusion mode". We provide all the essential data in a single excel file with the description below:</p> <p>Dataset 1- Evolution of the height of the particle bed obtained from experiments and simulations (used in Fig.6 of the associated paper): Experimental data for the measured hight and associated standard deviation at various time, as well as the numerical data.</p> <p>Dataset 2 - Evolutions of the normalised average radius r/r_0 (used in Fig.7a) and the coefficient of variation CV (used in Fig.7b) for SAP particles obtained from DEM-CFD simulations.</p> <p>Dataset 3 - Radial distribution Functions (RDFs) for (Fig.9a) SAP particles and (Fig.9b) POM particles at different time instants.</p> <p>Dataset 4 - Positions of POM particles projected into the x-y plane at different time instants (the red dots are particle centres), (a) t = 0 min; (b) t = 3 min; (c) t = 6 min; (d) t = 10 min.</p> <p>Dataset 5 - Evolution of Lacey mixing index for the binary particles during the swelling process.</p> <p>Dataset 6 - Average normal stress distribution of particles in x-y and x-z planes at t = 2 min.</p> <p>DataSet 7 - Velocity vectors of particles projected in x-z planes at different time instants: (a) t = 1 min; (b) t= 2 min (Data for Fig.13)</p> <p>Dataset 8 - Positions of POM particles projected into x-y plane at time t= 10 min (the red dots are particle centres) with different coefficient of frictions (a) μ=0.1; (b) μ=0.2; (c) μ=0.3; (d) μ=0.4; (e) μ=0.5.(Data for Fig.15)</p> <p>Dataset 9 - RDFs for POM particles at time t= 10 min with different coefficients of friction.(Data for Fig.16)</p>
ScienceDex guides
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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.