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47 results for “fluid simulation”
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>
Simulations of Rising Air Bubbles in Viscoelastic Fluids
<p>Videos, plots and the underlying data, generated from simulations of rising air bubbles in viscoelastic fluids for varying relaxation times and bubble volumes. The underlying model for the viscoelastic fluid is the Giesekus model, which was discretized with the fully implicit log-conformation approach. The free surface is modeled using an interface-tracking method. For more details cf. Knechtges, P. <em>Simulation of Viscoelastic Free-Surface Flows</em> PhD thesis (RWTH Aachen, 2018).</p>
Data for Fluid simulations accelerated with 16 bits: Approaching 4x speedup on A64FX
<p>Dataset for</p> <p>M Kloewer, S Hatfield, M Croci, PD Dueben and TN Palmer, 2021. Fluid simulations accelerated with 16 bits: Approaching 4x speedup on A64FX by squeezing ShallowWaters.jl into Float16, in review.</p> <p>This dataset contains data from simulations with <a href="https://github.com/milankl/ShallowWaters.jl">ShallowWaters.jl</a> with varying number formats and with or without a compensated time integration. All other parameters are shared between simulations. All .tar.gz are packed folders of the same name that contain netCDF files presenting velocities u,v, sea surface height eta, and tracer sst (sea surface temperature)</p> <ul> <li>run0002. Float16 simulation with compensated summation in the time integration.</li> <li>run0003. Float16 simulation without compensated summation in the time integration.</li> <li>run0004. Float64 reference simulation (without compensated summation in the time integration).</li> <li>run0005. Float16/32 mixed-precision simulation. No compensated time integration.</li> </ul> <p>Additionally, parameter.txt in each run summarizes all model parameters and progress.txt was created to monitor the progress of the data output during simulation. The file benchmarking.jld2 stores data for the benchmarking of the different runs as Julia's <a href="https://github.com/JuliaIO/JLD2.jl">JLD2 format</a> (a subset of HDF5).</p> <p>This dataset was created using the Isambard UK National Tier-2 HPC Service operated by GW4 and the UK Met Office, and funded by the Engineering and Physical Sciences Research Council EPSRC.</p> <p>For more details, see <a href="https://github.com/milankl/ShallowWaters.jl">ShallowWaters.jl</a> or the preprint <a href="http://doi.org/10.1002/essoar.10507472.2">Kloewer et al, 2021</a>.</p>
Dataset for "A simple and accurate method to determine fluid-crystal phase boundaries from direct coexistence simulations"
<p>This is a dataset for the article "A simple and accurate method to determine fluid-crystal phase boundaries from direct coexistence simulations", available at https://arxiv.org/abs/2403.10891 (Full citation data will be added upon final publication of the article.)</p> <p>This package provides figure data and representative configuration files associated with the systems studied in the article above. Additionally, for the hard sphere system, this package includes direct coexistence data for all reported system sizes and crystal orientations.</p> <p> </p> <p> </p>
Wall Resolved Fluid-Structure Interaction Numerical Simulation of a Modern Wind Turbine Blade
<p>Wall-resolved fluid-structure interaction (FSI) numerical simulations of the NREL 5 MW wind turbine blade<br> are compared using two FSI approaches. The first method is based on high-fidelity Nektar++/SHARPy FSI framework,<br> where the fluid governing equations are solved using high-order spectral/hp element method and the turbulent flow is<br> resolved using Large Eddy Simulation (LES) on thick strips, while large-deformation dynamics of the structure are mod-<br> elled using a geometrically exact nonlinear composite beam finite-element model. Thick strip method for the fluid reduces<br> the computational cost by considering a series of smaller domains, each of which has a finite thickness in the spanwise<br> direction. Hence, the overall flow over the blade is treated with a sectional approach, where in each of these sections,<br> strips, the 3D flow is reconstructed locally. Tip-loss correction is used to compensate for the sectional approach over the<br> blade. The second FSI approach is based on OpenFoam/Calculix coupling, where the second-order unstructured finite<br> volume method approach is used for solving the three-dimensional flow equations and the flow turbulence is captured us-<br> ing the k-ω SST model. The structural dynamics are modeled via second-order finite element method using standard solid<br> elements. Effects of the solution fidelity on the prediction of aerodynamic forces as well as on the full three-dimensional<br> flow modelling over the blade versus sectional representation of flow over the blade while incorporating the local three-<br> dimensionality in each section and tip-correction are discussed. Further, significance of two approaches on modelling<br> the slender blade, one using the beam mode and the other utilizing the full 3D solution of structure is addressed. Finally,<br> assessment of computational cost and scalability of the two approaches are presented and discussed.</p>
3D Flow Field of a subaqueous cylindrical pendulum from large eddy simulation with fluid structure interaction
<p>Relevant data of fluid structure interaction large eddy simulation of a subaqueous cylindrical pendulum to reproduce the major findings of the article "Fluid structure interaction of a subaqueous pendulum: Analyzing the effect of wake correction via large eddy simulations" published in Physics of Fluids. The article has been published as open access: https://doi.org/10.1063/5.0086557</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.
Mesh and configuration files to perform coupled heat+fluid simulations on a realistic human eyeball geometry with Feel++
<h1>Run the simulation</h1> <h2>With slurm</h2> <p>Set up position and desired mesh in the `run.slurm` file. Then, submit the job with the following command:</p> <p><code>sbatch run.slurm</code><br><br></p> <h2>Without slurm</h2> <p>Run by hand the command of the <code>run.slurm</code> file.<br><code>POSITION=prone # prone supine standing</code><br><code>SOLVER_TYPE=simple # simple lsc</code><br><code>MESH_INDEX=M4 # M1 M2 M3 M4 M5</code></p> <p><code>mpirun -np 128 feelpp_toolbox_heatfluid \</code><br><code> --config-files eye-${POSITION}.cfg pc_${SOLVER_TYPE}.cfg \</code><br><code> --heat-fluid.json.patch='{ "op": "replace", "path": "/Meshes/heatfluid/Import/filename", "value": "$cfgdir/mesh/Mr/'${MESH_INDEX}'/Eye_Mesh3D_p$np.json" }' \</code><br><code> --heat-fluid.scalability-save=1 --heat-fluid.heat.scalability-save=1 --heat-fluid.fluid.scalability-save=1</code></p> <h2>Available meshes</h2> <p>The meshes are available and are already partitioned for parallel computing:</p> <p><code>M0 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M1 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M2 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M3 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M4 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M5 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M6 : 128, 256, 384, 512, 640, 768</code></p>
Experimental data of Mg WE43 fiber biocorrosion in simulated body fluid
<p>This dataset contains the experimental data used to plot Figures 4 and 6 in the paper:</p> <p>S. Kovacevic, W. Ali, E. Martínez-Pañeda, J. LLorca “Phase-field modeling of pitting and mechanically-assisted corrosion of Mg alloys for biomedical applications” published in Acta Biomaterialia (2023)</p>
Validation of an Idealized Aorta Model Analysed through Fluid-Structure Interaction Simulation with Robin-Neumann Partitioned Approach
<p>The aorta is multiphysics system where hemodynamics and wall structural mechanic are mutually influenced. A fluid-structure interaction approach is appropriate to describe the mechanical alterations suffered by the aortic wall in response to altered hemodynamic patterns. This work demonstrates the validation of the simulated idealized aorta model with a fluid-structure interaction (FSI) model through modified PIMPLE solver to use Robin-Neumann partitioned approach for the strongly-coupled algorithm using solids4foam v2. The validation involves the comparison of streamlines, pressure, and displacements with in vivo measurements. The geometry is reconstructed from the healthy aorta presented in 10.5281/zenodo.5801938. Our analysis shows that the streamlines and pressure pattern are comparable with the literature data acquired using rich medical imaging data. The maximum diameter deformation at the level of abdominal aorta is comparable with measured data and the diameter deformation profile along the cardiac cycle correctly follow the velocity profile. According to this results, our work shows a high-performance simulation suitable for several future works.</p>
Coils test in simulated body fluid - H2020 UWIPOM2 PROJECT
<p>Coils test in simulated body fluid</p> <p>https://www.youtube.com/watch?v=HXBaD884-DA&ab_channel=Prof.Efr%C3%A9nD%C3%ADezJim%C3%A9nez-UniversidaddeAlcal%C3%A1</p>
Falling balls in a viscous fluid with contact: Comparing numerical simulations with experimental data
<p>The full results of the numerical computations and the source code for the rigid-body ALE and rigid-body CutFEM discretisations as presented in "H. von Wahl, T. Richter, S. Frei and T. Hagemeier. ‘Falling balls in a viscous fluid with contact: Comparing numerical simulations with experimental data’. In: <em>Phys. Fluids </em>33.3, 033304 (2nd Mar. 2021). doi: <a href="http://10.1063/5.0037971">10.1063/5.0037971</a>. <a href="https://arxiv.org/abs/2011.08691">arXiv:2011.08691</a> [physics.flu-dyn]".</p>
3D Multi-fluid MHD Simulation of the Early Time Behavior of an Artificial Plasma Cloud in the Bottom Side Ionosphere
<p>This repository contains the necessary files for reproducing each of the figures in Ober et. al (2021) - currently a manuscript. Each file is named after the associated figure number and stored in the HDF5 format - with maximum compression. In general all chemical species physical variables are stored with four indexed dimensions: (species, z-axis, y-axis, x-axis).</p> <p> </p> <p>The details for chemical species are based in corresponding index values with string array ‘species’:<br> </p> <p>0 -> Ba</p> <p>1 -> O</p> <p>2 -> O+</p> <p>3 -> Ba+</p> <p>4 -> e-</p> <p> </p> <p>Axis values are in units of meters (m) from spatial origin </p> <p>X-axis (1, 300)</p> <p>Y-axis (1, 300)</p> <p>Z-axis (1, 888)</p> <p> </p> <p>Chemical species number densities in units of meters cubed (m^3)</p> <p>number_density ( 5, 888, 300, 300 ) </p> <p> </p> <p>Chemical species Temperature in units of Kelvin (K)</p> <p>temperature ( 5, 888, 300, 300 ) </p> <p> </p> <p>Chemical species velocity component values - meters per second (m/s)</p> <p>velocity_x ( 5, 888, 300, 300 ) </p> <p>velocity_y ( 5, 888, 300, 300 ) </p> <p>velocity_z ( 5, 888, 300, 300 ) </p> <p> </p> <p>Magnetic field in units of Teslas (T)</p> <p>magnetic_x ( 1, 888, 300, 300 ) </p> <p>magnetic_y ( 1, 888, 300, 300 ) </p> <p>magnetic_z ( 1, 888, 300, 300 ) </p> <p> </p> <p>NOTE: To help meet the data set limit, magnetic field variables are excluded from file “_figure-1.h5”. For "__figure-7b.h5" and "__figure-7c.h5" only magnetic field components are included.</p>
Dataset to "Nanothermodynamic description and molecular simulation of a single-phase fluid in a slit pore"
<p>This is the dataset presented in the article "Nanothermodynamic description and molecular simulation of a single-phase fluid in a slit pore" [1]. The columns from left to right are</p> <ul> <li>Slit pore height</li> <li>Normal pressure</li> <li>Standard deviation of the mean of the normal pressure</li> <li>Integral pressure</li> <li>Standard deviation of the mean of the integral pressure</li> <li>Surface tension</li> <li>Standard deviation of the mean of the surface tension</li> <li>Normal pressure from local fluid number density profile</li> <li>Standard deviation of the mean of the normal pressure from local fluid number density profile</li> <li>Fluid number density</li> <li>Standard deviation of the mean of the fluid number density</li> <li>Number of samples</li> <li>Zeroes</li> <li>Temperature</li> <li>Standard deviation of the mean of the temperature</li> <li>Potential energy</li> <li>Standard deviation of the mean of the potential energy</li> <li>Kinetic energy</li> <li>Standard deviation of the mean of the kinetic energy</li> </ul> <ol> <li>https://arxiv.org/abs/2012.00562</li> </ol>
Simulated distribution of the fluid salinity, Cu and temperature in a sub-volcanic region
<p>The files brinelens.00.vtu, brinelens.01.vtu,... contain the simulated distributions of the fluid bulk salinity (a), the Cu concentration in fluid for the 'no sulfur' case (b), the Cu concenration in fluid for the 'sulfur unlimited' case (c), the Cu deposition for the 'sulfur unlimited' case (d), and the temperature (e) at t=0 yr, 10000 yr, 20000 yr,..., respectively. The distributions shown in Fig. 6a-e are in brinelens.10.vtu.</p> <p>The file brinelens.csv contains the simulated time evolution of the total mass of dissolved and precipitated Cu. These data are plotted in Fig. 7.</p>
Fully-coupled pressure-based finite-volume framework for the simulation of fluid flows at all speeds in complex geometries (Supporting Data)
<p>This data accompanies the paper "Fully-coupled pressure-based finite-volume framework for the simulation of fluid flows at all speeds in complex geometries", published in Journal of Computational Physics (2017), http://dx.doi.org/10.1016/j.jcp.2017.06.009.</p>
Dataset of paper "Predicting the size of silver nanoparticles synthesised in flow reactors: Coupling population balance models with fluid dynamic simulations"
<p>Dataset of paper "Predicting the size of silver nanoparticles synthesised in flow reactors: Coupling population balance models with fluid dynamic simulations"</p>
Comprehensive study of antimicrobial polycaprolactone/clay nanocomposite films: preparation, characterization, properties and degradation in simulated body fluid
<p>Even though the biodegradability of polycaprolactone (PCL) and its nanocomposites is lower compared to other biodegradable polyesters, this property and good biocompatibility are used for development of materials for drug delivery with a long-term effect. We prepared novel PCL/clay nanocomposite films with antimicrobials chlorhexidine (CH) or octenidine (OCT) combined with ZnO anchored on vermiculite (VER). The intercalation of CH and OCT into the interlayer of VER/ZnOVER was confirmed by XRD, FTIR and SEM. The organically modified nanofillers compared to VER (−46.0 mV) or ZnOVER (−34.9 mV) showed a positive ζ-potential (+30.7 mV (VER_CH), +21.9 mV (VER_OCT), +24.6 mV (ZnOVER_CH)) indicating a relatively stable materials, except ZnOVER_OCT (+8.6 mV), which strongly agglomerated.</p> <p>Thin PCL/clay films were prepared by solvent casting method and the effect of used nanofillers on structural, thermal, mechanical and antimicrobial properties followed by degradation under hydrolytic conditions was studied. The results showed that presence of ZnO significantly decreases thermal and mechanical stability. The nanofillers with the higher hydrophilic character are responsible for the fastest degradation of PCL matrix. Films possessed high antimicrobial efficiency in long time intervals, hence these nanocomposites open new avenues for the possible application of such materials for the drug delivery with a long-term effect.</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.