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47 results for “fluid simulation”
Reliable chaotic transition in incompressible fluid simulations - Supplementary Material
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Non-Newtonian Power-law fluid simulations in rectangular channel
<h1>Velocity distribution in rectangular channel for Power law fluid.</h1> <p>Using viscosity: eta(x)=K*(x/gamma0)^(n-1)/gamma0</p> <p>Solved the PDE equation: https://doc.comsol.com/5.5/doc/com.comsol.help.comsol/comsol_ref_equationbased.23.008.html<br>in rectangular channel in COMSOL with<br>c=eta(sqrt(d(u,x)^2+d(u,y)^2), f=0.013333333 [Pa*s]<br>e_a, d_a, alpha, beta, gamma=0<br>gamma0=1[1/s]<br>Boundary conditions on x=0, y=0, x=witdh, y=height is u=0</p> <p>Output file contains: Coordinates X, Y [mm] on triangular grid and Velocity u [m/s]</p> <p>File specific parameters:<br>"H2O_100x096.txt" - n=1, K=0.013333333 [Pa*s], height=1 [mm], width=0.96 [mm]<br>"10pFBS_37C_100x115.txt" - n=0.5, K=0.00541540444218501 [Pa*s], height=1 [mm], width=1.15 [mm]<br>"10pFBS_097x092.txt" - n=0.32, K=0.00541540444218501 [Pa*s], height=0.97 [mm], width=0.92 [mm]</p>
Quasi-Newton methods for partitioned simulation of fluid-structure interaction reviewed in the generalized Broyden framework: code and data
<p>These files accompany the publication</p><p>N. Delaissé, T. Demeester, R. Haelterman and J. Degroote. Quasi-Newton methods for partitioned simulation of fluid-structure interaction reviewed in the generalized Broyden framework.<i> Archives of Computational Methods in Engineering</i>, Vol.<strong> </strong>30, 3271-3300, 2023. doi: <a href="https://doi.org/10.1007/s11831-023-09907-y">10.1007/s11831-023-09907-y</a></p><p>In this work, the performance of multiple quasi-Newton methods are compared in terms of memory requirements and computational time. The results are generated for the well-known flexible tube example case, using the open-source code <a href="http://github.com/pyfsi/coconut">CoCoNuT</a>. This code, developed at Ghent University, is Python-based and has the capability to couple existing solvers, both open-source and commercial solvers.</p><p>This archive consists of the following files.</p><ul><li><strong>coconut.tar.gz: </strong>the specific CoCoNuT version used (sep-2022), including the Python flow and structure solvers for the flexible tube and modifications for monitoring memory requirements</li><li><strong>compare_coupling_algorithms.tar.gz: </strong>the scripts to set up the cases and perform the calculations and post-processing</li><li><strong>results.tar.gz:</strong> the generated result data</li></ul><p>For requirements to run CoCoNuT, refer to the <a href="http://pyfsi.github.io/coconut/">documentation</a>. Additionally, the Python package guppy3 is required for monitoring the memory use. In this work the data were generated with Andaconda3-2022.05 and the package guppy3-3.1.2.</p><p>Before running the provided scripts, make sure the parent directory of the "coconut" folder is added to the PYTHONPATH. The calculations can be started with "python run.py". For the cases which names contain "_m" followed by a number, e.g. "_m100", the number refers to the number of discretization points on the interface. The cases with suffix "_c" are distinct from those without, as they don't perform the time consuming memory monitoring and are therefore used for measuring computational time.</p>
Data for "Convective Organization and Dry Tropical Cyclones in Direct Numerical Simulations of Idealized Fluid Setups"
<p>Supporting data and analysis scripts for work contained in the manuscript Velez-Pardo, Martin & Cronin, Timothy W. (2023) Convective Organization and Dry Tropical Cyclones in Direct Numerical Simulations of Idealized Fluid Setups. README and scripts for running simulations and for data post-processing are found in README_and_scripts.zip. Data obtained using supercomputer Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory (CISL), sponsored by the National Science Foundation.</p>
Simulated distribution of the fluid salinity, Cu and temperature in a sub-volcanic region
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Simulation data and scripts for CFD-DEM simulation of granular flows in ambient fluid
<ul> <li>Data sets - contains the raw data required to replicate and validate all plots in the main article. Contains folders in containing measurements of basic flow properties, e.g. velocities, shear rates, etc., of monodisperse and bidisperse granular flows in different flow regimes. Each folder contains a ReadMe.txt briefly explaining the content and lay out of each data set.</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>
256 DPPC Molecules bilayer in pure Water, simulated at 288K (gel) or 358K (fluid)
<p><strong>Publication:</strong> MLLPA: A Machine Learning-assisted Python module to study phase-specific events in lipid membranes</p> <p><strong>Published on:</strong> 08 April 2021</p> <p><strong>Journal</strong>: <em><a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.26508">J Comp Chem</a>, </em>2021, DOI: 10.1002/jcc.26508</p> <p><strong>Description</strong>: Simulation files used to train our Python module to identify the thermodynamic phase of individual lipid molecules in a bilayer, as well as the simulation files analysed by the machine learning models. More information on the module can be found on <a href="https://vivien-walter.github.io/mllpa/">its website</a>.</p> <p>The training files are named dppc_gel.gro and dppc_fluid.gro. They respectively correspond to the final frame of the systems simulated at 288K and 358K. All other files are the files analysed by the module.</p> <p><strong>System composition:</strong></p> <ul> <li>DPPC molecules: 256 with 130 atoms each</li> <li>Water molecules: 42,492 with 3 atoms each </li> <li>Simulation box dimensions (approx.): 9 x 9 x 20 nm</li> </ul> <p><strong>Simulation details:</strong></p> <ul> <li>Software: Gromacs (v. 2020)</li> <li>Forcefield: Charmm36 (v. June 2015) - Water: TIP3P</li> <li>Thermostat: Nose-hoover (0.4ps, 2 groups)</li> <li>Barostat: Parrinello-Rahman semi-isotropic (2.0ps, 1.0 bar on each axis, 4.5e-5 bar-1)</li> <li>Duration: 25 ns (after stabilisation)</li> </ul>
Experimental Data for "Seismic wave attenuation and dispersion due to partial fluid saturation: Direct measurements and numerical simulations based on X-Ray CT"
<p>Experimental Data from a Berea sandstone sample. Includes X-ray CT scans and mechanical response of sample.</p> <p>Abstract</p> <p>Quantitatively assessing seismic attenuation caused by fluid pressure diffusion (FPD) in partially saturated rocks is challenging because of its sensitivity to the spatial fluid distribution. To address this challenge we performed depressurisation experiments to induce the exsolution of carbon dioxide from water in a Berea sandstone sample. In a first set of experiments we used medical X-ray computed tomography (CT) to characterise the fluid distribution. At an equilibrium pressure of ~1 MPa and applying a fluid pressure decline rate of ~0.6 MPa per minute, we allowed a change in saturation of less than 1 %. The gas was heterogeneously distributed along the length of the sample, with most of the gas exsolving near the sample outlet. In a second set of experiments, at the same pressure and temperature, following a very similar exsolution protocol, we measured the frequency dependent attenuation and modulus dispersion between 0.1 and 1000 Hz using the forced oscillation method. We observed significant attenuation and dispersion in the extensional and bulk deformation modes, however not in the shear mode. Lastly, we use the fluid distribution derived from the X-ray CT as an input for numerical simulations of FPD to compute the attenuation and modulus dispersion. The numerical solutions are in close agreement with the attenuation and modulus dispersion measured in the laboratory. Our methodology allows for accurately relating attenuation and dispersion to the fluid distribution, which can be applied to improving the seismic monitoring of the subsurface.</p>
Simulation results: effects of Chiari type 1 malformation on cerebrospinal fluid dynamics during arterial pulsations and coughing
<p>The folder contains the simulation results corresponding to the computational fluid dynamics study: Effects of Chiari type 1 malformation on cerebrospinal fluid dynamics during arterial pulsations and coughing</p> <p>Data is organized in the following way with files in .csv and .xlsx format</p> <ul> <li>Cropped model: <ul> <li>output_*: flow time in seconds, pressure in the fourth ventricle (Pv), outflow in m³/s or kg/s, and pressure at plane in spinal SAS (Psas) for physical herniations and herniations created by porous zones</li> </ul> </li> <li>Full model <ul> <li>arterial_pulsations: data with all boundary conditions (arterial) except from cough</li> <li>arterial_pulsations_and_cough: data with all boundary conditions (arterial) including cough</li> <li>file content: <ul> <li>*_boundary_data: time step number, number of coupling iterations necessary per time step, flow time (s), pressure (P) and flow (Q) at outlets with interstitium (1), spinal (2), lymphatic (3) and arachnoid villi (4), first element of Jacobian (J11), flow residual, value of perturbation (dP) in Pa, converged?: 1 when converged, 0 when not, perturbation parameter, number of times the perturbation value needed to be reduced.</li> <li>*_flow: flow time in seconds, volumetric flow through aqueduct, and spinal SAS</li> <li>*_pressure_data: flow time in seconds, relative pressure compared to interstitium outlet at the fourth ventricle (v4), the spinal SAS (sas) and the lateral ventricle (lv)</li> </ul> </li> </ul> </li> </ul>
Monitoring of the dissolution/precipitation behavior of bioglass with simulated body fluid buffered by HEPES
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Simulation code for fluids in two dimensions - vorticity movie
<p>A movie of the vorticity in a transverse only run. The data for the movie has been obtained using the simulation code found <a href="https://zenodo.org/record/5786090#.YbtUr71BwuU">here</a>.</p>
Simulation code for fluids in two dimensions - density and velocity movies
<p>Movies of the density perturbation and the magnitude of the velocity in a longitudinal only run. The data for the movies has been obtained using the simulation code found <a href="https://zenodo.org/record/5786090#.YbtUr71BwuU">here</a>.</p>
Transport and distribution of sodium ions in Mercury's magnetosphere: results from multi-fluid MHD simulations
<p>This dataset contains VTK files of the four simulations that are presented in the paper<em> </em><a href="https://essopenarchive.org/doi/full/10.22541/essoar.171629607.76912814/v1">Transport and distribution of sodium ions in Mercury’s magnetosphere: results from multi-fluid MHD simulations</a> .</p> <p>The physical parameters for each of the simulations are summarized below:</p> <div> <div>Sim-1: IMF = [0,0,-8.5] nT, V_sw = [-400,0,0] km/s</div> <div>Sim-2: IMF = [0,0,-8.5] nT, V_sw = [-400,0,0] km/s</div> <div>Sim-3: IMF = [0,0,-8.5] nT, V_sw = [-400,0,0] km/s</div> <div>Sim-4: IMF = [-15.2,8.4,-8.5] nT, V_sw = [-400,50,0] km/s</div> <div> <div> </div> <div>The Hall term is switched off in sim-2, but switched on in the rest of the simulations. The sodium source is used in all the simulations except for sim-3. In all the simulations, the solar wind density is 40 amu/cc with a temperature of 7.5 eV. We refer the readers to the aforementioned paper for more details.</div> </div> </div> <p>These VTK files can be opened with Paraview.</p>
Test Model for GTT Computational Fluid Dynamics Simulation by TransAT
<p>This archive contains the model and associated code need to run the GTT benchmark test for TransAT.</p>
Evaluation data for publication 'Swelling of kappa carrageenan hydrogels in a simulated body fluid'
<p>Original excel files, python script and rheometry data for the evaluation of the publication data.</p>
Code for paper named "Effects of fluid pressure development on hydrothermal mineralization via cellular automaton simulation"
<p>Code for paper named "Effects of fluid pressure development on hydrothermal mineralization via cellular automaton simulation"</p>
Simulation of sheared granular layers activated by fluid pressurization
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Fluid-structure simulations outperform computational fluid dynamics in the differentiation of progressive dilation in Marfan syndrome patients
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Fluid simulation of laminar flow past a cylinder
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Fluid simulation of the FDA nozzle benchmark
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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.