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59 results for “Fluid modeling”
Data for "On Ohm's law in reduced plasma fluid models"
<p>Simulation data and post-processing scripts to create the figures in the paper "On Ohm's law in reduced plasma fluid models", published in <em>Plasma Physics and Controlled Fusion</em>.</p> <p>To re-produce the figures, install the Python package `xbout` (using pip: `pip install xbout`; or conda: `conda install xbout`), unzip the file from this archive, and run the script `make_paper_figures.py`. Figure 1 is `finite_Ti_plots/compare-sims_baseall/CoM_midplane0.pdf`; figure 2a is `finite_Ti_plots/compare-sims_base/timestep.pdf`; figure 2b is `finite_Ti_plots/compare-sims_base/rhs_evals.pdf`.</p>
Output tomographic models for "The attenuation and scattering signature of fluids and tectonic interactions in Central-Southern Apennine."
<p>Output ASCII file for the seismic attenuation tomography in Central-Southern Apennines. The output format is the one from MuRAT software (De Siena et al., 2014). Q and Peak-Delay models in 1.5 Hz, 3 Hz and 6 Hz frequencies are reported as specificated by the files name. The output points of a grid with coordinates available in WGS84 degrees ("Degrees" suffix) or already projected in kilometric UTM coordinates ("UTM" suffix).</p> <p>All other information can be found in the main and supplementary text.</p>
Artificial viscosity model to mitigate numerical artefacts at fluid interfaces with surface tension (Supporting data)
<p>This data accompanies the paper "Artificial viscosity model to mitigate numerical artefacts at fluid interfaces with surface tension", published in Computers & Fluids.</p>
Three-Dimensional Thermoporoelastic Modeling of Hydrofracturing and Fluid Circulation in Hot Dry Rock: EGS Collab Experiment 1
<p>The data regarding the determined natural fractures, locations of monitoring devices, microseismic events, and well trajectories in EGS Collab Experiment 1.</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.
Data from: Fluid-kinetic model of a propulsive magnetic nozzle
<p># Data from: Fluid-kinetic model of a propulsive magnetic nozzle</p> <p> </p> <p>- Authors: Mario Merino, Judit Nuez, Eduardo Ahedo</p> <p>- Contact email: mario.merino@uc3m.es</p> <p>- Date: 2021-10-08</p> <p>- Keywords: magnetic nozzle, plasma propulsion, electrodeless plasma thrusters, kinetic model, collisionless electron cooling, magnetic thrust</p> <p>- Version: 1.0.0</p> <p>- Digital Object Identifier (DOI): 10.5281/zenodo.5557592</p> <p>- License: This dataset is made available under the [Open Data Commons Attribution License](http://opendatacommons.org/licenses/by/1.0/)</p> <p> </p> <p>## Abstract</p> <p> </p> <p>This dataset contains the magnetic nozzle fluid-kinetic simulation results used to prepare:</p> <p> </p> <p>_[Mario Merino, Judit Nuez, Eduardo Ahedo, "Fluid-kinetic model of a propulsive magnetic nozzle", Plasma Sources Science and Technology](https://doi.org/10.1088/1361-6595/ac2a0b)._</p> <p> </p> <p>## Dataset description</p> <p> </p> <p>The simulations have been prepared combining two open source codes:</p> <p>[Akiles](10.5281/zenodo.1098432) and [Fumagno](10.5281/zenodo.593787).</p> <p>The model and the simulation cases are explained in the accompanying paper (https://doi.org/10.1088/1361-6595/ac2a0b).</p> <p> </p> <p>## Data files</p> <p> </p> <p>The datafiles are in standard Matlab .mat format. A recent version of [Matlab](https://www.mathworks.com/products/matlab.html) (2018a or newer) is needed to read these files .</p> <p> </p> <p>Datafiles are subdivided into two groups (1D and 2D).</p> <p> </p> <p>In the 1D group, simulations for the first part of the paper are contained. These are simulations along a single (1D) magnetic line. There are 7 files:</p> <p>1. line_J0.mat</p> <p>2. line_phiinfty5.mat</p> <p>3. line_phiinfty6.mat</p> <p>4. line_phiinfty7.mat</p> <p>5. line_phiinfty8.mat</p> <p>6. line_phiinfty9.mat</p> <p>7. line_phiinfty10.mat </p> <p>Each of these files has an identical structure, with the following Matlab variables in them. All variables are normalized as explained in the paper:</p> <p>* h: a vector containing the value of B (magnetic field strength) at each point in the magnetic line</p> <p>* phi: a vector containing the value of phi (electric potential) at each point in the magnetic line</p> <p>* electrons: a structure with all the moments and all the properties of the electrons</p> <p>* ions: a structure with all the moments and all the properties of the ions</p> <p> </p> <p>In the 2D group, simulations for the second part of the paper are contained. These are 2D simulations. A total of 5 files exist, corresponding to each simulation case in the paper:</p> <p>1. F.mat</p> <p>2. PHID.mat</p> <p>3. PHII.mat</p> <p>4. TD.mat</p> <p>5. TI.mat</p> <p>Each of these files has an identical structure, with the following Matlab variables in them. All variables are normalized as explained in the paper:</p> <p>* Z,R: position of points</p> <p>* B,ALPHA,KAPPA: magnetic field strength, angle, and curvature. B_B0 is B normalized with the upstream value on each line.</p> <p>* PHI, EZ, ER: electric potential and field components</p> <p>* J, J0: current density, and the integral current in the magnetic nozzle</p> <p>* N, N1, N2, N4: density of the full electron population and subpopulations 1 (free), 2 (reflected), 4 (doubly-trapped)</p> <p>* TE, TE1, TE2, TE4: average temperature of the full electron population and subpopulations 1 (free), 2 (reflected), 4 (doubly-trapped)</p> <p>* TPARE, TPARE1, TPARE2, TPARE4: parallel temperature of the full electron population and subpopulations 1 (free), 2 (reflected), 4 (doubly-trapped)</p> <p>* TPERE, TPERE1, TPERE2, TPERE4: perpendicular temperature of the full electron population and subpopulations 1 (free), 2 (reflected), 4 (doubly-trapped)</p> <p>* UE, UE1, UI: velocity of electrons, free electrons, ions</p> <p> </p> <p>## Citation</p> <p> </p> <p>Works using this dataset or any part of it in any form shall cite it as follows.</p> <p> </p> <p>The preferred means of citation is to reference the publication associated to this dataset, of DOI 10.1088/1361-6595/ac2a0b.</p> <p> </p> <p>Optionally, the dataset may be cited directly by referencing the DOI: 10.5281/zenodo.5557592.</p> <p> </p> <p>## Acknowledgments</p> <p> </p> <p>This dataset was created by the [ERC-ZARATHUSTRA project](https://erc-zarathustra.uc3m.es/).</p> <p> </p> <p>The ERC-ZARATHUSTRA project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 950466). </p>
Dataset with the node discretisations employed for training advection models in "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"
<p>Dataset with the node discretisations employed for training advection models in "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics" (https://doi.org/10.1063/5.0097679).</p> <p>The training code is available at https://github.com/mario-linov/graphs4cfd.</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>
Data of Two-fluid Modeling of Acoustic Wave Propagation in Gravitationally Stratified Isothermal Media
<p>Fully data of the paper "Two-fluid Modeling of Acoustic Wave Propagation in Gravitationally Stratified Isothermal Media" in the Astrophysical Journal.</p> <p>The Astrophysical Journal, 911:119 (18pp), 2021 April 20.</p>
Data from: Exploiting nozzle geometry to predict resolution in extrusion-based bioprinting: mathematical modelling of a power-law fluid
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Slab transport of fluids to deep focus earthquake depths - thermal modeling constraints and evidence from diamonds
<p>This data set contains earthquake data and thermal models of subduction zones used in Shirey, S. B., Wagner, L. S., Walter, M. J., Pearson, D. G., & van Keken, P. E., "Slab transport of fluids to deep focus earthquake depths - thermal modeling constraints and evidence from diamonds", submitted to AGU Advances.</p> <p>There are four zip files:<br> 1) Events.zip contains the earthquake location data;<br> 2) PTeq.zip contains the estimated pressure and temperature in the EQ locations as projected onto slab top and Moho<br> 3) ThermalModels.zip contains the temperature along paths parallel to the slab top for each subduction zone<br> 4) ThermalModels_vtu.zip contains the temperature on the full computational grid<br> <br> See the README files for information on the data formats for 1-3.</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>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.