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14 results for “finite volume”
Finite amplitude sound propagation effects in volume backscattering measurements for fish abundance estimation
<p>The upload contains measurement and simulation data for finite-amplitude sound propagation effects in volume backscattering measurements. The experimental data are from a trawl survey conducted in the North Sea with R/V "G. O. Sars", 6-7 November 2004, passing several times over a group of Atlantic mackerel schools. The measurements are of the relative area backscattering coefficient, relative to 38 kHz, 2000 W power setting, at</p> <p>(1) 120 kHz with 250 W transmit power setting, 200 kHz with 120 W transmit power setting<br> (2) 120 kHz with 1000 W power setting, 200 kHz with 1000 W power setting.</p> <p>A Simrad EK60 echosounder system was used, alternating between the low (1) and high (2) power settings through the measurement series.</p> <p>The corresponding simulation data are calculated using the Bergen Code numerical solver of the KZK Equation. The medium parameters input to the simulations are based on CTD data from the field survey . The transducer and amplitude data were found by laboratory measurements on echo sounders of the same type as used in the survey.</p> <p>.m files are included for both .mat data files, with details on how to read the data.</p> <p>An article describing the data has been submitted by the authors to Acta Acustica, 2022.</p>
A consistent discretization of the single-field two-phase momentum convection term for the unstructured finite volume Level Set / Front Tracking method - data
<p>Research data from the rhoLENT unstructured Level Set / Front Tracking method for simulating two-phase flows with large density ratios. </p>
One-dimensional hydrodynamic solution data of wavelet-based adaptive finite volume and discontinuous Galerkin shallow water solvers
<p>Raw data for a series of idealised, one-dimensional hydrodynamic test cases:</p> <ul> <li>dambreakwet (SWASHES 4.1.1)</li> <li>dambreakdry (SWASHES 4.1.2)</li> <li>dambreakmanning (SWASHES 4.1.3)</li> <li>dambreakupslope, dambreakdownslope (<a href="http://doi.org/10.1061/(ASCE)HY.1943-7900.0000494">Kesserwani and Liang 2011</a>)</li> <li>dambreakonehump (<a href="https://doi.org/10.1080/19942060.2011.11015393">Ozmen-Cagatay and Kocaman 2011</a>)</li> <li>lakeatrest (<a href="https://doi.org/10.29007/vm3q">Kesserwani et al. 2018</a>)</li> <li>parabolicbowlswashes (SWASHES 4.2.1)</li> <li>parabolicbowlliangmarche (<a href="https://doi.org/10.1016/j.advwatres.2009.02.010">Liang and Marche 2009</a>)</li> <li>steadysubcritical (SWASHES 3.1.3)</li> <li>steadysupercritical (<a href="https://doi.org/10.2166/hydro.2015.039">Haleem et al. 2015</a>)</li> <li>steadytranscriticalshockless (SWASHES 4.1.4)</li> <li>steadytranscriticalshock (SWASHES 4.1.5) </li> </ul> <p>SWASHES refers to <a href="https://doi.org/10.1002/fld.3741">Delestre et al. 2013</a> </p>
Efficient GPU Offloading with OpenMP for a Hyperbolic Finite Volume Solver on Dynamically Adaptive Meshes
<p>We identify and show how to overcome an OpenMP bottleneck in the administration of GPU memory. It arises for a wave equation solver on dynamically adaptive block-structured Cartesian meshes, which keeps all CPU threads busy and allows all of them to offload sets of patches to the GPU. Our studies show that multithreaded, concurrent, non-deterministic access to the GPU leads to performance breakdowns, since the GPU memory bookkeeping as offered through OpenMP's map clause, i.e., the allocation and freeing, becomes another runtime challenge besides expensive data transfer and actual computation. We, therefore, propose to retain the memory management responsibility on the host: A caching mechanism acquires memory on the accelerator for all CPU threads, keeps hold of this memory and hands it out to the offloading threads upon demand. We show that this user-managed, CPU-based memory administration helps us to overcome the GPU memory bookkeeping bottleneck and speeds up the time-to-solution of Finite Volume kernels by more than an order of magnitude.</p>
Dataset for "Verifying Monte Carlo simulations of diffusion tensor cardiovascular magnetic resonance using a finite volume method"
<p>This dataset contains the results of random walk and finite volume simulations of diffusion in cardiac tissue. The data was used for the work presented at the 8th World Congress of Biomechanics in 2018.</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>
Numerical data set belonging to: 'A Finite Volume Parallel Adaptive Mesh Refinement Method for Solid-Liquid Phase'
<p>This data set corresponds to the paper 'A Finite Volume Parallel Adaptive Mesh Refinement Method for Solid-Liquid Phase Change', submitted to Numerical Heat Transfer, Part A: Applications. The numerical data is included in VTK format (to be read by paraView) for the following cases:</p><p>1) 2D Gallium melting in a rectangular cavity (70x50 elements, 140x100 elements, 280x200 elements, 560x400 elements, 1120x800 elements and adaptive mesh)</p><p>2) 3D Gallium melting in a hexagonal cavity (adaptive mesh)</p><p>3) 2D freeze-plug (both steady-state and melting transient): 110x300 elements, 220x600 elements, 440x1200 elements and adaptive mesh)</p><p>Due to the size of the data-set, the data has been split over 8 tar archives featuring a gzip compression. To unpack the data, run the command: cat paper_<i>amr</i>_<i>data.</i>tar.gz.* | tar xzvf -</p><p> </p>
FVM 1.0: A nonhydrostatic finite-volume dynamical core for the IFS
<p>Simulation output data used to produce the figures in the paper.</p> <p>Four different files are provided. These refer to the IFS-FVM and IFS-ST results for the dry and moist configurations of the baroclinic instability benchmark.</p> <p>The paper is available at: https://doi.org/10.5194/gmd-12-651-2019</p>
Quasi-2D Finite Volume Modeling of Corona Discharges for Ionic Propulsion: Comparison of Reduced Reaction Schemes
<p>This work investigated the effects of different kinetic models on the results of corona discharge simulations of a wire-cylinder geometry. The considered dry air kinetic models are: a 6-species model from Parent et al. (<a title="https://doi.org/10.1016/j.jcp.2013.11.029" href="https://doi.org/10.1016/j.jcp.2013.11.029" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.jcp.2013.11.029</a>), a Townsend-like model (<a title="http://dx.doi.org/10.1088/0022-3727/30/4/017" href="http://dx.doi.org/10.1088/0022-3727/30/4/017" target="_blank" rel="noreferrer noopener">http://dx.doi.org/10.1088/0022-3727/30/4/017</a>) and the simple model proposed by Mateo-Velez et al. (<a title="http://dx.doi.org/10.1088/0022-3727/41/3/035205" href="http://dx.doi.org/10.1088/0022-3727/41/3/035205" target="_blank" rel="noreferrer noopener">http://dx.doi.org/10.1088/0022-3727/41/3/035205</a>).</p>
Simulation data for "Modeling radiation belt dynamics using a positivity-preserving finite volume method on general meshes"
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Simulation data and code for "Modeling radiation belt dynamics using a positivity-preserving finite volume method on general meshes"
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Model codes and data for ``A control volume finite element model for predicting the morphology of cohesive-frictional debris flow deposits"
<p>The code and the dataset can be read/run by using Matlab. The description is as follows:</p> <p>1. Dataset (field_data) includes transect data of three field debris flow deposits from Coussot et al. (1996). The data can be read and calibrated with the analytical solution by the code field_calibration.m.</p> <p>2. Dataset (data_T01-T04, T11-T15_DT) includes experimental fan topography data, calibrated parameters, and simulation outputs. </p> <p>3. Two calibration codes are used for the model parameter calibrations of the two sets of experiments.</p> <p>4. Function aggradation_DT.m is the CVFEM model for simulating fan morphology. Use CVFEM_exp_simulation.m code to run simulations for the experiments.</p>
Finite Volume Community Ocean Model-based Arctic Ocean Forecast System: A Comprehensive Assessment of Sea Ice Forecast Results without Data Assimilation
<p>The dataset contains sea ice forecast results from the Finite Volume Community Ocean Model-based Arctic Ocean Forecast System (FVCOM-AOFS) for the period 2019-2020. The output result of the system is presented as daily mean values.</p><p>Each netCDF file within the dataset includes the following variables: lon, lat, lonc, latc, aice, vice, uuice, and vvice. Specifically, the variables of lon and lat represent the longitude and latitude of the unstructured triangular grid. The variables of lonc and latc represent the longitude and latitude of the unstructured triangular cell. The variables of aice and vice indicate sea ice concentration and sea ice thickness. The variables of uuice and vvice indicate eastward and northward sea ice drift velocity. The scalars including aice and vice use lon and lat coordinates, and the vectors including uuice and vvice use lonc and latc coordinates. </p>
Database For Finite Volume Features, Global Geometry Representations, and Residual Training for Deep Learning-based CFD Simulation
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