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30 results for “Hydrodynamic Simulations”
Compressible Hydrodynamics Simulation Data for "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows"
<p><strong>Background</strong></p> <p>This data is a 2D cross-section from a 3D compressible hydrodynamics simulation (Hyburn / AMRex code) of a rapid decompression / shock tube experiment at Special Technologies Laboratory. The simulated shot is a pure argon gas decompression from 1000Psi to atmosphere. </p> <p>This data is used in figures 3 and 5 of the paper "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows".</p> <p>Electric sparks and explosive flows have long been associated with each other. Flowing dust particles originate charge through contact and separate based on inertia, resulting in strong electric fields supporting sparks. These sparks can cause explosions in dusty environments, especially those rich in carbon, such as coal mines and grain elevators. Recent observations of explosive events in nature and decompression experiments indicate that supersonic flows of explosions may alter the electrical discharge process. Shocks may suppress parts of the hierarchy of the discharge phenomena, such as leaders. In our decompression experiments, a shock tube ejects a flow of gas and particles into an expansion chamber. We imaged an illuminated plume from the decompression of a mixture of argon and <100 mg of diamond particles and observe sparks occurring below the sharp boundary of a condensation cloud. We also performed hydrodynamics simulations of the decompression event that provide insight into the conditions supporting the observed behavior. Simulation results agree closely with the experimentally observed Mach disk shock shape and height. This represents direct evidence that the sparks are sculpted by the outflow. The spatial and temporal scale of the sparks transmit an impression of the shock tube flow, a connection that could enable novel instrumentation to diagnose currently inaccessible supersonic granular phenomena.</p> <p><strong>Accessing Data</strong></p> <p>The data is saved as python numpy zipped archives numbered by the timestep in the simulation. Files starting with 'tube' contain data from inside the shock tube. Files starting with 'near_vent' contain data from the expansion chamber above the nozzle. All units are in SI.</p> <p>Each .npz file is an array file generated with python numpy.savez(). It can be opened with:</p> <p><em>import numpy as np</em></p> <p><em>data = np.load('<name>.npz')</em></p> <p>The data is an python dictionary. The dictionary keys can be displayed with:</p> <p><em>print(data.files)</em></p> <p>The numpy arrays can be accessed by keyname:</p> <p><em>print(data['keyname'])</em></p> <p>The key names correspond to physical quantities (density, temperature, etc.). All particle quantities are 0 as the simulation did not include particles.</p>
A high-resolution, multi-decadal, free-running, hydrodynamic simulation of the East Australia Current System using the Regional Ocean Modeling System (Version 3.0, 1994-2019)
<p>The data is from a Regional Ocean Modelling System free-running, hydrodynamic simulation of the East Australian Current System. The model has a horizontal resolution of 2.5-6 km in the cross-shore direction and 5 km in the alongshore direction, and 30 vertical s-levels. The model domain covers the southeastern Australia oceanic region from 25.1-41.5°S and 147.1-162.2°E, and the grid is orientated 20 degrees clockwise to be predominantly orientated alongshore. The time period covered is 02 Jan 1994 to 28 Feb 2019. The model outputs provided are daily averages of the following variables: Two-dimensional variables: Sea surface height (zeta), barotropic cross-grid velocity (u) and barotropic along-grid velocity (v). Three-dimensional variables: Temperature (temp), salinity (salt), density (rho), cross-grid velocity (u), along-grid velocity (v) and vertical velocity (w), temperature time rate of change (temp_rate), temperature horizontal advection term (temp_hadv), temperature vertical advection term (temp_vadv), temperature horizontal diffusion term (temp_hdiff), temperature vertical diffusion term (temp_vdiff). In this version, the heat budget terms (temp_rate, temp_hadv, temp_vadv, temp_hdiff and temp_vdiff) are set to be zeros on the land.</p> <p> </p> <p>This model is part of the <a href="../records/8294716"><strong>South East Australian Coastal Ocean Forecast System (SEA-COFS)</strong></a> suite of models.</p>
Example input files and output data for 1D hydrodynamic simulations of shock compressed iron
<p>Example input files and output data for 1D hydrodynamic simulations of shock compressed iron. Input files consists of 3 examples from the SIMEX github wiki page for a 50 micron CH ablator with 5 micro Fe foil (laser pulse is a 6 ns flat top pulse, 1064 nm with 0.3 TW/cm<sup>2</sup>). Output data are from Esther hydrocode in .txt format and the SIMEX opmd.h5 format.</p>
Datasets for "Hydrodynamic and hydromagnetic energy spectra from large eddy simulations"
<pre>This directory contains an index.html file with links to the run directories and idl plotting routines with secondary data for the other figures for the paper "Hydrodynamic and hydromagnetic energy spectra from large eddy simulations" Haugen & Brandenburg. If anything turns our to be incomplete, please email brandenb@nordita.org.</pre>
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.
Three-Dimensional Hydrodynamic Simulations of Convective Nuclear Burning In Massive Stars Near Iron Core Collapse
<p>Data products from ApJ article Three-Dimensional Hydrodynamic Simulations of Convective Nuclear Burning In Massive Stars Near Iron Core Collapse, 2021. Four 3D core-collapse supernova progenitor models. Works that utilize these progenitor models are required to cite article. The models were evolved to times listed in Table 1 of the article, the collapse time according to the 1D MESA model. All 3D data are in FLASH4 format using the HDF5 data structure. </p>
General-Relativistic Hydrodynamics Simulation of a Neutron Star — Sub-Solar-Mass Black Hole Merger - 3D Ejecta Data
<p>This dataset contains the 3D output for NSbh_R2 run at refinement level l=1 and simulation time t=7950 (t=39.16 ms).</p> <p>Data: Swami Vivekanandji Chaurasia (Stockholm University), Data release packaging: Ivan Markin (University of Potsdam);</p> <p>Simulations for the project have been performed on the national supercomputer HPE Apollo Hawk at the High Performance Computing (HPC) Center Stuttgart (HLRS) under the grant number GWanalysis/44189, on the GCS Supercomputer SuperMUC NG at the Leibniz Supercomputing Centre (LRZ) [project pn29ba], and on the HPC systems Lise/Emmy of the North German Supercomputing Alliance (HLRN) [project bbp00049] for the final production runs. The particular simulation has been run on HLRN.</p>
3D hydrodynamic simulations of C ingestion into a convective O shell: Animated visualisations
<p>Animated visualisations of the 3D simulations presented by Andrassy et al. (2019), arXiv:1808.04014.</p>
General-Relativistic Hydrodynamics Simulation of a Neutron Star — Sub-Solar-Mass Black Hole Merger - 3D Density Visualization
<p>Matter density distribution of our simulation of a neutron star -- sub-solar mass black hole merger; cf. color bar to obtain a density estimate. The gray region represents the apparent horizon of the black hole.</p> <p>Visualization: Ivan Markin (University of Potsdam); Data: Swami Vivekanandji Chaurasia (Stockholm University)</p> <p>Simulations for the project have been performed on the national supercomputer HPE Apollo Hawk at the High Performance Computing (HPC) Center Stuttgart (HLRS) under the grant number GWanalysis/44189, on the GCS Supercomputer SuperMUC NG at the Leibniz Supercomputing Centre (LRZ) [project pn29ba], and on the HPC systems Lise/Emmy of the North German Supercomputing Alliance (HLRN) [project bbp00049] for the final production runs. The particular simulation shown here has been run on HLRN.</p>
General-Relativistic Hydrodynamics Simulation of a Neutron Star — Sub-Solar-Mass Black Hole Merger - Kilonova Luminosity Evolution Visualization
<p>Evolution of luminosity maps as seen by observers from the pole with Θ = 0° and from the four angles Φ = 0°, Φ = 90°, Φ = 180° and Φ = 270° in the equatorial plane with Θ = 90°. The maps show the luminosity from each region of the ejecta integrated along the line of sight and are calculated in (6000 − 8000) Å band at 1 day after the merger.</p> <p>Visualization: Ivan Markin (University of Potsdam), Mattia Bulla (University of Ferrara); Data: Anna Neuweiler (University of Potsdam), Swami Vivekanandji Chaurasia (Stockholm University)</p> <p>Simulations for the project have been performed on the national supercomputer HPE Apollo Hawk at the High Performance Computing (HPC) Center Stuttgart (HLRS) under the grant number GWanalysis/44189, on the GCS Supercomputer SuperMUC NG at the Leibniz Supercomputing Centre (LRZ) [project pn29ba], and on the HPC systems Lise/Emmy of the North German Supercomputing Alliance (HLRN) [project bbp00049] for the final production runs. The particular simulation shown here has been run on HLRN.</p>
New Mass and Distance Estimates for Betelgeuse through Combined Evolutionary, Asteroseismic, and Hydrodynamical Simulations with MESA
<p>inlists for MESA version 11701 used to compute evolutionary tracks accompanying this manuscript on Betelgeuse</p>
Data from: Understanding age-specific dispersal in fishes through hydrodynamic modelling, genetic simulations and microsatellite DNA analysis
Many marine species have vastly different capacities for dispersal during larval, juvenile and adult life stages, and this has the potential to complicate the identification of population boundaries and the implementation of effective management strategies such as marine protected areas. Genetic studies of population structure and dispersal rarely disentangle these differences and usually provide only lifetime-averaged information that can be considered by managers. We address this limitation by combining age-specific autocorrelation analysis of microsatellite genotypes, hydrodynamic modelling and genetic simulations to reveal changes in the extent of dispersal during the lifetime of a marine fish. We focus on an exploited coral reef species, Lethrinus nebulosus, which has a circum-tropical distribution and is a key component of a multispecies fishery in northwestern Australia. Conventional population genetic analyses revealed extensive gene flow in this species over vast distances (up to 1500 km). Yet, when realistic adult dispersal behaviours were modelled, they could not account for these observations, implying adult dispersal does not dominate gene flow. Instead, hydrodynamic modelling showed that larval L. nebulosus are likely to be transported hundreds of kilometres, easily accounting for the observed gene flow. Despite the vast scale of larval transport, juvenile L. nebulosus exhibited fine-scale genetic autocorrelation, which declined with age. This implies both larval cohesion and extremely limited juvenile dispersal prior to maturity. The multidisciplinary approach adopted in this study provides a uniquely comprehensive insight into spatial processes in this marine fish.
Data of paper "Grid ,Hydrodynamic boundary and Uncertainty analysis of 2D-SWEs in the context of digital twins: Taking numerical simulation of river networksas an example"
<p>论文数据 “数字孪生背景下2D-SWEs的网格、水动力边界和不确定性分析:以河流网络数值模拟为例”</p>
Simulating Wave Characteristics with Second-Order Wave Generator using Smoothed Particle Hydrodynamics (SPH)
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Grouped star formation: converting sink particles to stars in hydrodynamical simulations
<p>This repository contains the essential raw data and codes to recreate the plots in this paper (10.1093/mnras/stab3617; <a href="https://ui.adsabs.harvard.edu/abs/2022MNRAS.510.2657L/abstract">Liow et al. 2022</a>), which contains the following directories and codes:</p> <p><strong>B12/</strong></p> <ul> <li>This directory contains the initial condition (or the snapshot right before star formation) and the snapshot at 0.19 Myr for all the sub-parsec-scale isolated cluster models (see Sections 2.3.1 and 3.1). The only exception is the B12/B12/ subdirectory, which contains the sink particles at 0.19 Myr from the original simulation (Bate 2012). </li> <li>The gas, sink and star files were produced using AMUSE v2021.6.0 (Portegies Zwart et al. 2021; <a href="https://zenodo.org/record/4946130">https://zenodo.org/record/4946130</a>), while the random state and setting files were created using Ekster v1.01 (Rieder and Liow 2021; <a href="https://zenodo.org/record/5520944">https://zenodo.org/record/5520944</a>). See Ekster for more information on how to run the code.</li> </ul> <p><strong>L20/ </strong></p> <ul> <li>This directory contains the initial condition (or the snapshot right before star formation) and the snapshot at 1.75 Myr for all the parsec-scale cloud-cloud collision models (see Sections 2.3.2 and 3.2). The only exception is the L20/L20/ subdirectory, which contains the sink particles at 1.75 Myr from the original simulation (Liow and Dobbs 2020). </li> </ul> <p><strong>J+/</strong></p> <ul> <li>This directory contains the stars at 5 Myr, and stars and sinks at 20 Myr for all the parsec-scale isolated cluster models (see Sections 2.3.3 and 3.3). The only exception is the J+/J+/ subdirectory, which contains the sink particles at 5 Myr and at 20 Myr from the original simulation (Jaffa et al. 2022).</li> </ul> <p><strong>R+/</strong></p> <ul> <li>This directory contains the stars at 1.80 Myr and at 2.40 Myr for the kilo-parsec-scale spiral arm model (see Sections 2.3.4 and 3.4).</li> </ul> <p><strong>L20_Extra/</strong></p> <ul> <li>This directory contains the initial condition (or the snapshot right before star formation) and the snapshot at 1.75 Myr for all the additional parsec-scale cloud-cloud collision models to study the effect of changing random seeds on cluster properties (see Section 4.1).</li> </ul> <p><strong>B12_VG/</strong></p> <ul> <li>This directory contains the initial condition (or the snapshot right before star formation) and the snapshot at 0.19 Myr for all the additional sub-parsec-scale isolated cluster models to study the effect of varying upper star mass limits (see Section 4.2).</li> </ul> <p><strong>L20_VG/</strong></p> <ul> <li>This directory contains the initial condition (or the snapshot right before star formation) and the snapshot at 1.75 Myr for all the additional parsec-scale cloud-cloud collision models to study the effect of varying upper star mass limits (see Section 4.2).</li> </ul> <p><strong>misc/</strong></p> <ul> <li>This directory contains the miscellaneous files to produce some of the plots in the paper. </li> </ul> <p><strong>load_data.py</strong></p> <ul> <li>This python code loads the necessary sink and star files for the code 'plot.py'. </li> </ul> <p><strong>plot.py</strong></p> <ul> <li>This python code recreates the plots in the paper. See individual functions for more information.</li> </ul> <p>Please contact the authors for more information.</p>
A Hydrodynamic-Based Physical Unified Modeling for Simulating Shallow Landslide Local Failures, Mass Release and Debris Flow Run-out Extent Behavior
<p>Simulation results for manuscript "A Hydrodynamic-Based Physical Unified Modelling Framework for Simulating Shallow Landslide Local Failures, Mass Release and Debris Flow Run-out Extent Behaviour", submitted to Water Resources Research.</p>
A Hydrodynamic-Based Physical Unified Modeling for Simulating Shallow Landslide Local Failures, Mass Release and Debris Flow Run-out Extent Behavior
<p>Simulation results for manuscript "A Hydrodynamic-Based Physical Unified Modelling Framework for Simulating Shallow Landslide Local Failures, Mass Release and Debris Flow Run-out Extent Behaviour", submitted to Water Resources Research.</p>
Appendices A-B-C for Malfait et al. 2024(a), Impact of HI cooling and study of accretion disks in AGB wind-companion smoothed particle hydrodynamic simulations
<p>Appendices A, B, C with figures, of the paper Malfait et al. 2024(a) "Impact of HI cooling and study of accretion disks in AGB wind-companion smoothed particle hydrodynamic simulations".</p>
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