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30 results for “Hydrodynamic Simulations”
Intermediate-Mass & Supermassive Black Hole Feedback in Cosmological Hydrodynamical Simulations
<p>Intermediate-Mass Black Holes (IMBHs: with mass between 100 to 10^6 Msun) historically comprise of an elusive population in the Universe compared to the widely observed Stellar-Mass and Supermassive Black Holes. An increasing fraction of low-mass galaxies are observed to host IMBHs at their centers; some of which show signatures of activity in the form of low-luminosity AGN. In this context, a pertinent theory is that there are dormant IMBHs at the centers of Dwarf Galaxies, which we explore by performing small-volume Cosmological Hydrodynamical Simulations. The IMBHs are expected to be more prevalent in low-mass halos. We also perform medium-volume cosmological simulations to explore feedback from supermassive BHs. Our simulations employ a modified version of the SPH code GADGET-3, and include baryonic feedback models: radiative cooling, star formation, chemical enrichment, stellar evolution, supernova feedback, AGN accretion and AGN kinetic feedback. We investigate the growth of central IMBHs and SMBHs in galaxies employing the different volume simulations, as well as the resulting BH feedback. We quantify the impact of central massive BHs on their host galaxies; especially the effects on star formation and galaxy gas properties.</p>
General-Relativistic Hydrodynamics Simulation of a Neutron Star — Sub-Solar-Mass Black Hole Merger - Gravitational Waveform
<p>This dataset contains the gravitational waveform for NSbh simulation. See the README.txt file for details.</p> <p>Simulations: Swami Vivekanandji Chaurasia (Stockholm University);</p> <p>Postprocessing: Maximiliano Ujevic (Universidade Federal do ABC) and Adrian Abac (Max Planck Institute for Gravitational Physics);</p> <p>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>
Data of smoothed particle hydrodynamic giant impact simulations for the paper "A super-massive Neptune-sized planet"
<p>This dataset contains data from the article:</p> <p>L. Naponiello et al. 2023 "A super-massive Neptune-sized planet". DOI: https://doi.org/10.1038/s41586-023-06499-2</p> <p>The dataset comprises initial conditions and final snapshots of the smoothed particle hydrodynamics simulations selected in the paper.</p> <p>All the simulations were performed using <a href="https://swift.com">SWIFT</a> with initial condtions generated using <a href="https://github.com/srbonilla/WoMa">WoMa</a>. The planets are modeled with forsterite core (see <a href="https://github.com/ststewart/aneos-forsterite-2019">Link</a>), water middle layer (see <a href="https://ui.adsabs.harvard.edu/abs/2020A%26A...643A.105H/abstract">link</a>), and H&He mixture atmosphere top layer (see <a href="https://ui.adsabs.harvard.edu/abs/1980JGR....85..225H/abstract">link</a>).</p> <p>We simulated three different pre-impact compositions: water-rich by mass (22.5 % rock, 67.5 % water, 10 % H/He), equal rock and water (45 % rock, 45 % water, 10 % H/He), and rock-rich (67.5 % rock, 22.5 % water, 10 % H/He). These are located in the "data.zip" file under the folders "water rich", "equal_water_rock", and "rock_rich", respectively. In each simulation subfolder, "snapshot_0000.hdf5" is the initial condition file, where the target and impactor are separated by a distance that allows contact to occur 1 hour after the start of the simulation. "snapshot_00xx.hdf5" is the final snapshot of that simulation, where "xx" is the simulation time in hours. For example, "snapshot_0015.hdf5" is the snapshot taken after 15 hours of the simulation.</p>
Data from: Understanding age-specific dispersal in fishes through hydrodynamic modelling, genetic simulations and microsatellite DNA analysis
Open the record for dataset details and reuse information.
Data for the hydrodynamic, salinity and temperature 2D numerical simulation at Cádiz Bay (Spain)
<p>The dataset included in this repository was used to perform numerical simulations of hydrodynamics, salinity and temperature in the Bay of Cádiz, including the calibration and testing of the model. The simulation were performed with a 2D implementation of the Delft3D model. <br> Brief description of the files included in the dataset:</p> <ul> <li>REDEXT_T_HIS_GolfoDeCadiz is a text file with the data from buoy 2342 used to define the offshore boundary conditions for waves (Hm0, Tp, Dmd), salinity (Sa2) and temperature (Ts2), and the wind forcing (Vv_md, Dv_md). See heading for variable units. </li> <li>Field_data.mat: this file contains the field data used to calibrate and validate the model. There is one structure for each of the five instruments deployed (I1, I2, I3, I3a and I3b) with measurements from acoustic Doppler current profilers (ADCPs) and conductivity-temperature (CTs) devices. For each instruments, data are split by calibration and testing periods. The variables therein are: <ul> <li>X and Y: X and Y UTM 30S coordinates of the deployment location</li> <li>time: date of the data in julian days</li> <li>pressure: pressure data (m)</li> <li>vE and vN: East and North depth average velocities (m/s)</li> <li>sal: depth-average salinity (psu)</li> <li>temp: depth-average water temperature (ºC)</li> </ul> </li> <li>Heat_model.mat: a file containing the variables needed for the heat model. The values correspond to the closest station to the Cádiz bay and were considered as representative of the condition therein. The variables are: <ul> <li>Time: date in Julian days</li> <li>Cloudiness: daily-average cloudiness (%)</li> <li>Humidity: daily-average atmospheric relative humidity (%)</li> <li>Radiation: daily-average (short wave) solar radiation (W/m2)</li> <li>Temperature: daily-average air surface temperature (ºC)</li> </ul> </li> <li>Flow_wave.zip: a zip file containing the necessary files to run the 13-month Delft3D simulation (October 2012 to November 2013) with online coupling of the Flow and Wave modules, including the harmonics of the astronomical tide in the .bca file</li> <li>Flow.zip: a zip file containing the necessary files to run the 13-month Delft3D simulation (October 2012 to November 2013) with only the Flow module activated. </li> </ul> <p>Some of the files are binary (.mat format) but can be easily read using open source tools (e.g., Python or Octave).</p> <p>The research leading to this dataset was founded by the Bay of Cadiz Port Authority and the Department of Innovation, Science and Business of the Andalusian Regional Government (Project P09-TEP-4630), the Spanish Ministry of Economy and Competitiveness through the Projects CTM2017-89531-R (PIRATES), CTM2017-82274-R (MICROBAHIA2) and by the <em>“Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo”</em>, CYTED (project PROTOCOL 917PTE0538). The authors would also thank to the staff of the Environmental Fluid Dynamics research group (GDFA, University of Granada) for their support during the field survey. The first author was partially funded by the Andalusian Regional Government, Research Grant RNM-6352.</p>
Multi-dimensional hydrodynamic simulations of stellar objects
<p>Supplementary material for the ArbeitsgruppenInspirationsMesse event</p>
Stellar models with calibrated convection and temperature stratification from 3D hydrodynamics simulations
<p>MESA T-tau data file associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018MNRAS.478.5650M/abstract">Stellar models with calibrated convection and temperature stratification from 3D hydrodynamics simulations</a></p>
Data for "Impact of HI cooling and study of accretion disks in AGB wind-companion smoothed particle hydrodynamic simulations"
<p>Additional material to Malfait et al. 2024(a) "Impact of HI cooling and study of accretion disks in AGB wind-companion smoothed particle hydrodynamic simulations"</p> <p>This contains input files and final output dumps of the Phantom simulations of this paper, and some movies to illustrate the complex flows within the binary systems.</p> <p>The code used to perform the simulations is available at: <a href="https://github.com/danieljprice/phantom">https://github.com/danieljprice/phantom.</a></p> <p>Splash (<a href="https://github.com/danieljprice/splash">https://github.com/danieljprice/splash</a> ) and Plons (<a href="https://github.com/Ensor-code/plons">https://github.com/Ensor-code/plons</a> ) were used to create figures and plots from this data.</p> <p> </p>
Example simulation showing spatial and temporal variations in surface carbon biomass of plankton functional groups during a Spring bloom as shown by a 3D hydrodynamic-biogeochemical model (FVCOM-ERSEM), with and without integration of the mixoplankton paradigm.
<p>The outputs are from simulations from using the FVCOM hydrodynamic model coupled to two different versions of ERSEM – (i) ERSEM and (ii) ERSEM-PB (the latter includes the implementation of the mixoplankton paradigm through integration of the 'Perfect Beast' PB model; Flynn and Mitra 2009 <em>Journal of Plankton Research</em>).</p> <p>The FVCOM domain was configured to represent Lyme Bay: a protected bay on the South Coast of England. This region is an important area for shellfish aquaculture. The domain was configured at 350 m – 5 km high-resolution, resolving sub-km scale dynamics in the area. A nested modelling approach of increasing model resolution was set up using two model domains. For the coupled hydrodynamic-biogeochemical model, a parent domain of 1.5 km – 10 km resolution was used to drive Lyme Bay model domain. The atmospheric forcing was provided by a 3-step downscaling of GFS global datasets to reach the 3 km of the final model domain using the Weather Research Forecast (WRF) model. Hydrodynamic boundary conditions are extracted from the European Copernicus Marine System North West European Shelf Forecast system. River flows were extracted from a National scale hydrology model run by the Center for Hydrology and Ecology in the UK. Simulations were initialised at Jan 1<sup>st</sup> 2005, and spun up for 3 months prior to the output of the data visualised in these videos. </p> <p>The 6 videos portray spatial and temporal variation of daily averaged surface carbon biomass (μgC L<sup>-1</sup>) during the month of April 2005 for the different plankton functional types (FTs) as follows:</p> <ul> <li>Video 1: all phytoplankton FTs in standard ERSEM grouped together. These thus include diatoms, nano-, pico- and micro- plankton; i.e., these simulations do not discriminate between phytoplankton and constitutive mixoplankton (CM).</li> <li>Video 2: phytoplankton FT in ERSEM-PB now considering only diatoms and picoplankton (i.e., cyanobacteria) only; CM are now included in Video 3 outputs.</li> <li>Video 3: all mixoplankton FTs grouped together in ERSEM-PB. These outputs thus include biomasses of micro-CM, nano-CM and NCM.</li> <li>Video 4: all zooplankton FTs grouped together in standard ERSEM. Thus, these include nanoflagellates, meso- and micro- zooplankton and thus includes the primary producing non-constitutive mixoplankton</li> <li>Video 5: zooplankton FT representing only the heterotrophic nano- and micro- zooplankton in ERSEM-PB.</li> <li>Video 6: spatio-temporal variability between the constitutive and non-constitutive mixoplankton functional groupings within FVCOM-ERSEM-PB. </li> </ul> <p>For further information about the mixoplankton paradigm, please see the following open access publications and references there in:</p> <p>Mitra A, Caron DA, Faure E, Flynn KJ, Leles SG, Hansen PJ, McManus GB, Not F, Gomes HR, Santoferrara L, Stoecker DK, Tillmann U (2023) <strong>The Mixoplankton Database – diversity of photo-phago-trophic plankton in form, function and distribution across the global ocean</strong>. <em>Journal of Eukaryotic Microbiology</em>, e12972. <a href="https://doi.org/10.1111/jeu.12972">https://doi.org/10.1111/jeu.12972</a></p> <p>Glibert PM, Mitra A (2022) <strong>From webs, loops, shunts, and pumps to microbial multitasking: evolving concepts of marine microbial ecology, the mixoplankton paradigm, and implications for a future ocean</strong>. <em>Limnology and Oceanography</em> 67: 585-597 <a href="https://doi.org.10.1002/lno.12018">https://doi.org.10.1002/lno.12018</a> </p> <p>Mitra A, Irigoien X (2022) <strong>Mixoplankton – Marine Organisms that break the rules</strong>. EU Researcher. <a href="https://issuu.com/euresearcher/docs/mixitin_eur28_h_res">https://issuu.com/euresearcher/docs/mixitin_eur28_h_res</a> </p> <p>Flynn KJ, Mitra A, Anestis K, Anschütz AA, Calbet A, et al. (2019) <strong>Mixotrophic protists and a new paradigm for marine ecology: where does plankton research go now?</strong> <em>Journal of Plankton Research</em> 41: 375-391 <a href="https://doi.org/10.1093/plankt/fbz026">https://doi.org/10.1093/plankt/fbz026</a></p>
A Hydrodynamic-Based Physical Unified Modelling Framework for Simulating Shallow Landslide Local Failures, Mass Release and Debris Flow Run-out Extent Behaviour
<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>
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