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41 results for “large-eddy simulation”
The Plunging of Hyperpycnal Plumes on Tilted Bed by Three-Dimensional Large-Eddy Simulations
<p><strong>Abstract:</strong> Theoretical and experimental interest in the transport and deposition of sediments from rivers to oceans has increased rapidly over the last two decades. The marine ecosystem is strongly affected by mixing at river mouths, with for instance anthropogenic actions like pollutant spreading. Particle-laden flows entering a lighter ambient fluid (hyperpycnal flows) can plunge at a sufficient depth, and their deposits might preserve a remarkable record across a variety of climatic and tectonic settings. Numerical simulations play an essential role in this context since they provide information on all flow variables for any point of time and space. This work offers valuable Spatio-temporal information generated by turbulence-resolving 3D simulations of poly-disperse hyperpycnal plumes over a tilted bed. The simulations are performed with the high-order flow solver Xcompact3d, which solves the incompressible Navier-Stokes equations on a Cartesian mesh using high-order finite-difference schemes. Five cases are presented, with different values for flow discharge and sediment concentration at the inlet. A detailed comparison with experimental data and analytical models is already available in the literature. The main objective of this work is to present a new data-set that shows the entire three-dimensional Spatio-temporal evolution of the plunge phenomenon and all the relevant quantities of interest.</p> <p><strong>Description:</strong> Data from the five simulations are included (cases 2, 4, 5, 6, and 7). The output files from Xcompact3d were converted to NetCDF, including coordinates and metadata, aiming to be more friendly than raw binaries.</p> <p>More details, including examples about how to read and plot the dataset using Python and xarray, are available at <a href="https://github.com/fschuch/the-plunging-flow-by-3D-LES">GitHub</a>.</p>
Large-eddy simulation investigating the role of double-diffusive convection in basal melting of Antarctic ice shelves: model output
<p>Model output used in the publication:</p> <p>M. G. Rosevear, B. Gayen, B. K. Galton-Fenzi, The role of double-diffusive convection in the basal melting of Antarctic ice shelves. <em>Proc. Natl. Acad. Sci. </em>(2021) https://doi.org/10.1073/pnas.207541118</p> <p>See README.md for a description of the data.</p>
Large-Eddy Simulation of Wind Turbine Flows: A New Evaluation of Actuator Disk Models - Dataset
<p>Main data used in the following paper: Revaz, T.; Porté-Agel, F. Large-Eddy Simulation of Wind Turbine Flows: A New Evaluation of Actuator Disk Models. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 3745. https://doi.org/10.3390/en14133745</p>
3D output of idealized large-eddy simulations with varying speed and surface heating to assess Doppler lidar scan patterns
<p><span>This dataset consists of nine idealized large-eddy simulations that were designed to systematically investigate the ability of different Doppler lidar scan patterns to measure the 3-dimensional wind vector at one point or in one profile. For more information, please see the documentation.</span></p>
Dataset for Paper Titled "First assessment of cloud-land coupling in LASSO Large-Eddy Simulations"
<div>The attached two files were used for analysis in the paper "First assessment of cloud-land coupling in LASSO Large-Eddy Simulations." The NetCDF file included planetary boundary layer heights derived from lidar and radiosondes. The CSV file detailed the model configurations for selected case days in simulation sets ID1-5.</div> <div> <div> <p> </p> </div> </div>
PPMLES – Perturbed-Parameter ensemble of MUST Large-Eddy Simulations
<h2>Dataset description</h2> <p>This repository contains the PPMLES (Perturbed-Parameter ensemble of MUST Large-Eddy Simulations) dataset, which corresponds to the main outputs of 200 large-eddy simulations (LES) of microscale pollutant dispersion that replicate the MUST field experiment [Biltoft. 2001, Yee and Biltoft. 2004] for varying meteorological forcing parameters.</p> <p>The goal of the PPMLES dataset is to provide a comprehensive dataset to better understand the complex interactions between the atmospheric boundary layer (ABL), the urban environment, and pollutant dispersion. It was originally used to assess the impact of the meteorological uncertainty on microscale pollutant prediction and to build a surrogate model that can replace the costly LES model [Lumet et al. 2025]. The total computational cost of the PPMLES dataset is estimated to be about 6 million core hours.</p> <p>For each sample of meteorological forcing parameters (inlet wind direction and friction velocity), the <a href="https://www.cerfacs.fr/avbp7x/">AVBP</a> solver code [Schonfeld and Rudgyard. 1999, Gicquel et al. 2011] was used to perform LES at very high spatio-temporal resolution (1e-3s time step, 30cm discretization length) to provide a fine representation of the pollutant concentration and wind velocity statistics within the urban-like canopy. The total computational cost of the PPMLES dataset is estimated to be about 6 million core hours.</p> <h2>File list</h2> <p>The data is stored in <a href="https://www.hdfgroup.org/solutions/hdf5/">HDF5</a> files, which can be efficiently processed in Python using the <a href="https://docs.h5py.org/en/stable/index.html">h5py</a> module. </p> <ul> <li><em>input_parameters.h5:</em> list of the 200 input parameter samples<em> (alpha_inlet, ustar) </em>obtained using the Halton sequence that defines the PPMLES ensemble.</li> <li><em>ave_fields.h5</em>: lists of the main field statistics predicted by each of the 200 LES samples over the 200-s reference window [Yee and Biltoft. 2004], including: <ul> <li><em>c:</em> the time-averaged pollutant concentration in ppmv <em>(dim = (n_samples, n_nodes) = (200, 1878585))</em>, </li> <li><em>(u, v, w): </em>the time-averaged wind velocity components in m/s,</li> <li><em>crms: </em>the root mean square concentration fluctuations in ppmv, </li> <li><em>tke:</em> the turbulent kinetic energy in m^2/s^2,</li> <li><em>(uprim_cprim, vprim_cprim, wprim_cprim)</em>: the pollutant turbulent transport components</li> </ul> </li> <li><em>uncertainty.h5</em>: lists of the estimated aleatory uncertainty induced by the internal variability of the LES <em>(variability_#)</em> [Lumet et al. 2024] for each of the fields in <em>ave_fields.h5</em>. Also includes the stationary bootstrap [Politis and Romano. 1994] parameters <em>(n_replicates, block_length) </em>used to estimate the uncertainty for each field and each sample.</li> <li><em>mesh.h5</em>: the tetrahedral mesh on which the fields are discretized, composed of about 1.8 millions of nodes.</li> <li><em>time_series.h5</em>: HDF5 file consisting of 200 groups (<em>Sample_NNN</em>) each containing the time series of the pollutant concentration (c) and wind velocity components (u, v, w) predicted by the LES sample #NNN at 93 locations. </li> <li><em>probe_network.dat</em>: provides the location of each of the 93 probes corresponding to the positions of the experimental campaign sensors [Biltoft. 2001].</li> </ul> <p><strong>Warning:</strong> the propylene concentration are expressed in ppmv, except in time_series.h5 in which they are given as mass fractions. To convert them in ppmv, the formula is: <code>c = c * (rho/rho_propylene) * 10**6</code> with (rho/rho_propylene) = 0.66 the density ratio between air and propylene.</p> <h2>Code examples</h2> <p>In the following, examples of how to use the PPMLES dataset in Python are provided. These examples have the following dependencies: </p> <pre><code>requires-python = ">=3.9" dependencies = [ "h5py==3.8.0", "numpy==1.26.4", "scipy", ]</code></pre> <h3>A) Dataset reading</h3> <div> <div> <pre><code>### Imports import h5py import numpy as np ### Load the input parameters list into a numpy array (shape = (200, 2)) inputf = h5py.File('PPMLES/input_parameters.h5', 'r') input_parameters = np.array((inputf['alpha_inlet'], inputf['friction_velocity'])).T<br>### Load the domain mesh node coordinates<br>meshf = h5py.File('../PPMLES/mesh.h5', 'r')<br>mesh_nodes = np.array((meshf['Nodes']['x'], meshf['Nodes']['y'], meshf['Nodes']['z'])).T ### Load the set of time-averaged LES fields and their associated uncertainty var = 'c' # Can be: 'c', 'u', 'v', 'w', 'crms', 'tke', 'uprim_cprim', 'vprim_cprim', or 'wprim_cprim' fieldsf = h5py.File('PPMLES/ave_fields.h5', 'r') fields_list = fieldsf[var] uncertaintyf = h5py.File('PPMLES/uncertainty_ave_fields.h5', 'r') uncertainty_list = uncertaintyf[var] ### Time series reading example timeseriesf = h5py.File('PPMLES/time_series.h5', 'r') var = 'c' # Can be: 'c', 'u', 'v', or 'w' probe = 32 # Integer between 0 and 92, see probe_network.csv time_list = [] time_series_list = [] for i in range(200): time_list.append(np.array(timeseriesf[f'Sample_{i+1:03}']['time'])) time_series_list.append(np.array(timeseriesf[f'Sample_{i+1:03}'][var][probe]))</code></pre> </div> </div> <h3>B) Interpolation of one-field from the unstructured grid to a new structured grid</h3> <pre><code>### Imports import h5py import numpy as np from scipy.interpolate import griddata ### Load the mean concentration field sample #028 fieldsf = h5py.File('PPMLES/ave_fields.h5', 'r') c = fieldsf['c'][27] ### Load the unstructured grid meshf = h5py.File('PPMLES/mesh.h5', 'r') unstructured_nodes = np.array((meshf['Nodes']['x'], meshf['Nodes']['y'], meshf['Nodes']['z'])).T ### Structured grid definition x0, y0, z0 = -16.9, -115.7, 0. lx, ly, lz = 205.5, 232.1, 20. resolution = 0.75 x_grid, y_grid, z_grid = np.meshgrid(np.linspace(x0, x0 + lx, int(lx/resolution)), np.linspace(y0, y0 + ly, int(ly/resolution)), np.linspace(z0, z0 + lz, int(lz/resolution)), indexing='ij') ### Interpolation of the field on the new grid c_interpolated = griddata(unstructured_nodes, c, (x_grid.flatten(), y_grid.flatten(), z_grid.flatten()), method='nearest')</code></pre> <h3>C) Expression of all time series over the same time window with the same time discretization</h3> <pre><code>### Imports import h5py import numpy as np from scipy.interpolate import griddata ### Define a common time discretization over the 200-s analysis period common_time = np.arange(0., 200., 0.05) u_series_list = np.zeros((200, np.shape(common_time)[0])) ### Interpolate the u-compnent velocity time series at probe DPID10 over this time discretization timeseriesf = h5py.File('PPMLES/time_series.h5', 'r') for i in range(200): sample_time = np.array(timeseriesf[f'Sample_{i+1:03}']['time']) - \ np.array(timeseriesf[f'Sample_{i+1:03}']['Parameters']['t_spinup']) # Offset the spinup time u_series_list[i] = griddata(sample_time, timeseriesf[f'Sample_{i+1:03}']['u'][9], common_time, method='linear')</code></pre> <h3>D) Surrogate model construction example</h3> <p>The training and validation of a POD-GPR surrogate model [Marrel et al. 2015] learning from the PPMLES dataset is given in the following <a href="https://github.com/eliott-lumet/pod_gpr_ppmles">GitHub repository</a>. This surrogate model was successfully used by Lumet et al. 2025 to emulate the LES mean concentration prediction for varying meteorological forcing parameters.</p> <h2>Acknowledgments</h2> <p>This work was granted access to the HPC resources from GENCI-TGCC/CINES (A0062A10822, project 2020-2022). The authors would like to thank Olivier Vermorel for the preliminary development of the LES model, and Simon Lacroix for his proofreading.</p>
Data from Large-eddy simulation data of stratocumulus advecting over cold and warm waters
<p>Data from Large-eddy simulation data of stratocumulus advecting over cold and warm waters. The LES model used is the System for Atmospheric Modeling (SAM) model (http://rossby.msrc.sunysb.edu/~marat/SAM.html). </p>
Supplementary Material: A Large-Eddy Simulation Study of Vertical Axis Wind Turbine Wakes in the Atmospheric Boundary Layer
<p>Supplementary material for <em>Energies</em> <strong>2016</strong>, <em>9</em>, 366; doi:10.3390/en9050366:</p> <p><strong>Video S1:</strong> Normalized instantaneous streamwise velocity field both on a vertical plane (<em>x</em>-<em>z</em>) going through the center of the turbine and on a horizontal plane at the equator height of the turbine (Note: the physical time corresponding to this video is 1 minute and 17 seconds, and the size of the blades is magnified for illustration purposes).</p> <p><strong>Video S2:</strong> Normalized instantaneous streamwise velocity field on a horizontal plane at the equator height of the turbine for two cases: when the turbine starts to operate (top) and when the flow has reached statistically steady condition (bottom) (Note: the physical time corresponding to both videos is 1 minute and 17 seconds, and the size of the blades is magnified for illustration purposes).</p>
A Stereo Camera Simulator for Large-Eddy Simulations of Continental Shallow Cumulus clouds based on three-dimensional Path-Tracing
<p>Dataset to produce the results of the publication: "A Stereo Camera Simulator for Large-Eddy Simulations of Continental Shallow Cumulus clouds based on three-dimensional Path-Tracing"</p><p>The dataset contains:</p><ul><li>Large-Eddy Simulation (LES) model configuration files</li><li>Selected output data of the LES experiments</li><li>Data and analysis scripts for the figures</li><li>The rendered camera images</li><li>The cloud field, cloud hulls, and reconstructed hulls</li><li>A frozen version of the open-source Blender code (version 2.90) as used in this study</li></ul><p>For the latest version of Blender, please visit:</p><p><a href="https://chat.openai.com/c/www.blender.org">www.blender.org</a></p><p>It is important to note that the method was specifically tested only on version 2.90.</p><p> </p><p>This research is supported by the German Research Foundation (DFG) under project number 430226822 (https://gepris.dfg.de/gepris/projekt/430226822). This research was supported by the U.S. Department of Energy's Atmospheric System Research, an Office of Science Biological and Environmental Research program, under grant DE-SC0022126. This work used resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project ID bb1086. The Gauss Centre for Supercomputing e.V. (https://www.gauss-centre.eu/) is acknowledged for providing computing time on the Gauss Centre for Supercomputing (GCS) supercomputer JUWELS at the Jülich Supercomputing Centre (JSC) under projects VIRTUALLAB and RCONGM.</p>
Data for iPyCLES v1.0: A New Isotope-Enabled Large-Eddy Simulator for Mixed-Phase Clouds
Open the record for dataset details and reuse information.
Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 1 of 6
<p>Numerical Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 1 / 6<br>Authors: Diego F. Abreu, João Luiz F. Azevedo, Carlos Junqueira-Junior</p> <p>This database presents unsteady calculated data derived from large-eddy simulations (LES) of a supersonic jet flow utilizing the FLEXI solver (https://numericsresearchgroup.org/codes.html#codes_flexi). <br>The operational parameters for the jet flow include a Mach number of 1.4 and a Reynolds number of 1.58E6 referenced to the nozzle exit diameter, corresponding to a perfectly expanded supersonic condition. The pressure and temperature of the jet flow match those of the surrounding ambient conditions.</p> <p>The dataset originates from six numerical simulations employing various mesh resolutions and polynomial orders, along with different boundary conditions. These calculations were performed to investigate the impact of mesh resolution, polynomial order, and boundary conditions on LES of the supersonic jet flow in the absence of nozzle effects. The database encompasses a collection of probes and planes extracted from the 3-D domain as outlined in the attached README.md file. </p> <p>For further details regarding these probes and planes, as well as information on the numerical simulations, please refer to the supplemental-material-database.pdf file. <br>The database is divided into six parts. The present set of data is number one.</p> <p>This database is associated with the manuscript entitled "Assessment of Jet Inflow Condition on the Development of Supersonic Jet Flows". The numerical data presented herein were previously published in the work entitled "Accuracy Assessment of Discontinuous Galerkin Spectral Element Method in Simulating Supersonic Free Jets" (https://doi.org/10.1007/s40430-024-04788-z) and the Ph.D. Thesis "Study of Turbulent Supersonic Jet Flows and the Influence of Nozzle-Exit Boundary Conditions on the Jet Initial Development". </p>
Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 6 of 6
<p>Numerical Database from Large-Eddy Simulations of a Supersonic Jet Flow - Database 6 / 6<br>Authors: Diego F. Abreu, João Luiz F. Azevedo, Carlos Junqueira-Junior</p> <p>This database presents unsteady calculated data derived from large-eddy simulations (LES) of a supersonic jet flow utilizing the FLEXI solver (https://numericsresearchgroup.org/codes.html#codes_flexi). <br>The operational parameters for the jet flow include a Mach number of 1.4 and a Reynolds number of 1.58E6 referenced to the nozzle exit diameter, corresponding to a perfectly expanded supersonic condition. <br>The pressure and temperature of the jet flow match those of the surrounding ambient conditions.</p> <p>The dataset originates from six numerical simulations employing various mesh resolutions and polynomial orders, along with different boundary conditions. <br>These calculations were performed to investigate the impact of mesh resolution, polynomial order, and boundary conditions on LES of the supersonic jet flow in the absence of nozzle effects. <br>The database encompasses a collection of probes and planes extracted from the 3-D domain as outlined in the attached README.md file. <br>For further details regarding these probes and planes, as well as information on the numerical simulations, please refer to the supplemental-material-database.pdf file. <br>The database is divided into six parts. The present set of data is number six.</p> <p>This database is associated with the manuscript entitled "Assessment of Jet Inflow Condition on the Development of Supersonic Jet Flows". <br>The numerical data presented herein were previously published in the Ph.D. Thesis "Study of Turbulent Supersonic Jet flows and the Influence of Nozzle-Exit Boundary Conditions on the Jet Initial Development".</p>
Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 5 of 6
<p>Numerical Database from Large-Eddy Simulations of a Supersonic Jet Flow - Database 5 / 6 (Continuation of https://doi.org/10.5281/zenodo.13902381 database)<br>Authors: Diego F. Abreu, João Luiz F. Azevedo, Carlos Junqueira-Junior</p> <p>This database presents unsteady calculated data derived from large-eddy simulations (LES) of a supersonic jet flow utilizing the FLEXI solver (https://numericsresearchgroup.org/codes.html#codes_flexi). <br>The operational parameters for the jet flow include a Mach number of 1.4 and a Reynolds number of 1.58E6 referenced to the nozzle exit diameter, corresponding to a perfectly expanded supersonic condition. <br>The pressure and temperature of the jet flow match those of the surrounding ambient conditions.</p> <p>The dataset originates from six numerical simulations employing various mesh resolutions and polynomial orders, along with different boundary conditions. <br>These calculations were performed to investigate the impact of mesh resolution, polynomial order, and boundary conditions on LES of the supersonic jet flow in the absence of nozzle effects. <br>The database encompasses a collection of probes and planes extracted from the 3-D domain as outlined in the attached README.md file. <br>For further details regarding these probes and planes, as well as information on the numerical simulations, please refer to the supplemental-material-database.pdf file. <br>The database is divided into six parts. The present set of data is number five.</p> <p>This database is associated with the manuscript entitled "Assessment of Jet Inflow Condition on the Development of Supersonic Jet Flows". <br>The numerical data presented herein were previously published in the Ph.D. Thesis "Study of Turbulent Supersonic Jet flows and the Influence of Nozzle-Exit Boundary Conditions on the Jet Initial Development".</p>
Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 2 of 6
<p>Numerical Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 2 / 6 (Continuation of https://doi.org/10.5281/zenodo.13902381 database)<br>Authors: Diego F. Abreu, João Luiz F. Azevedo, Carlos Junqueira-Junior</p> <p>The operational parameters for the jet flow include a Mach number of 1.4 and a Reynolds number of 1.58E6 referenced to the nozzle exit diameter, corresponding to a perfectly expanded supersonic condition. The pressure and temperature of the jet flow match those of the surrounding ambient conditions.</p> <p>The dataset originates from six numerical simulations employing various mesh resolutions and polynomial orders, along with different boundary conditions. These calculations were performed to investigate the impact of mesh resolution, polynomial order, and boundary conditions on LES of the supersonic jet flow in the absence of nozzle effects. The database encompasses a collection of probes and planes extracted from the 3-D domain as outlined in the attached README.md file.</p> <p>For further details regarding these probes and planes, as well as information on the numerical simulations, please refer to the supplemental-material-database.pdf file.<br>The database is divided into six parts. The present set of data is number one.</p> <p>This database is associated with the manuscript entitled "Assessment of Jet Inflow Condition on the Development of Supersonic Jet Flows". The numerical data presented herein were previously published in the work entitled "Accuracy Assessment of Discontinuous Galerkin Spectral Element Method in Simulating Supersonic Free Jets" (https://doi.org/10.1007/s40430-024-04788-z) and the Ph.D. Thesis "Study of Turbulent Supersonic Jet Flows and the Influence of Nozzle-Exit Boundary Conditions on the Jet Initial Development".</p>
Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 4 of 6
<div> <p>Numerical Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 4 / 6 (Continuation of https://doi.org/10.5281/zenodo.13902381 database)<br>Authors: Diego F. Abreu, João Luiz F. Azevedo, Carlos Junqueira-Junior</p> <p>The operational parameters for the jet flow include a Mach number of 1.4 and a Reynolds number of 1.58E6 referenced to the nozzle exit diameter, corresponding to a perfectly expanded supersonic condition. The pressure and temperature of the jet flow match those of the surrounding ambient conditions.</p> <p>The dataset originates from six numerical simulations employing various mesh resolutions and polynomial orders, along with different boundary conditions. These calculations were performed to investigate the impact of mesh resolution, polynomial order, and boundary conditions on LES of the supersonic jet flow in the absence of nozzle effects. The database encompasses a collection of probes and planes extracted from the 3-D domain as outlined in the attached README.md file.</p> <p>For further details regarding these probes and planes, as well as information on the numerical simulations, please refer to the supplemental-material-database.pdf file.<br>The database is divided into six parts. The present set of data is number one.</p> <p>This database is associated with the manuscript entitled "Assessment of Jet Inflow Condition on the Development of Supersonic Jet Flows". The numerical data presented herein were previously published in the work entitled "Accuracy Assessment of Discontinuous Galerkin Spectral Element Method in Simulating Supersonic Free Jets" (https://doi.org/10.1007/s40430-024-04788-z) and the Ph.D. Thesis "Study of Turbulent Supersonic Jet Flows and the Influence of Nozzle-Exit Boundary Conditions on the Jet Initial Development".</p> <p> </p> </div>
Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 3 of 6
<p>Numerical Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 3 / 6 (Continuation of https://doi.org/10.5281/zenodo.13902381 database)<br>Authors: Diego F. Abreu, João Luiz F. Azevedo, Carlos Junqueira-Junior</p> <p>The operational parameters for the jet flow include a Mach number of 1.4 and a Reynolds number of 1.58E6 referenced to the nozzle exit diameter, corresponding to a perfectly expanded supersonic condition. The pressure and temperature of the jet flow match those of the surrounding ambient conditions.</p> <p>The dataset originates from six numerical simulations employing various mesh resolutions and polynomial orders, along with different boundary conditions. These calculations were performed to investigate the impact of mesh resolution, polynomial order, and boundary conditions on LES of the supersonic jet flow in the absence of nozzle effects. The database encompasses a collection of probes and planes extracted from the 3-D domain as outlined in the attached README.md file.</p> <p>For further details regarding these probes and planes, as well as information on the numerical simulations, please refer to the supplemental-material-database.pdf file.<br>The database is divided into six parts. The present set of data is number one.</p> <p>This database is associated with the manuscript entitled "Assessment of Jet Inflow Condition on the Development of Supersonic Jet Flows". The numerical data presented herein were previously published in the work entitled "Accuracy Assessment of Discontinuous Galerkin Spectral Element Method in Simulating Supersonic Free Jets" (https://doi.org/10.1007/s40430-024-04788-z) and the Ph.D. Thesis "Study of Turbulent Supersonic Jet Flows and the Influence of Nozzle-Exit Boundary Conditions on the Jet Initial Development".</p>
Large-eddy simulation source code and data for (LS)2D reference publication in JAMES.
<p>This archive contains the MicroHH large-eddy simulation source code, the (LS)2D source code, and all simulation input and statistics, used for the publication:</p> <p><em>"The Benefits and Challenges of Downscaling a Global Reanalysis with Doubly-Periodic Large-Eddy Simulations" </em>by B.J.H. van Stratum et al.</p>
Diurnal cycle of the semi-direct effect from a persistent absorbing aerosol layer over marine stratocumulus in large-eddy simulations: supporting dataset
<p>Relevant data for reproducing figures and tables from Herbert et al., 2020, Atmospheric Chemistry and Physics: "Diurnal cycle of the semi-direct effect from a persistent absorbing aerosol layer over marine stratocumulus in large-eddy simulations".</p> <p>The tar file contains the relevant netcdf files that contain simulation output from the MET Office large eddy model (LEM). Each netcdf file corresponds to a single model setup and experiment - details of which can be found within the published manuscript. Information concerning the LEM, and details on how to obtain access to the code, can be found at http://appconv.metoffice.com/LEM/index.html.</p> <p>The file 'relevant_information.pdf' contains two tables. The first provides the names of each netcdf file that was used to produce each figure in the manuscript. The second provides the metadata for the netcdf variables that were used to produce the figures and tables.</p> <p>The netcdf filename provides information on the experiment setup as follows:</p> <p>run-identifier-number _ length-of-run _ AOD-of-layer _ GAP-between-cloud-and-layer _ THICKNESS-of-layer _ 'bigarray' _ special-setups-for-sensitivity-experiments</p> <p>For example, r1360_8day_transient_aod02_0dz_250th_bigarray_nodrizz is run number r1360, simulating 8 days, with the aerosol layer properties AOD=0.2, cloud-to-aerosol gap of 0m, layer thickness of 250m, in the sensitivity setup where precipitation has been switched off.</p>
High-CAPE summer convection in large-domain large-eddy simulations with ICON - model and observational data sets
<p>Data sets including all observational and ICON model data for publication in Atmosperic Chemistry and Physics Journal (ACP) - "High-CAPE summer convection in large-domain large-eddy simulations with ICON"</p>
Dataset: Large-eddy simulation of the ice shelf-ocean boundary layer model output
<p>This repository contains large-eddy simulation output from the CFD model <em>Diablo</em>. The simulations are of the boundary layer beneath a melting ice shelf. This model output underpins the submitted manuscript <em>Regimes and transitions in the basal melting of Antarctic ice shelves</em> submitted to the <em>Journal of Physical Oceanography</em> (December 2021).</p>
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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)
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DANDI Archive for NWB datasets
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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.