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70 results for “Large Eddy Simulations”
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>
Relative Random Errors in the Convective Atmospheric Boundary Layer Estimated by the Relaxed Filtering Method from Large Eddy Simulations
<p>Data supporting the paper "How representative are uncrewed aircraft system measurements of the convective boundary layer?" by Brian R. Greene, Leia M. Otterstatter, and Scott T. Salesky, submitted to Geophysical Research Letters in 2024. Data are postprocessed from large-eddy simulations of the convective atmospheric boundary layer that are used to produce the figures within the paper. Details on the production of these files are included in the supplementary informatin of this paper.</p>
Comparison of Large Eddy Simulations against measurements from the Lillgrund offshore wind farm - Manuscript data
<p>Time averaged power and farm inflow velocity for the manuscript "Comparison of Large Eddy Simulations against measurements from the Lillgrund offshore wind farm" for publication in the wind energy science journal. Data is uploaded for the 5 simulation cases covered.</p> <p>'Power' files contain average power production for 48 turbines. First row corresponds to LES data, second row corresponds to SCADA data from the Lillgrund wind farm.</p> <p>'Velocity' files contain inflow mean velocity measurements at the 72 range gate locations. First row corresponds to LES inflow data, second row corresponds to LIDAR inflow data from the Lillgrund wind farm.</p>
Idealized large eddy model simulations
<p>This dataset contains matlab and grads output of four large eddy model simulations of the atmospheric boundary layer in Matlab data format. They are used to create figures presented in a paper titled "Large eddy simulations of diurnal entrainment/detrainment of the shallow cumulus clouds in subtropical Western Pacific Ocean". The matlabscripts and figures are in the les_scripts_figs.tar.gz file. Install the *.mat files and dropsonde netcdf files in the following "data" directory tree structure, then the figures can be recreated by running the matlabscripts. The *.ctl and *.dat files are the grads output that contains a 10 min interval statistics of model variables, i.e. stats_sw.dat for 3D variables and stats_sfc.dat for 2D surface variables of these LES experiments. The name of the variables are listed in their corresponding *.ctl files. </p> <p> >ls data<br> CAMP2EX_AVAPS_RD41_v1_20190927_212721.nc L2<br> CAMP2EX_AVAPS_RD41_v1_20190927_222459.nc L2sst<br> CAMP2EX_AVAPS_RD41_v1_20190928_004304.nc les_grads_L1sst.tar.gz<br> cld_iso.mat les_grads_L1.tar.gz<br> cld_mat.tar.gz les_grads_L2sst.tar.gz<br> cldtb_ent_det.mat les_grads_L2.tar.gz<br> L1 mse_cld_entr.mat<br> L1sst tke_cld_all.mat</p> <p>The L1, L1sst, L2, and L2sst are the subdirectories under data. Install the following files in their corresponding experiment subdirectory. </p> <p>>ls data/L1<br> L1_2721.mat L1_qc_w_tke.mat stats_sm.ctl stats_sw.dat<br> L1_cld_depth.mat stats_sfc.ctl stats_sm.dat<br> L1_mse_cld_entr.mat stats_sfc.dat stats_sw.ctl</p> <p>>ls data/L1sst<br> L1sst_2721.mat stats_sfc.ctl stats_sm.dat<br> L1sst_cld_depth.mat stats_sfc.dat stats_sw.ctl<br> L1sst_mse_cld_entr.mat stats_sm.ctl stats_sw.dat</p> <p>>ls data/L2<br> L2_2723.mat stats_sfc.ctl stats_sm.dat<br> L2_cld_depth.mat stats_sfc.dat stats_sw.ctl<br> L2_mse_cld_entr.mat stats_sm.ctl stats_sw.dat</p> <p>>ls data/L2sst<br> L2sst_2723.mat stats_sfc.ctl stats_sm.dat<br> L2sst_cld_depth.mat stats_sfc.dat stats_sw.ctl<br> L2sst_mse_cld_entr.mat stats_sm.ctl stats_sw.dat<br> </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>
PALM Model System v 6.0 input and configuration files for coupled large eddy simulations of land surface heterogeneity effects and diurnal evolution of late summer and early autumn atmospheric boundary layers during the CHEESEHEAD19 field campaign
<p>Namelist, configuration and forcing files for the PALM Model System 6.0 revision number 21.10-rc.2 used for the numerical simulations Coupled Large Eddy Simulations of land surface heterogeneity induced atmospheric boundary layer response during the CHEESEHEAD19 field campaign.</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>
Hampering Effect of Wave-Driven Turbulence on Advection Speed and Diffusion Rate of Pollutants: A Large Eddy Simulation Study
<p>The source codes and namelist files are prepared to reproduce the results of the study entitled 'Hampering Effect of Wave-Driven Turbulence on Advection Speed and Diffusion Rate of Pollutants: A Large Eddy Simulation Study'. Download the originial source codes for the Parallelized LES model (PALM) at <a href="https://palm.muk.uni-hannover.de">https://palm.muk.uni-hannover.de</a> and use the source codes provided here as 'user-defined code' in PALM.</p> <p>The data can be used to reproduce the figures of the scientific article named after the study.</p>
Large Eddy Simulation of the Southern Ocean
The data set contains seven Large Eddy Simulations (LES) at the Southern Ocean Flux Site for studies of deep turbulent ocean boundary layers, with and without surface wave effects, and with both idealized and observed forcing by wind, surface buoyancy flux and Stokes drift profiles. There are 20 days of hourly statistics computed every half-hour of turbulence quantities; namely, the vertical fluxes of buoyancy (temperature) and momentum, buoyancy and velocity variances, and the turbulent kinetic energy, its production terms and its dissipation.
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>
uDALES 1.0: a large-eddy-simulation model for urban environments
<p>Data accompanying the GMD article 'uDALES 1.0: a large-eddy-simulation model for urban environments'</p> <p>https://doi.org/10.5194/gmd-2021-255</p> <p>1) Figures shown in the manuscript</p> <p>2) Input to rerun the presented cases in uDALES</p> <p>3) Model output and Matlab scripts to recreate the figures</p>
On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model: Trained Models
<p>Trained machine learning models and scaling values used in the paper "On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model."</p>
Large-eddy simulation of yawed wind-turbine wakes: comparisons with wind tunnel measurements and analytical wake models
<p>Dataset of the paper "Large-eddy simulation of yawed wind-turbine wakes: comparisons with wind tunnel measurements and analytical wake models" published on Energies [1].</p> <p>[1] Lin, M., & Porté-Agel, F. (2019). Large-eddy simulation of yawed wind-turbine wakes: comparisons with wind tunnel measurements and analytical wake models. <em>Energies</em>, <em>12</em>(23), 4574.</p>
Simulation results with the EULAG research model for the publication: "Large eddy simulations of the interaction between the Atmospheric Boundary Layer and degrading Arctic permafrost"
<p>Supplementary material for the publication</p> <ul> <li>Mark Schlutow, Tobias Stacke, Tom Doerffel, et al. Large eddy simulations of the interaction between the Atmospheric Boundary Layer and degrading Arctic permafrost. ESS Open Archive . January 24, 2024. <a href="https://doi.org/10.22541/essoar.170612558.81370785/v1">https://doi.org/10.22541/essoar.170612558.81370785/v1</a></li> </ul> <p>The material contains all simulation results and raw outputs that are necessary to reproduce the figures and statistics of the publication. </p>
Supporting data for "The time scale of shallow convective self-aggregation in large-eddy simulations is sensitive to numerics"
<p>Numerical settings, routines and post-processed data used to generate the figures presented in "The time scale of shallow convective self-aggregation in large-eddy simulations is sensitive to numerics", manuscript submitted to Journal of Advances in Modeling Earth Systems. This version is an update after accounting for comments of three reviewers to the submitted manuscript.</p>
Data for submitted manuscript "Connections between Sub-cloud Coherent Structures and the Life Cycle of Shallow Cumulus Clouds: Evidence from Large Eddy Simulation"
<p>This is the dataset for a submitted manuscript "Connections between Sub-cloud Coherent Structures and the Life Cycle of Shallow Cumulus Clouds: Evidence from Large Eddy Simulation" for peer review.</p> <p>The NetCDF file includes masked objects for sub-cloud coherent structures and cloud for tracking.</p>
Realtime WRF Large-Eddy Simulation Data
<p>Sample data files from realtime micro-scale weather simulations that were performed to support UAV (Unmanned Aerial Vehicles) flights during ISARRA Lower Atmospheric Process Studies at Elevation – Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) field experiment. Two types of data are available: (1) ascii files that contain profiles and surface variables at select grid points that correspond with 3 key observing locations, (2) grib2 files contain 2D and 3D grids of variables described below and stored on levels AGL as described more below. The sample data includes all leadtimes from both domains. Domain 01 (1 km grid spacing) is initialized at 0400 UTC, lead times = 00h00m to 18h00m by 10m increments (108 files) and DO2 is initialized at 1000 UTC with leadtimes of 06h00m-18h00m by 10 min increments (72 files). Each grib2 file from the WRF-LES domain (D02) are around 100 MB per lead time. The full dataset is available here: <a href="https://doi.org/10.5065/83r2-0579">https://doi.org/10.5065/83r2-0579</a></p> <p>These simulations were performed by driving a nested grid configuration of the Weather Research and Forecasting model with its innermost mesh being run at 111 m grid spacing. The innermost grid was nested within a grid with 1 km grid spacing. The outermost grid being driven using operational forecast models data as described below. While the MYNN2 PBL scheme is used to parameterize turbulence in the 1 km grid, the PBL scheme is turned off within the 111 m grid, thus, allowing large-scale turbulent eddies to be resolved by WRF primitive equations. Subgrid-scale turbulence is diagnosed and stored within the TKE variable using Lilly (1966, 1967).<br> <br> The realtime simulations were produced twice per day in order to support mission planning and UAVs flight operations. A next-day simulation was run using forcing data from NCEP's Global Forecast System (GFS) while a day-of simulation was run using data from the High Resolution Rapid Refresh (HRRR). Both simulations were valid between 04:00 and 16:00 local time providing an opportunity to explore the impact of lateral boundary conditions on forecast skill. The dataset consists of a series of two sets of files: 3D grids and point profiles. The 3D grids consist of all relevant basic state parameters (p,T,U,RH) and diagnostics (e.g., sub-grid scale TKE, ceiling height, visibility) that have been interpolated to flight levels AGL using the Unified Post-Processor (UPP). The UPP was used to de-stagger the mass and wind fields (and compute wind in earth-relative coordinates), interpolate forecast data to flight levels AGL and to compute diagnostics such as visibility, ceiling height, and radar reflectivity.</p> <p>Profile and surface data are stored in ascii format for select grid points coincident with up to 3 fixed observation sites set up during LAPSE-RATE (i.e., Saguache, Moffat and Leach Airfield) with a time resolution of 0.666 sec. See README files for details. The 3D output files are stored in grib2 format which are available every 10 min. The grib2 data can be converted to netCDF using a variety of tools such as ncl_convert2nc command available on many linuxOS.</p> <p>---------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Details of ascii file names:</p> <p>XXX.dnn.VV.yyyymmddhh.gz</p> <p>where</p> <p>XXX is the location name (SAG - Saguache, MOF - Moffat, LEA - Leach)</p> <p>nn is the domain number (01,02,03)</p> <p>VV is the variable (see readme file for details)</p> <p>yyyymmddhh is the model initialization time (UTC)</p> <p> </p> <p>---------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Details of grib2 data files:</p> <p>Data are stored as follows: one file per forecast lead time using the following file naming convention:</p> <p>WRFPRS_YYYYMMDDhhmm_dnn.lh_lm</p> <p>where</p> <p>hhmm is the hour and minute of the day the model run was initialize</p> <p>nn = domain number</p> <p>lh = forecast outlook hour</p> <p>lm = forecast outlook min</p> <p>valid_time = hhmm + lhlm</p> <p>zlevels = (30,80,150,300,500,750,1000,1250,1500,1750,2000,2500,3000,3500,4000,4500,5000 m AGL)<br> <br> DatasetTemporalCoverage: 14 - 19 July 2018<br> Forecasts were issued twice per-day and valid between 04:00 and 16:00 LT (10:00 and 22:00 UTC)</p> <p>Gridded data are stored at 10 min intervals</p> <p><br> Interpolated 3D Variables: temperature,pressure,u,v,w,RH,sub-gridscale turbulence kinetic energy, energy dissipation, etc at selected heights AGL<br> <br> 2D Diagnosed variables:<br> Ceiling height, visibility, precip rate, accumulated precip, precipitable water, vertically- integrated condensed water, downwelling shortwave and longwave radiation at surface, sensible and latent heat flux, 10 m U and V, 2 m T and specific humidity<br> </p>
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