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161 results for “Numerical Simulation”
Numerical weather simulation using COSMOiso in June 2019 during L-WAIVE field campaign: selected model output and post-processed data.
<p>This dataset consists of extracts from a simulation with the isotope-enabled regional numerical weather prediction model COSMOiso, which covers the timespan of the Lacustrine-Water vApor Isotope inVentory Experiment (L-WAIVE) field campaign taking place in June 2019 in the Annecy valley in the French Alps (Chazette et al. 2021).The simulation has a horizontal resolution of 0.1° (~10km) and 40 vertical levels.</p><p>This COSMOiso simulation is used in Thurnherr et al. (submitted) to compare stable water isotope measurements from various platforms. Here, we provide selected model outputs and post-processed data used in this comparison study. The post-processed data contain:</p><ol><li>COSMOiso output files for time steps 20190612_12, 20190613_12, 20190615_13, 20190616_13, 20190617_12, 20190622_12.</li><li>Pressure weighted total and subcolumn averages for time steps 20190612_12, 20190613_12, 20190615_13, 20190616_13, 20190617_12, 20190622_12.</li><li>Vertical cross section of selected variables at Annecy, the location of the L-WAIVE field campaign, for the simulation time window.</li><li>Interpolated time series of subcolumn and total column averages at Annecy, the location of the L-WAIVE field campaign, for the simulation time window.</li><li>Interpolated variables along the flight tracks from the L-WAIVE campaign (see Sodemann and Seidl, 2023).</li></ol><p>See also README files for more details on the provided data.</p><p>To access further model output and post-processed data, please contact the dataset authors.</p>
Drop count and size of 38 numerical simulation of a flat fan spray
<p>Data refer to the drop size distribution used in the paper "Data-driven modelling for drop size distributions"</p> <p>by T. Traverso, T. Abadie, O. K. Matar, and L. Magri (arXiv link: https://arxiv.org/abs/2305.18049)</p> <p></p> <p></p> <p>Each of the 38 .csv file in this folder is associated with a different working condition of the nozzle.</p> <p>Specifically, the name 'alpha##_Re##_We##.csv' contains the working condition of the nozzle as</p> <p>- alpha## (## is the spray angle)</p> <p>- Re## (## is the Reynolds number)</p> <p>- We## (## is the Weber number)</p> <p>Each file contains as many raws as the number of drops.</p> <p>In the i-th raw,</p> <p>1) the first element is the Volume of the i-th drop;</p> <p>2) the second element is the estimated surface of the i-th drop with the method in equation (18) of [1];</p> <p>3) the third element is the equivalent diameter of the i-th drop (i.e., as if it was spherical - computed from the volume)</p> <p>The value of the Weber number found in the Arxiv paper is half of that reported here. The correct one is the one in this database. The paper will be corrected in due time.</p> <p> </p>
Numerical simulations of AZO/ZnGeO/Cu2O solar cells: Impact of the germanium composition of the buffer layer and the use of low cost fabrication on the photovoltaic performances
<p>The dataset contains the results of the numerical simulations of AZO/ZnGeO/Cu2O solar cell models.</p> <p>The physical parameters of the model are chosen with special care to match literature experimental measurements or are interpolated using the values from binary metal oxides in the case of the new ZnGeO compound. The solar cell structure includes an interface and a defective layer at the ZnGeO/Cu2O heterojunction.</p> <p>The AZO/ZnGeO/Cu2O model results reproduce the photovoltaic characteristics of experimental devices presented by Minami et al. (Applied Physics Express 9, 052301 (2016) DOI:10.7567/APEX.9.052301)</p> <p>The dataset also includes results using models with different germanium compositions for the ZnGeO buffer layer.</p> <p>Other solar cell simulation results are presented to model the impact of low cost fabrication processes, such as spray pyrolysis, by varying the thickness, doping concentration, carrier mobilities and defect concentration of the Cu2O absorber layer as well as the germanium composition of the buffer layer.</p>
Post-processed dataset from 50000 numerical simulations of monopile-supported NREL 5MW wind turbine in OpenFAST
<p>The dataset contains two separate files: NREL_Trainset40000.mat and NREL_Testset10000.mat.</p> <p>The stored input enviormental and operational parameters are:</p> <ul> <li>Significant wave height, m (Hs), peak period, s (Tp), wave direction, deg (Wave_dir);</li> <li>Wind speed, m/s (Vw_mean, Vw_std), wind direction, deg (Wdir_mean, Wdir_std);</li> <li>Turbine rotational speed, rpm (Rpm_mean, Rpm_std), blade pitch, deg (Pitch_mean, Pitch_std), turbine yaw angle, deg (Yaw_mean, Yaw_std).</li> </ul> <p>The output of the simulations includes the time series, sampled at 50 Hz, of the reaction force and bending moments at the mudline:</p> <ul> <li>Fzz, N</li> <li>Mxx, Nm</li> <li>Myy, Nm</li> </ul> <p>contact: nandar.hlaing@uliege.be</p>
Data for the paper "Particle method for the numerical simulation of the path-dependent McKean-Vlasov equation"
<p>This deposit contains the data obtained by the method described in [A. Bernou, Y. Liu, Particle method for the numerical simulation of the path-dependent McKean-Vlasov equation, 2024]. The notebooks used to generate them through a suitable Euler scheme can be find at https://github.com/ArmdBrn/McKean_PathDep, along with files containing the estimated errors. <br>The two models considered are:<br>- a modified Ornstein-Uhlenbeck model with path-dependency;<br>- a model of neural masses with intrinsic potentiation leading to path-dependent dynamics. </p>
Site-Specific MCER Response Spectra for Los Angeles Region based on 3-D Numerical Simulations and the NGA West2 Equations
<p><strong>ABSTRACT</strong></p> <p>The Utilization of Ground Motion Simulation (UGMS) committee of the Southern California Earthquake Center (SCEC) developed site-specific, risk-targeted Maximum Considered Earthquake (MCER) response spectra for the Los Angeles region. The long period (T ≥ 2-sec) MCER response spectra were computed as the weighted average of MCER spectral accelerations derived from (1) 3-D numerical ground-motion simulations using the CyberShake computational platform, and (2) empirical ground-motion prediction equations (GMPEs) from the Pacific Earthquake Engineering Research (PEER) Center NGAWest2 project. The short period (T < 2- sec) MCER response spectra were computed exclusively from the NGAWest2 GMPEs. A web-based lookup tool was also developed so users can obtain the MCER response spectrum for a specified latitude and longitude and for a specified site class or 30-m average shear-wave velocity, VS30. The tool provides acceleration ordinates of the MCER response spectrum at 21 natural periods in the 0 to 10-sec band.</p> <p>This dataset includes a Java application to run queries. It serves as the backend data source for the web-based tool that can be found at: <a href="https://data2.scec.org/ugms-mcerGM-tool_v18.4/">https://data2.scec.org/ugms-mcerGM-tool_v18.4/</a>.</p> <p>For more information, please see <a href="https://www.scec.org/research/ugms">https://www.scec.org/research/ugms</a>.</p> <p><strong>DISCLAIMER</strong></p> <p>The UGMS MCER Tool is provided "as is" and without warranties of any kind. While SCEC and the UGMS Committee have made every effort to provide data from reliable sources or methodologies, SCEC and the UGMS Committee do not make any representations or warranties as to the accuracy, completeness, reliability, currency, or quality of any data provided herein. SCEC and the UGMS Committee do not intend the results provided by this tool to replace the sound judgment of a competent professional, who has knowledge and experience in the appropriate field(s) of practice. By using this tool, you accept to release SCEC and the UGMS Committee of any and all liability.</p> <p>Please note: The site-specific, design response spectral acceleration, Sa, returned by this tool for user-specified inputs, must be compared to the minimum Sa requirement described in Section 21.3 of ASCE 7-16 (second and third paragraphs). This minimum Sa is computed as 80% of the design response spectrum derived from the SDS, SD1, and TL values obtained from the ASCE tool at https://asce7hazardtool.online/. The larger of the site-specific Sa and the 80% minimum Sa at each period, T, is the final design response spectral acceleration. This final Sa x 1.5 is the final MCER response spectral acceleration.</p>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Input files
<p>This dataset contains the parent input used to generate the simulation files of the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets, see "Related work" section. A report describing this dataset will be made available on BEL-Float project website by November 2024: https://www.owi-lab.be/bel-float.</p>
Data and scripts for reproducing "Quantifying Uncertainties in Direct Numerical Simulations of a Turbulent Channel Flow"
<p>This is the accompanying data and Python scripts to reproduce the figures in "Quantifying Uncertainties in Direct Numerical Simulations of a Turbulent Channel Flow", currently under review.</p>
Probability of Detection applied to X-ray inspection using numerical simulations
<p>In this work, we apply and adapt established Probability of Detection (POD) methods on inline inspection of aluminium cylinder heads using X-ray computed tomography. The CT simulation tool SimCT [4] is used to acquire virtual images of the specimens including artificial defects, which avoids the manufacturing of calibrated defects of known type (e.g., pore, inclusion, crack etc.), size and location. One of the exemplary defects is discussed as representative result together with the generated POD curves as well as its characteristics (i.e., the minimum detected defect, the maximum missed defect, POD(a90) =0.90 and a90/95).</p>
Numerical simulations and experimental measurements of the ULB semi-industrial furnace for the development of a Digital Twin
<p>This dataset contains the numerical and experimental data used to build the Digital Twin in Aversano et al. (https://doi.org/10.1016/j.proci.2020.06.045) and the adaptive Digital Twin in Procacci et al. (https://doi.org/10.1016/j.proci.2022.07.029).</p> <p>The directory "Numerical_data" includes 45 text files containing the data coming from the CFD simulations of the ULB furnace. <br> In each file, for each computational cell the features reported are: <br> - the cell's position in x, y, z coordinates and in meters.<br> - the cell's temperature in K. <br> - the cell's species mass fraction of NO (mf-pollut-pollutant-0), CO, OH, H2, H2O, CO2, O2, CH4.<br> The details of the setup of the numerical simulations are reported in Aversano et al.</p> <p>The numerical simulations have been computed for different values of the equivalence ratio (phi), blend of H2-CH4 (H2) and <br> inlet diameter (D).<br> The simulations for different inlet diameter where computed using different meshes, with slightly different numbers of cells.<br> In the file 'cases_parameters.csv', the value of the parameters is reported for of each simulation. There is a <br> discrepancy between the naming of the simulations in Aversano et al. and the one used in naming the files, so both are reported.</p> <p>The experimental measurements used to validate the numerical simulations can be found in the directory "Experimental_data". Each<br> file contains the value of the measured temperature along with the position in x and z in meters (y being 0). The temperature is<br> in K. The experimental uncertainty is estimated at 10 K.</p> <p>The file 'grid.vtu' contains the computational grid used to solve the CFD simulations. It can be opened using VTK-based software <br> such as Paraview or Pyvista.</p> <p>Changelog:</p> <p>- In version V1, some simulations were corrupted during data export.<br> - Added the grid file in V3</p>
Dataset to "Modeling Collision-Coalescence in Particle Microphysics: Numerical Convergence of Mean and Variance of Precipitation in Cloud Simulations Using University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1 " by Zmijewski, Dziekan & Pawlowska
<p>The archive contains datasets, run scripts, time series and plotting scripts used when preparing the paper: P. Zmijewski, P. Dziekan and H. Pawlowska "Modeling Collision-Coalescence in Particle Microphysics: Numerical Convergence of Mean and Variance of Precipitation in Cloud Simulations Using University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1 " submitted to Geoscientific Model Development in March 2023.</p>
Numerical simulation of the equatorial plasma bubble: the effect of seeding by the vertical winds and random background noise perturbations.
<p>A wide variety of small-amplitude waves widely exist in the ionosphere and have significant effects on the evolution of equatorial plasma bubbles. In this paper, we simulated equatorial plasma bubbles (EPB) seeded by vertical neutral wind perturbations with wavelengths of 125 km and 250 km, and compared the morphology characteristics of plasma bubble structures with those under random noise perturbations in the background density. The numerical results showed that both vertical winds and random background noise perturbations can contribute to the growth of plasma bubbles, and the perturbations under additional random background noise can promote the growth of the plasma bubble structures faster. Additionally, several processes of the nonlinear behavior of bifurcated EPB structures, including bifurcation, pinching, and small-scale turbulent structures, were successfully obtained. Our simulation captured supersonic flows within the low-density plasma structures characterized by vertical velocities of about 1.5 km/s, which is consistent with experimental studies found in the literature.</p>
Numerical simulation of friction extrusion: Process characteristics and material deformation due to friction
<p>This study employs a finite element thermo-mechanical model, using a Lagrangian incremental setting to investigate friction extrusion (FE) under varying process conditions. The incorporation of rotation in FE generates substantial frictional heat, leading to significantly reduced process forces in comparison to conventional extrusion (CE). The model reveals the interplay between temperature, strain, and strain rate across different microstructural zones of the resulting wire. Specifically, the sticking friction condition in FE enhances initial shear deformation, aligning with a homogeneous spatial strain distribution and predicting complete grain refinement in the extruded wire, as per Zener-Hollomon calculations. On the other hand, under the sliding friction condition in FE, the shear deformation is reduced which results in an inhomogeneous microstructure in the extruded wire. The analysis of material flow in the workpiece reveals distinct transitions from the base material to the thermo-mechanically affected zones. The simulated process force, thermal history, and microstructure during sliding friction conditions align well with the findings from performed friction extrusion experiments.</p>
Wall Resolved Fluid-Structure Interaction Numerical Simulation of a Modern Wind Turbine Blade
<p>Wall-resolved fluid-structure interaction (FSI) numerical simulations of the NREL 5 MW wind turbine blade<br> are compared using two FSI approaches. The first method is based on high-fidelity Nektar++/SHARPy FSI framework,<br> where the fluid governing equations are solved using high-order spectral/hp element method and the turbulent flow is<br> resolved using Large Eddy Simulation (LES) on thick strips, while large-deformation dynamics of the structure are mod-<br> elled using a geometrically exact nonlinear composite beam finite-element model. Thick strip method for the fluid reduces<br> the computational cost by considering a series of smaller domains, each of which has a finite thickness in the spanwise<br> direction. Hence, the overall flow over the blade is treated with a sectional approach, where in each of these sections,<br> strips, the 3D flow is reconstructed locally. Tip-loss correction is used to compensate for the sectional approach over the<br> blade. The second FSI approach is based on OpenFoam/Calculix coupling, where the second-order unstructured finite<br> volume method approach is used for solving the three-dimensional flow equations and the flow turbulence is captured us-<br> ing the k-ω SST model. The structural dynamics are modeled via second-order finite element method using standard solid<br> elements. Effects of the solution fidelity on the prediction of aerodynamic forces as well as on the full three-dimensional<br> flow modelling over the blade versus sectional representation of flow over the blade while incorporating the local three-<br> dimensionality in each section and tip-correction are discussed. Further, significance of two approaches on modelling<br> the slender blade, one using the beam mode and the other utilizing the full 3D solution of structure is addressed. Finally,<br> assessment of computational cost and scalability of the two approaches are presented and discussed.</p>
Idealized Planar Array study for Quantifying Spatial heterogeneity (IPAQS) - Numerical Simulations
<p>The Idealized Planar Array study for Quantifying Spatial heterogeneity (IPAQS) is the result of a National Science Foundation (US) funded project, that aims at studying the effect of surface thermal heterogeneities of different length-scale on the atmospheric boundary layer. This project consisted of a computational effort (dataset here included), and an experimental effort (dataset being prepared for publication). </p> <p><strong>Overview of the numerical (Large Eddy) simulations:</strong></p> <p>The simulations are separated into two sets to study the differences between heterogeneous and homogeneous surfaces. In the first set, a total of seven configurations are considered, all with a homogeneous surface temperature fixed at a value of <span class="math-tex">\(T_s\)</span> = 290 K, and for which the geostrophic wind speed has been increased from 1 to 15 m s<sup>-1</sup> (i.e., U<sub>g</sub> = 1, 2, 3, 4, 6, 9, 15 m s<sup>−1</sup> ). These homogeneous cases are referred to as Homog-X, where X indicates the geostrophic wind speed corresponding case (see Margairaz et al. 2020a). In the second set, the surface temperature is distributed amongst square patches, where the temperature of each patch is determined by sampling a Gaussian distribution with a mean temperature of 290 K and a standard deviation of 5 K. In this case, three different patch sizes were considered (i.e., l<sub>h</sub> = 800, 400, and 200 m). The sizes of the heterogeneities were chosen to be of similar size (l<sub>h</sub> /l<sub>d</sub> ≈ 1), half the size (l<sub>h</sub> /l<sub>d</sub> ≈ 1/2), and about a quarter of the size (l<sub>h</sub> /l<sub>d</sub> ≈ 1/4) of the largest flow motions within the represented thermal boundary layer, assuming that this is of the order of the boundary-layer height (l<sub>d</sub> ∼ z <sub>i</sub> ). These heterogeneities are typically not resolved in NWP models. These cases have been studied for the same geostrophic wind speeds indicated above, and hereafter are referred to as PYYY-X-, where X indicates the corresponding geostrophic wind speed, and YYY refers to the size of the patches (e.g., P800_Ug1_ would be the heterogeneous case with patches of 800 m, and forced with Ug = 1 m s<sup>−1</sup> ). Additionally, for the case with larger patches, three different random distributions of the patches were considered to evaluate the potential effect of a given surface distribution for all geostrophic wind speeds. In this dataset we only include case v3. The LES imposed surface temperature distributions emulate the surface thermal conditions observed in Morrison et al. (2017 QJRMS, 2021 BLM, 2022 BLM), where measurements of the surface temperature were taken with a thermal camera at the SLTEST site of the US Army Dugway Proving Ground in Utah, USA. This is an ideal site with uniform roughness and a large unperturbed fetch, where surface thermal heterogeneities are naturally created by differences in surface salinity. In all studied cases, the surface roughness is assumed homogeneous, with z<sub>0</sub> = 0.1 m, and representative of a surface with sparse forest or farmland with many hedges (Brutsaert 1982; Stull 1988). The initial boundary-layer height is set to z<sub>i</sub> = 1000 m. The temperature profile is initialized with a mean air temperature of 285 K. At the top of the initial boundary layer, a capping inversion of 1000 m is used to limit its growth. The strength of this inversion is fixed at Γ = 0.012 K m<sup>−1</sup>. The atmospheric boundary layer (ABL) is considered dry and the latent heat flux is neglected in all cases. Further, in all simulations, the surface heat flux is computed using MOST, as explained in Margairaz et al. 2020a, where the surface temperature is kept constant in time throughout the simulations. Thus, there is no feedback from the atmosphere to the surface as the surface temperature does not cool down or warm up with local changes in velocities. As a consequence, the ABL gradually warms up as the simulations progress, and hence becomes less convective over time. However, the runs are not long enough for this to be significant. In addition, to ensure a degree of homogeneity within each patch and a certain degree of validity of MOST, note that even for the heterogeneous cases with the fewest amount of grid points per patch, a minimum of eight grid points is granted in each horizontal direction. The domain size is set to (L<sub>x</sub>, L<sub>y</sub>, L<sub>z</sub>) = (2π, 2π, 2) km at a grid size of (Nx , Ny , Nz ) = (256, 256, 256) resulting in a horizontal resolution of <span class="math-tex">\(\Delta\)</span>x = <span class="math-tex">\(\Delta\)</span>y = 24.5 m and a vertical grid spacing of <span class="math-tex">\(\Delta\)</span>z = 7.8 m. A timestep of <span class="math-tex">\(\Delta\)</span>t = 0.1 s is used to ensure the stability of the time integration. The two sets of simulations span a large range of geostrophic forcing conditions, allowing the study of the effect on the structure of the convective boundary layer (CBL) above a patchy surface compared to a homogeneous surface. The procedure used to spin up the simulations is the following: a spinup phase of four hours of real time is used to achieve converged turbulent statistics, which is then followed by an evaluation phase. During the latter, running averages are computed for the next hour of real time (dataset here published). Statistics have been computed for averaging times of 5 min to 1 h, showing statistical convergence at 30-min averages with negligible changes between the 30-min and the 60-min averages. The simulations cover a wide range of atmospheric stability regimes ranging from −z<sub>i</sub>/L < 5 to −z<sub>i</sub>/L > 700, and hence spanning from near neutral to highly convective scenarios.</p> <p><strong>Description of the Dataset as included in the NetCDF files:</strong></p> <p>Data for each study case is included in two files, one for momentum related variables, and one for temperature related variables. For example, the following files "P200_Ug1_Momentum.nc" and "P200_Ug1_Scalar.nc", include the 1h averaged variables for momentum and temperature for the case of 200 m surface patches with 1 m/s geostrophic winds. </p> <p>Each corresponding momentum file "PXXX_UgX_Momentum.nc" includes the following variables in a Python Xarray structure:</p> <ul> <li>'avgU' = mean streamwise wind speed; 'avgV' = mean spanwise wind speed; 'avgW' = mean vertical wind speed, 'avgP' = mean dynamic modified pressure field (<span class="math-tex">\(p^*\)</span>, see Margairaz et al 2020a),</li> <li>'avgU2', 'avgV2', 'avgW2' = correspond to <span class="math-tex">\(\overline{UU}\)</span>, <span class="math-tex">\(\overline{VV}\)</span>, and <span class="math-tex">\(\overline{WW}\)</span>, where the capital indicates the LES filtered variable.</li> <li>'avgUV', 'avgUW', 'avgVW' = correspond to <span class="math-tex">\(\overline{UV}\)</span>, <span class="math-tex">\(\overline{UW}\)</span>, and <span class="math-tex">\(\overline{VW}\)</span>. These variables together with the ones above are used to compute the Reynolds stress components (e.g. <span class="math-tex">\(R_{xz} = \overline{U}\overline{W} - \overline{UW}\)</span>).</li> <li>avgU3', 'avgV3', 'avgW3', 'avgU4', 'avgV4', 'avgW4' = correspond to the equivalent but instead of squared they are cubed and to the 4th power.</li> <li>'avgtxx','avgtyy','avgtzz','avgtxy','avgtxz','avgtyz' = These represent the corresponding averaged subgrid scale (SGS) stress.</li> <li>'avgdudz','avgdvdz','avgNut','avgCs' = Represent the averaged vertical derivatives, an averaged subgrid Nusselt number, and the Cs coefficient computed in the SGS model.</li> </ul> <p>Overall, there are a total of 26 variables related to the momentum field. Alternatively, the temperature fields are included in the "PXXX_UgX_Scalar.nc" files. These files include 10 variables, </p> <ul> <li>'avgT' = mean Temperature field, 'avgT2' = corresponds to <span class="math-tex">\(\overline{TT}\)</span>, 'avgUT' = correspond to <span class="math-tex">\(\overline{UT}\)</span>, 'avgVT' = correspond to <span class="math-tex">\(\overline{VT}\)</span>, 'avgWT' = correspond to <span class="math-tex">\(\overline{WT}\)</span>; one can use these terms to compute the corresponding Reynolds averaged turbulent fluxes as is the case for momentum. </li> <li>'avgUT_sgs','avgVT_sgs','avgWT_sgs' = These represent the corresponding subgrid scale fluxes.</li> <li>'avg_nus', avg_ds' = averaged subgrid Nusselt number, and the Ds coefficient computed in the scalar SGS model.</li> </ul> <p>All variables output from the LES are normalized by Tscale = 290 [K] when it includes dimensions of temperature, u_scale = 0.45 [m/s], when it relates to velocity fields, and z<sub>i</sub> = 1000 [m] for length scales.</p> <p>The only output variables that are expressed in dimensional form are those for the surface temperature included in the files "SurfTemp_DXXX.nc"</p> <p>Together with the data files we include a Python script that loads the data and includes it in two Xarray structures that one can then use to work with the datasets. </p>
Data for "Numerical simulation study of the evolution of lightning channel decay and reactivation processes" by Zheng et al.
<p>All data of the manuscript "Numerical simulation study of the evolution of lightning channel decay and reactivation processes" submitted to Journal of Geophysical Research: Atmospheres.</p> <p>The data supports the manuscript entitled "Numerical simulation study of the evolution of lightning channel decay and reactivation processes”. Microsoft Notepad can open the *.txt files and the *.DAT files, they contain the channel information of two intracloud flashes (IC1 and IC2) and the channel elctrical parameters at different channel segments. </p> <p>The data can be used freely for scientific purposes with the appropriate citation. </p>
Two-dimensional Numerical Simulations of Mixing under Ice Keels Data
<p>This deposition contains time series of relevant outputs from the ice keel simulations. These include:</p> <ol> <li>The buoyancy frequency squared (Nstar_sq)</li> <li>The spatially-average irreversible mixing rate (phi_d)</li> <li>The spatially-averaged dimensionless diapycnal diffusivity (K)</li> <li>The mixing depth (95% of mixing occurs above this depth) in meters (z_mix)</li> <li>The relative mixing depth (z_mix_rel). This differs from the previous quantity as it is relative to the keel depth. That is, if the mixing depth was the keel height then the relative mixing depth would be 0.</li> </ol> <p>The files are formatted to be imported as a dictionary into a Python file via the Json package. The keys are the simulation names. The key values are a tuple with the first value being the time series and the second being an array of times at which the respective values were recorded. All values are separated into upstream and downstream files.</p>
Data from: Numerical Simulation of the Atmospheric Signature of Artificial and Natural Seismic Events
<p>This data is related to the seismic hammer experiment discussed in "Numerical Simulation of the Atmospheric Signature of Artificial and Natural Seismic Events" by Martire et al. (2018, DOI will be added upon acceptance of the manuscript).</p> <p>The .zip file contains 3 .mseed files, and 1 .txt file. The .mseed are the raw seismometer signals. The .txt details the position of the sensor.</p> <p>Remaining data used in our paper can be found in the repository related to "Detection of Artificially Generated Seismic Signals using Balloon-borne Infrasound Sensor" by Krishnamoorthy et al. (2018, DOI 10.1002/2018GL077481). That repository has DOI 10.6084/m9.figshare.6137507.</p>
Dataset of Paper "Novel procedure for the numerical simulation of solar water disinfection processes in flow reactors" (DOI: 10.1016/j.cej.2018.10.131)
<p>Datasets of Paper "Novel procedure for the numerical simulation of solar water disinfection processes in flow reactors".</p> <p>DOI: 10.1016/j.cej.2018.10.131</p> <p>Data of the velocity profiles at different distances from the inlet of a solar rainwater reactor.</p> <p>Data of the simulated radiation field inside of a solar rainwater reactor as a function of the location, date, time and CPC inclination.</p> <p>Data of the disinfection efficiency versus illumination time in a solar reactor under simulated and natural sunlight.</p>
3D Taylor-Green vortex Direct Numerical Simulation statistics from Re=1250 to Re=20000
<p>Statistical data for the 3D Taylor Green flow from Re=1250 to Re=20000 obtained with the flow solver <a href="https://www.incompact3d.com/">Incompact3d</a>. </p> <p># ===========================================================================================<br> # When publishing results using this data, the following paper should be cited as the source: <br> # Thibault Dairay, Eric Lamballais, Sylvain Laizet and John Christos Vassilicos<br> # Numerical dissipation vs. subgrid-scale modelling for large eddy simulation<br> # Journal of Computational Physics 337 (2017) 252–274<br> # https://doi.org/10.1016/j.jcp.2017.02.035<br> # ===========================================================================================</p> <p># Column 1 : time t<br> # Column 2 : kinetic energy E_k [=(u^2+v^2+w^2)/2]<br> # Column 3 : dissipation epsilon_t [=-dE_k/dt]<br> # Column 4 : dissipation epsilon [= nu ((du/dx)^2+(du/dy)^2+(du/dz)^2+(dv/dx)^2+(dv/dy)^2+(dv/dz)^2+(dw/dx)^2+(dw/dy)^2+ dw/dz)^2)]<br> # Column 5 : enstrophy Dzeta [=2 nu epsilon]<br> # Column 6 : mean square u^2<br> # Column 7 : mean square v^2<br> # Column 8 : mean square w^2<br> # Column 9 : mean square (du/dx)^2<br> # Column 10 : mean square (du/dy)^2<br> # Column 11 : mean square (du/dz)^2<br> # Column 12 : mean square (dv/dx)^2<br> # Column 13 : mean square (dv/dy)^2<br> # Column 14 : mean square (dv/dz)^2<br> # Column 15 : mean square (dw/dx)^2<br> # Column 16 : mean square (dw/dy)^2<br> # Column 17 : mean square (dw/dz)^2</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.