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11 results for “Direct Numerical Simulation”
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>
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>
Figures, plotting scripts, and data for "A fast, low-cost, and stable memory algorithm for implementing multicomponent transport in direct numerical simulations"
<p>This dataset contains the figures, as well as the necessary plotting scripts and data to reproduce them, for the article "A fast, low-cost, and stable memory algorithm for implementing multicomponent transport in direct numerical simulations" by Aaron J. Fillo, Jason Schlup, Guillaume Beardsell, Guillaume Blanquart, and Kyle E. Niemeyer (2019). In addition, the code used to generate the eigenvalues in Table 1 is included.</p> <p>The scripts were run in Matlab 2019a, though none of the versions used should be version-dependent. Furthermore, non-standard functions are included with dependencies hard-coded. We used export_fig (https://github.com/altmany/export_fig) to generate high-quality figures, and redistribute the version used here for reproducibility (export_fig was developed by Oliver J. Woodford and Yair M. Altman, and made available openly under the BSD 3-Clause License).</p> <p>The code included in this dataset is released under the BSD 3-Clause License (see LICENSE.txt for details), other than the source of export_fig, as described. The figures are shared under the Creative Commons Attribution 4.0 International License (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/).</p>
Direct Numeric Simulation dataset archives
<p>Cloud droplets are analyzed using DNS data. Let's first discuss the many types of cloud representation and the cloud's structure before getting into the data. Later, the World Meteorological Organization adopted this classification, and eleven cloud genera are now recognized according to height and appearance. The middle clouds in the center, low clouds in the bottom panel, and high clouds in the top panel. We used simulated data of cumulus-type low clouds. Fair weather clouds that resemble white cotton balls are present. Clouds have a number of effects on the atmosphere, including the hydrological cycle and the radiation imbalance.</p>
Data for: Direct numerical simulation surface layer and IR-based measurements
<p>This is the primary dataset used in a manuscript in the process of being submitted to JTECH. The bulk of the dataset is a direct numerical simulation (DNS) of an open channel flow with a shear-free surface. The DNS includes scalar, velocity, and divergence fields over 181 realizations. From the scalar field, another vector field was generated by a method called feature image velocimetry that successively cross-correlates the scalar fields to derive the underlying velocity field. The two velocity fields are then compared by their spectra, one from the DNS and the other derived from the scalar field.</p>
Dataset for "Quantifying the effects of bed roughness on transit time distributions via direct numerical simulations of turbulent hyporheic exchange"
<p>This dataset contains the sediment models, DNS flow field data, subsurface path data, and calculated transit time distributions for both the regular- and random-interface cases used in the paper: "Quantifying the effects of bed roughness on transit time distributions via direct numerical simulations of turbulent hyporheic exchange" by Guangchen Shen, Junlin Yuan, and Mantha S. Phanikumar (Submitted to Water Resources Research). <br> Detailed introduction of each data file is as follows.</p> <p>1. DNS flow field data</p> <p>Flowfield_Reg.h5 and Flowfield_Ran.h5 contains the following fields for the regular and random cases, respectively. 'ni', 'nj', 'nk' are the numbers of grid points along x, y, and z directions. 'xc', 'yc', 'zc' are the cell center locations. 'u3d','v3d','w3d' are the three-dimensional time-averaged velocities at each grid point. 'vof' is the volume-of-fluid field used by the immersed-boundary method to prescribe the fluid-solid interface (vof=1 in fluid and 0 in solid), at each grid point. 'vof' contains the information of sediment grain distribution and bed roughness geometry. Only the subsurface data (those below the sediment crest) are shared due to dataset size limit.</p> <p>2. Particle-tracked subsurface flow paths and corresponding transit time distributions</p> <p>The mat files “xxx_pathline” store the (x,y,z) location of each point (saved as 'StrX', 'StrY, 'StrZ') along the subsurface paths, discretized by uniform steps of travel time (with time intervals of 0.1 for 'A' and 0.01 for 'MD' cases, normalized by channel height and friction velocity). The files “xxx_TT” store the array of transit times corresponding to the tracked paths, where the 1d array T is the transit time. 'A' denotes calculations based on time-mean advection only, while 'MD' denotes calculations accounting for additional molecular diffusion. 'Interface' and '3DiameterBelow' indicate that the particles were released at the interface and -3 D below the interface, respectively.</p>
Data for "Convective Organization and Dry Tropical Cyclones in Direct Numerical Simulations of Idealized Fluid Setups"
<p>Supporting data and analysis scripts for work contained in the manuscript Velez-Pardo, Martin & Cronin, Timothy W. (2023) Convective Organization and Dry Tropical Cyclones in Direct Numerical Simulations of Idealized Fluid Setups. README and scripts for running simulations and for data post-processing are found in README_and_scripts.zip. Data obtained using supercomputer Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory (CISL), sponsored by the National Science Foundation.</p>
Emulator of PR-DNS: Accelerating Dynamical Fields with Neural Operators in Particle-Resolved Direct Numerical Simulation
<p>The codes directory includes the various machine learning models, such as FNO, UNet and ResNet. R128_init1 and R128_init2 are the PR-DNS time step simulations at different initial conditions. R64_init2, R128_init2 and R256_init2 are the PR-DNS time step simulations at different resolutions. </p> <p> </p>
Experimental Data for "Seismic wave attenuation and dispersion due to partial fluid saturation: Direct measurements and numerical simulations based on X-Ray CT"
<p>Experimental Data from a Berea sandstone sample. Includes X-ray CT scans and mechanical response of sample.</p> <p>Abstract</p> <p>Quantitatively assessing seismic attenuation caused by fluid pressure diffusion (FPD) in partially saturated rocks is challenging because of its sensitivity to the spatial fluid distribution. To address this challenge we performed depressurisation experiments to induce the exsolution of carbon dioxide from water in a Berea sandstone sample. In a first set of experiments we used medical X-ray computed tomography (CT) to characterise the fluid distribution. At an equilibrium pressure of ~1 MPa and applying a fluid pressure decline rate of ~0.6 MPa per minute, we allowed a change in saturation of less than 1 %. The gas was heterogeneously distributed along the length of the sample, with most of the gas exsolving near the sample outlet. In a second set of experiments, at the same pressure and temperature, following a very similar exsolution protocol, we measured the frequency dependent attenuation and modulus dispersion between 0.1 and 1000 Hz using the forced oscillation method. We observed significant attenuation and dispersion in the extensional and bulk deformation modes, however not in the shear mode. Lastly, we use the fluid distribution derived from the X-ray CT as an input for numerical simulations of FPD to compute the attenuation and modulus dispersion. The numerical solutions are in close agreement with the attenuation and modulus dispersion measured in the laboratory. Our methodology allows for accurately relating attenuation and dispersion to the fluid distribution, which can be applied to improving the seismic monitoring of the subsurface.</p>
Simulation results for "Deciphering clues regarding magma composition encoded in quartz-hosted embayments and melt inclusions through direct numerical simulations"
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Emulator of PR-DNS: Part II, dataset for training the emulator of thermodynamics and cloud droplet fields in Particle-Resolved Direct Numerical Simulation
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