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129 results for “Flow simulation”
Compressible Hydrodynamics Simulation Data for "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows"
<p><strong>Background</strong></p> <p>This data is a 2D cross-section from a 3D compressible hydrodynamics simulation (Hyburn / AMRex code) of a rapid decompression / shock tube experiment at Special Technologies Laboratory. The simulated shot is a pure argon gas decompression from 1000Psi to atmosphere. </p> <p>This data is used in figures 3 and 5 of the paper "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows".</p> <p>Electric sparks and explosive flows have long been associated with each other. Flowing dust particles originate charge through contact and separate based on inertia, resulting in strong electric fields supporting sparks. These sparks can cause explosions in dusty environments, especially those rich in carbon, such as coal mines and grain elevators. Recent observations of explosive events in nature and decompression experiments indicate that supersonic flows of explosions may alter the electrical discharge process. Shocks may suppress parts of the hierarchy of the discharge phenomena, such as leaders. In our decompression experiments, a shock tube ejects a flow of gas and particles into an expansion chamber. We imaged an illuminated plume from the decompression of a mixture of argon and <100 mg of diamond particles and observe sparks occurring below the sharp boundary of a condensation cloud. We also performed hydrodynamics simulations of the decompression event that provide insight into the conditions supporting the observed behavior. Simulation results agree closely with the experimentally observed Mach disk shock shape and height. This represents direct evidence that the sparks are sculpted by the outflow. The spatial and temporal scale of the sparks transmit an impression of the shock tube flow, a connection that could enable novel instrumentation to diagnose currently inaccessible supersonic granular phenomena.</p> <p><strong>Accessing Data</strong></p> <p>The data is saved as python numpy zipped archives numbered by the timestep in the simulation. Files starting with 'tube' contain data from inside the shock tube. Files starting with 'near_vent' contain data from the expansion chamber above the nozzle. All units are in SI.</p> <p>Each .npz file is an array file generated with python numpy.savez(). It can be opened with:</p> <p><em>import numpy as np</em></p> <p><em>data = np.load('<name>.npz')</em></p> <p>The data is an python dictionary. The dictionary keys can be displayed with:</p> <p><em>print(data.files)</em></p> <p>The numpy arrays can be accessed by keyname:</p> <p><em>print(data['keyname'])</em></p> <p>The key names correspond to physical quantities (density, temperature, etc.). All particle quantities are 0 as the simulation did not include particles.</p>
Dark matter flow dataset Part I: Halo-based statistics from cosmological N-body simulation
<p>Dark matter (DM), if exists, is believed to be cold, collisionless, dissipationless, non-baryonic, barely interacting with baryonic matter except through gravity, and sufficiently smooth on large scales with a fluid-like behavior. The flow of dark matter can be best described by a self-gravitating collisionless fluid dynamics (SG-CFD). The statistics of dark matter density, velocity, acceleration, energy, momentum, and their redshift evolution play essential roles for structure formation and evolution. These information can be systematically extracted from cosmological N-body simulations by either i) a structural (halo-based) or ii) a statistical (correlation-based) approach. In this halo-based statistical dataset, i) all halos in N-body system are identified with all particles divided into halo and out-of-halo particles; ii) halos are grouped into halo groups including all halos of the same mass (m<sub>h</sub>); iii) the redshift (z) and mass scale (m<sub>h</sub>) dependence of all halo properties (momentum, energy, size, shape, velocity, acceleration, etc.) are presented . </p> <p>Applications of cascade and statistical theory for dark matter and bulge-SMBH evolution:</p> <ol> <li>Dark matter particle mass ,size, and properties from energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.07240">arxiv</a> 2) <a href="https://zenodo.org/record/6640353">zenodo slides</a></li> <li>Origin of MOND acceleration & deep-MOND from acceleration fluctuation & energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2203.05606">arxiv</a> 2) <a href="https://zenodo.org/record/6640386">zenodo slides</a></li> <li>The baryonic-to-halo mass relation from mass and energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2203.06899">arxiv</a> 2) <a href="https://zenodo.org/record/6640355">zenodo slides</a></li> <li>Universal scaling laws and density slope for dark matter haloes: 1) <a href="http://doi.org/10.48550/arXiv.2209.03313">arxiv</a> 2) <a href="https://zenodo.org/record/7059193">zenodo slides</a> 3) <a href="http://doi.org/10.1038/s41598-023-31083-z">paper</a></li> <li>Dark matter halo mass functions and density profiles from mass/energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2210.01200">arxiv</a> 2) <a href="https://zenodo.org/record/7146473">zenodo slides</a> 3) <a href="https://doi.org/10.1038/s41598-023-42958-6">paper</a></li> <li>Energy cascade for distribution and evolution of supermassive black holes (SMBHs): 2) <a href="http://doi.org/10.5281/zenodo.7490502">zenodo slides</a></li> </ol> <p>Condensed slides for all applications "<a href="http://doi.org/10.5281/zenodo.7508310">Cascade Theory for Turbulence, Dark Matter, and bulge-SMBH evolution </a>"</p> <p>The two relevant datasets and accompanying presentation can be found at: </p> <ol> <li><a href="https://doi.org/10.5281/zenodo.6541230">Dark matter flow dataset Part I: Halo-based statistics from cosmological N-body simulation</a> </li> <li><a href="https://doi.org/10.5281/zenodo.6569898">Dark matter flow dataset Part II: Correlation-based statistics from cosmological N-body simulation</a>.</li> <li><a href="https://doi.org/10.5281/zenodo.6569901">A comparative study of Dark matter flow & hydrodynamic turbulence and its applications</a></li> </ol> <p>The same dataset also available on Github at: <a href="https://github.com/ZhijieXu2022/dark_matter_flow_dataset/">Github: dark_matter_flow_dataset</a> and zenodo at: <a href="http://doi.org/10.5281/zenodo.6586212">Dark matter flow dataset from cosmological N-body simulation</a>.</p> <p>Cascade and statistical theory developed by these datasets:</p> <ol> <li>Inverse mass cascade in dark matter flow and effects on halo mass functions: 1) <a href="http://doi.org/10.48550/arXiv.2109.09985">arxiv</a> 2) <a href="https://zenodo.org/record/6639536">zenodo slides</a> </li> <li>Inverse mass cascade and effects on halo deformation, energy, size, and density profiles: 1) <a href="http://doi.org/10.48550/arXiv.2109.12244">arxiv</a> 2) <a href="https://zenodo.org/record/6640337">zenodo slides</a></li> <li>Inverse energy cascade in dark matter flow and effects of halo shape: 1) <a href="http://doi.org/10.48550/arXiv.2110.13885">arxiv</a> 2) <a href="https://zenodo.org/record/6640331">zenodo slides</a></li> <li>The mean flow, velocity dispersion, energy transfer and evolution of dark matter halos: 1) <a href="http://doi.org/10.48550/arXiv.2201.12665">arxiv</a> 2) <a href="https://zenodo.org/record/6640380">zenodo slides</a></li> <li>Two-body collapse model and generalized stable clustering hypothesis for pairwise velocity 1) <a href="http://doi.org/10.48550/arXiv.2110.05784">arxiv</a> 2) <a href="https://zenodo.org/record/6640306">zenodo slides</a></li> <li>Energy, momentum, spin parameter in dark matter flow and integral constants of motion: 1) <a href="http://doi.org/10.48550/arXiv.2202.04054">arxiv</a> 2) <a href="https://zenodo.org/record/6640322">zenodo slides</a></li> <li>Maximum entropy distributions of dark matter in ΛCDM cosmology: 1) <a href="http://doi.org/10.48550/arXiv.2110.03126">arxiv</a> 2) <a href="https://zenodo.org/record/6640373">zenodo slides</a> 3) <a href="http://doi.org/10.1051/0004-6361/202346429">paper</a></li> <li>Halo mass functions from maximum entropy distributions in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2110.09676">arxiv</a> 2) <a href="https://zenodo.org/record/6640325">zenodo slides</a></li> <li>On the statistical theory of self-gravitating collisionless dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.00910">arxiv</a> 2) <a href="https://zenodo.org/record/6640705">zenodo slides</a> 3) <a href="http://doi.org/10.1063/5.0151129">paper</a></li> <li>High order kinematic and dynamic relations for velocity correlations in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.02991">arxiv</a> 2) <a href="https://zenodo.org/record/6640684">zenodo slides</a></li> <li>Evolution of density and velocity distributions and two-thirds law for pairwise velocity: 1) <a href="http://doi.org/10.48550/arXiv.2202.06515">arxiv</a> 2) <a href="https://zenodo.org/record/6640676">zenodo slides</a></li> </ol>
Dark matter flow dataset Part II: Correlation-based statistics from cosmological N-body simulation
<p>Dark matter (DM), if exists, is believed to be cold, collisionless, dissipationless, non-baryonic, barely interacting with baryonic matter except through gravity, and sufficiently smooth on large scales with a fluid-like behavior. The flow of dark matter can be best described by a self-gravitating collisionless fluid dynamics (SG-CFD). The statistics of dark matter density, velocity, acceleration, energy, momentum, and their redshift evolution play essential roles for structure formation and evolution. These information can be systematically extracted from cosmological N-body simulations by either i) a structural (halo-based) or ii) a statistical (correlation-based) approach. In this correlation-based statistical dataset, i) all particle pairs with any given separation r in a N-body system are identified; ii) statistical measures are calculated over all particle pairs with the same separation r (pairwise average); iii) the redshift (z) and scale (r) dependence of all statistical measures (correlation/moment/structure/dispersion/spectrum functions for density, velocity and potential etc.) are presented. </p> <p>Applications of cascade and statistical theory for dark matter and bulge-SMBH evolution:</p> <ol> <li>Dark matter particle mass ,size, and properties from energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.07240">arxiv</a> 2) <a href="https://zenodo.org/record/6640353">zenodo slides</a></li> <li>Origin of MOND acceleration & deep-MOND from acceleration fluctuation & energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2203.05606">arxiv</a> 2) <a href="https://zenodo.org/record/6640386">zenodo slides</a></li> <li>The baryonic-to-halo mass relation from mass and energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2203.06899">arxiv</a> 2) <a href="https://zenodo.org/record/6640355">zenodo slides</a></li> <li>Universal scaling laws and density slope for dark matter haloes: 1) <a href="http://doi.org/10.48550/arXiv.2209.03313">arxiv</a> 2) <a href="https://zenodo.org/record/7059193">zenodo slides</a> 3) <a href="http://doi.org/10.1038/s41598-023-31083-z">paper</a></li> <li>Dark matter halo mass functions and density profiles from mass/energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2210.01200">arxiv</a> 2) <a href="https://zenodo.org/record/7146473">zenodo slides</a> 3) <a href="https://doi.org/10.1038/s41598-023-42958-6">paper</a></li> <li>Energy cascade for distribution and evolution of supermassive black holes (SMBHs): 2) <a href="http://doi.org/10.5281/zenodo.7490502">zenodo slides</a></li> </ol> <p>Condensed slides for all applications "<a href="http://doi.org/10.5281/zenodo.7508310">Cascade Theory for Turbulence, Dark Matter, and bulge-SMBH evolution </a>"</p> <p>The two relevant datasets and accompanying presentation can be found at: </p> <ol> <li><a href="https://doi.org/10.5281/zenodo.6541230">Dark matter flow dataset Part I: Halo-based statistics from cosmological N-body simulation</a> </li> <li><a href="https://doi.org/10.5281/zenodo.6569898">Dark matter flow dataset Part II: Correlation-based statistics from cosmological N-body simulation</a>.</li> <li><a href="https://doi.org/10.5281/zenodo.6569901">A comparative study of Dark matter flow & hydrodynamic turbulence and its applications</a></li> </ol> <p>The same dataset also available on Github at: <a href="https://github.com/ZhijieXu2022/dark_matter_flow_dataset/">Github: dark_matter_flow_dataset</a> and zenodo at: <a href="http://doi.org/10.5281/zenodo.6586212">Dark matter flow dataset from cosmological N-body simulation</a>.</p> <p>Cascade and statistical theory developed by these datasets:</p> <ol> <li>Inverse mass cascade in dark matter flow and effects on halo mass functions: 1) <a href="http://doi.org/10.48550/arXiv.2109.09985">arxiv</a> 2) <a href="https://zenodo.org/record/6639536">zenodo slides</a> </li> <li>Inverse mass cascade and effects on halo deformation, energy, size, and density profiles: 1) <a href="http://doi.org/10.48550/arXiv.2109.12244">arxiv</a> 2) <a href="https://zenodo.org/record/6640337">zenodo slides</a></li> <li>Inverse energy cascade in dark matter flow and effects of halo shape: 1) <a href="http://doi.org/10.48550/arXiv.2110.13885">arxiv</a> 2) <a href="https://zenodo.org/record/6640331">zenodo slides</a></li> <li>The mean flow, velocity dispersion, energy transfer and evolution of dark matter halos: 1) <a href="http://doi.org/10.48550/arXiv.2201.12665">arxiv</a> 2) <a href="https://zenodo.org/record/6640380">zenodo slides</a></li> <li>Two-body collapse model and generalized stable clustering hypothesis for pairwise velocity 1) <a href="http://doi.org/10.48550/arXiv.2110.05784">arxiv</a> 2) <a href="https://zenodo.org/record/6640306">zenodo slides</a></li> <li>Energy, momentum, spin parameter in dark matter flow and integral constants of motion: 1) <a href="http://doi.org/10.48550/arXiv.2202.04054">arxiv</a> 2) <a href="https://zenodo.org/record/6640322">zenodo slides</a></li> <li>Maximum entropy distributions of dark matter in ΛCDM cosmology: 1) <a href="http://doi.org/10.48550/arXiv.2110.03126">arxiv</a> 2) <a href="https://zenodo.org/record/6640373">zenodo slides</a> 3) <a href="http://doi.org/10.1051/0004-6361/202346429">paper</a></li> <li>Halo mass functions from maximum entropy distributions in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2110.09676">arxiv</a> 2) <a href="https://zenodo.org/record/6640325">zenodo slides</a></li> <li>On the statistical theory of self-gravitating collisionless dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.00910">arxiv</a> 2) <a href="https://zenodo.org/record/6640705">zenodo slides</a> 3) <a href="http://doi.org/10.1063/5.0151129">paper</a></li> <li>High order kinematic and dynamic relations for velocity correlations in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.02991">arxiv</a> 2) <a href="https://zenodo.org/record/6640684">zenodo slides</a></li> <li>Evolution of density and velocity distributions and two-thirds law for pairwise velocity: 1) <a href="http://doi.org/10.48550/arXiv.2202.06515">arxiv</a> 2) <a href="https://zenodo.org/record/6640676">zenodo slides</a></li> </ol>
Simulated river flow and temperature in regulated river systems in the southeastern United States
<p>Streamflow and stream temperature are important water resources variables, and regional-scale simulations are essential for water resources management and multi-sector assessment for large regions, e.g., regional ecological assessment and power system planning. In large-scale stream temperature modeling practices, reservoir thermal stratification is mostly ignored. We have synthesized a process-based modeling approach, consisting of a series of established models, to simulate streamflow and stream temperature for a complicated river-reservoir system, which explicitly considers thermal stratification. This approach consists of a large-scale, spatially-distributed hydrological model (Variable Infiltration Capacity or VIC; Liang et al., 1994; Hamman et al., 2018), a river routing model (Model for Scale Adaptive River Transport or MOSART; Li et al., 2013), coupled to a spatially-distributed water management model (WM; Voisin et al., 2013, 2017), and a stream temperature model (River Basin Model or RBM; Yearsley, 2009; 2012) that includes a two-layer reservoir thermal stratification module (2L; Niemeyer et al., 2018). To generate this dataset, we applied this modeling approach at a temporal resolution of 1 day and a spatial resolution of 1/8º to river systems in the southeastern United States that include 271 major reservoirs. We used an ensemble of downscaled meteorological forcing data from 20 global climate models (GCM) based on RCP8.5 to simulate potential climate change impacts. This dataset includes simulated river flow and temperatures for both historical (1980-2009; 1980s) and future periods (2070-2099; 2080s). The simulations for the 1980s are based on the gridMet data set (Abatzolglou, 2013), which also forms the basis for the statistical downscaling that is applied to each of the climate models. All simulations for the 2080s are based on downscaled climate model outputs. This dataset includes streamflow and stream temperature using both unregulated and regulated model setups to quantify the impacts of reservoir regulations. The unregulated setup does not account for withdrawals and impoundments in the river system. The stream temperature in the unregulated model setups is constant in each river cross section. In the regulated setup, we explicitly considered reservoir regulation, thermal stratification, and water withdrawal. For a more detailed description of the model configuration, please see Cheng et al. (2020).</p> <p> </p> <p>File structure and filenames: The archive includes two directories, named “streamflow/” and “stream_temperature/”, which contain model output for streamflow and stream temperature, respectively. Within each directory, subdirectories named “regulated/” and “unregulated/” contain model output for the regulated and unregulated model setups, respectively. All data files are in netCDF format and provide model outputs at a temporal resolution of 1 day and a spatial resolution of 1/8º. The unit for streamflow is m<sup>3</sup>/s and the unit for stream temperature is °C.</p> <p> </p> <p>Files are constructed as follows:</p> <p>SERC.<climate simulation>.RCP85.<model setup>.<variable>.nc</p> <p>where</p> <ul> <li><climate simulation> is either ‘historical’ for the simulation that represents the 1980s or an abbreviation that indicates the climate model for the simulations that represent the 2080s. The abbreviations for the climate models are shown in column 1 in the Table below.</li> <li><model setup> is either ‘regulated’ or ‘unregulated’ for the regulated and unregulated model setups, respectively.</li> <li><variable> is either ‘streamflow’ or ‘stream_temperature’ for streamflow and stream temperature, respectively.</li> </ul> <p>More details please see README.pdf</p>
Ionization rate simulation data for "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows"
<p><strong>Background</strong></p> <p>This is a set of 3d data containing ionization rates computed from Hyburn hydrodynamic simulations contained in a Matlab .mat file, along with a plot in both .png and Matlab .fig format, and a Matlab script for plotting.</p> <p>This data is used in figure 5 of the paper "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows".</p> <p>Electric sparks and explosive flows have long been associated with each other. Flowing dust particles originate charge through contact and separate based on inertia, resulting in strong electric fields supporting sparks. These sparks can cause explosions in dusty environments, especially those rich in carbon, such as coal mines and grain elevators. Recent observations of explosive events in nature and decompression experiments indicate that supersonic flows of explosions may alter the electrical discharge process. Shocks may suppress parts of the hierarchy of the discharge phenomena, such as leaders. In our decompression experiments, a shock tube ejects a flow of gas and particles into an expansion chamber. We imaged an illuminated plume from the decompression of a mixture of argon and <100 mg of diamond particles and observe sparks occurring below the sharp boundary of a condensation cloud. We also performed hydrodynamics simulations of the decompression event that provide insight into the conditions supporting the observed behavior. Simulation results agree closely with the experimentally observed Mach disk shock shape and height. This represents direct evidence that the sparks are sculpted by the outflow. The spatial and temporal scale of the sparks transmit an impression of the shock tube flow, a connection that could enable novel instrumentation to diagnose currently inaccessible supersonic granular phenomena.</p> <p><strong>Accessing Data</strong></p> <p>The .mat file can be opened in Matlab to examine data. The 3d arrays contained therein can be viewed in various ways, including using the enclosed script with syntax like plot_isosurfaces(xg,yg,zg,density,max(density(:)),pressure,max(pressure(:))) to produce the included isosurface plot.</p> <p>The data arrays contained are:</p> <p>e: electric field magnitude</p> <p>alpha: ionization rate lengths: ionization lengths (equal to 1/alpha)</p> <p>eOverN: electric field divided by gas number density</p> <p>alphaOverN: ionization rate divided by gas number</p> <p>density density: gas mass density</p> <p>pressure: gas pressure</p> <p>x,y,z: spatial coordinates</p> <p>xg,yg,zg: spatial coordinates in 3d meshgrid format, for Matlab plotting</p> <p>The electric field e was artificially generated from velocities in Hyburn output; alpha was computed from BOLSIG+ with Hyburn input; density and pressure data were from Hyburn.</p> <p> </p>
Simulated datasets for detector and particle flow reconstruction: CLIC detector, machine learning format
<p><strong>Synopsis</strong></p> <p>Machine-learning friendly format of tracks, clusters and target particles in electron-positron events, simulated with the CLIC detector. Ready to be used with <a href="https://zenodo.org/records/14930299">jpata/particleflow:v2.3.0</a>. Derived from the EDM4HEP ROOT files in <a href="https://zenodo.org/record/8260741">https://zenodo.org/record/8260741</a>.</p> <ul> <li>clic_edm_ttbar_pf.zip: e+e- -> ttbar, center of mass energy at 380 GeV</li> <li>clic_edm_qq_pf.zip: e+e- -> Z* -> qqbar, center of mass energy at 380 GeV</li> <li>clic_edm_ww_fullhad_pf.zip: e+e- -> WW -> W decaying hadronically, center of mass energy at 380 GeV</li> <li>clic-tfds.ipynb: an example notebook on how to load the files</li> </ul> <p><strong>Contents</strong></p> <p>Each .zip file contains the dataset in the <a href="https://github.com/tensorflow/datasets">tensorflow-datasets</a>, <a href="https://github.com/google/array_record">array_record</a> format. We have split the full datasets into 10 subsets, due to space considerations on zenodo, two subsets from each dataset are uploaded. Each dataset contains a train and test split of events.</p> <p><strong>Dataset semantics (to be updated)</strong></p> <p>Each dataset consists of events that can be iterated over using the tensorflow-datasets library and used in either tensorflow or pytorch. Each event has the following information available:</p> <ul> <li>X: the reconstruction input features, i.e. tracks and clusters</li> <li>ytarget: the ground truth particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle</li> <li>ycand: the baseline Pandora PF particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle</li> </ul> <p>The full semantics, including the list of features for X, are available at https://github.com/jpata/particleflow/blob/v2.3.0/mlpf/heptfds/clic_pf_edm4hep/utils_edm.py and https://github.com/jpata/particleflow/blob/v2.3.0/mlpf/data/key4hep/postprocessing.py.</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>
Molecular simulations of nanoscale two-phase Couette flow of a water-hexane system on a hydrophobic substrate
<p>This dataset contains the output of Molecular Dynamics simulations (MD) of two-phase Couette flow of water/hexane biphasic systems, in terms of density, velocity and temperature fields. Instructions on how to read and analyze the output files in the <code>.tar.gz</code> archives can be found in these previously-published datasets: <a href="https://doi.org/10.5281/zenodo.8077915">https://doi.org/10.5281/zenodo.8077915</a>, <a href="https://doi.org/10.5281/zenodo.6541983">https://doi.org/10.5281/zenodo.6541983</a></p> <p>The run output files are labeled using the following pattern: <code>hex-ca<capillary-number>-q<partial-charge>.tar.gz</code>. It is possible to obtain the wall speed/contact line speed from the capillary number using the following formula: <code>u_w = U_0*<capillary-number></code>, with <code>U_0 = 37.246 m/s</code>.</p> <p>To reproduce the runs it is necessary to use a specific version of Gromacs that allows for a special algorithm of pressure scaling with position restraints. The code can be obtained by cloning <a href="https://github.com/MicPellegrino/gromacs-flow-field.git">https://github.com/MicPellegrino/gromacs-flow-field.git</a>, and switching to the <code>flow-field-grid-visco-coms-deform</code> branch.</p> <p>The folder <code>conf-wat-hex.zip</code> contains the configuration files to reproduce MD simulations. To prepare the equilibration runs at constant pressure, run after having installed Gromacs:</p> <p><code>gmx grompp -f npt.mdp -p topology.top -c before-npt.gro -r before-npt.gro -o system-npt.tpr</code></p> <p>while to prepare the shear runs:</p> <p><code>gmx grompp -f shear.mdp -p topology.top -c after-npt.gro -r lambda0.gro -rb lambda1.gro -o system-shear.tpr</code></p> <p>Simulations are launched by running:</p> <p><code>gmx mdrun -v -s <tpr-file-name>.tpr <possibly-other-mdrun-flags></code></p> <p>Have fun simulating!</p>
Molecular simulations of nanoscale two-phase Couette flow of a water-hexane system on a hydrophilic substrate
<p>This dataset contains the output of Molecular Dynamics simulations (MD) of two-phase Couette flow of water/hexane biphasic systems, in terms of density, velocity and temperature fields. Instructions on how to read and analyze the output files in the <code>.tar.gz</code> archives can be found in these previously-published datasets: <a href="https://doi.org/10.5281/zenodo.8077915">https://doi.org/10.5281/zenodo.8077915</a>, <a href="https://doi.org/10.5281/zenodo.6541983">https://doi.org/10.5281/zenodo.6541983</a></p> <p>The run output files are labeled using the following pattern: <code>hex-ca<capillary-number>-q<partial-charge>.tar.gz</code>. It is possible to obtain the wall speed/contact line speed from the capillary number using the following formula: <code>u_w = U_0*<capillary-number></code>, with <code>U_0 = 37.246 m/s</code>.</p> <p>To reproduce the runs it is necessary to use a specific version of Gromacs that allows for a special algorithm of pressure scaling with position restraints. The code can be obtained by cloning <a href="https://github.com/MicPellegrino/gromacs-flow-field.git">https://github.com/MicPellegrino/gromacs-flow-field.git</a>, and switching to the <code>flow-field-grid-visco-coms-deform</code> branch.</p> <p>The folder <code>conf-wat-hex.zip</code> contains the configuration files to reproduce MD simulations. To prepare the equilibration runs at constant pressure, run after having installed Gromacs:</p> <p><code>gmx grompp -f npt.mdp -p topology.top -c before-npt.gro -r before-npt.gro -o system-npt.tpr</code></p> <p>while to prepare the shear runs:</p> <p><code>gmx grompp -f shear.mdp -p topology.top -c after-npt.gro -r lambda0.gro -rb lambda1.gro -o system-shear.tpr</code></p> <p>Simulations are launched by running:</p> <p><code>gmx mdrun -v -s <tpr-file-name>.tpr <possibly-other-mdrun-flags></code></p> <p>Have fun simulating!</p>
3D geological models of dolomitized clinoforms and flow simulation results: scenario 2 in Teoh, C.P. et al (2021)
<p>3D geological models of dolomitized clinoforms (10 different realisations) and flow simulation results according to Scenario 2 in Teoh, C.P. et al (2021) doi:<a href="http://doi.org/10.1016/j.marpetgeo.2021.105344">10.1016/j.marpetgeo.2021.105344</a>.<br> Models are built using surface-based modelling approach (doi:<a href="https://doi.org/10.1007/s11004-018-9764-8">10.1007/s11004-018-9764-8</a>). Flow simulations are run with IC-FERST, using unstructured tetrahedral meshes that adapt to geological heterogeneity and flow behaviour throughout the simulation to improve simulation quality and performance.</p> <p>For each of the 10 stochastic realisations, 5 geological models are available with corresponding flow simulation results:<br> - Only clinoforms and facies boundaries<br> - 1 dolomite body per clinothem (~20% dolomite)<br> - 2 dolomite bodies per clinothem (~40% dolomite)<br> - 3 dolomite bodies per clinothem (~60% dolomite)<br> - 4 dolomite bodies per clinothem (~80% dolomite)<br> <br> Input model files for simulation are provided in Exodus (.e) and GMSH (.msh) formats.<br> Flow simulation settings are provided for IC-FERST in .mpml files (<a href="http://multifluids.github.io/">multifluids.github.io</a>)<br> Flow simulation results are provided as:</p> <ul> <li> 3D unstructured adaptive mesh in .vtu format, which can be opened with Paraview (www.paraview.org). Time interval between successive mesh outputs is 1 month.</li> <li> In- and outflow rates and volumetric proportions per phase in .csv</li> </ul> <p>Naming of files and folders:<br> <em>Sxxxxxx_yyyyz</em> where:<br> '<em>xxxxxx</em>' is the stochastic seed number used to sample the input statistics and create the geological model<br> '<em>yyyy</em>' is either 'clino' or 'dolo' to indicate if the model represents respectively only clinoforms, or contains dolomite bodies <br> '<em>z</em>' corresponds to the number of dolomite bodies per clinothem</p>
3D geological models of dolomitized clinoforms and flow simulation results: scenario 1 in Teoh, C.P. et al (2021)
<p>3D geological models of dolomitized clinoforms (10 different realisations) and flow simulation results according to Scenario 1 in Teoh, C.P. et al (2021) doi:<a href="http://doi.org/10.1016/j.marpetgeo.2021.105344">10.1016/j.marpetgeo.2021.105344</a>.<br> Models are built using surface-based modelling approach (doi:<a href="https://doi.org/10.1007/s11004-018-9764-8">10.1007/s11004-018-9764-8</a>). Flow simulations are run with IC-FERST, using unstructured tetrahedral meshes that adapt to geological heterogeneity and flow behaviour throughout the simulation to improve simulation quality and performance.</p> <p>For each of the 10 stochastic realisations, 5 geological models are available with corresponding flow simulation results:<br> - Only clinoforms and facies boundaries<br> - 1 dolomite body per clinothem (~20% dolomite)<br> - 2 dolomite bodies per clinothem (~40% dolomite)<br> - 3 dolomite bodies per clinothem (~60% dolomite)<br> - 4 dolomite bodies per clinothem (~80% dolomite)<br> <br> Input model files for simulation are provided in Exodus (.e) and GMSH (.msh) formats.<br> Flow simulation settings are provided for IC-FERST in .mpml files (<a href="http://multifluids.github.io/">multifluids.github.io</a>)<br> Flow simulation results are provided as:</p> <ul> <li> 3D unstructured adaptive mesh in .vtu format, which can be opened with Paraview (www.paraview.org). Time interval between successive mesh outputs is 1 month.</li> <li> In- and outflow rates and volumetric proportions per phase in .csv</li> </ul> <p>Naming of files and folders:<br> <em>Sxxxxxx_yyyyz</em> where:<br> '<em>xxxxxx</em>' is the stochastic seed number used to sample the input statistics and create the geological model<br> '<em>yyyy</em>' is either 'clino' or 'dolo' to indicate if the model represents respectively only clinoforms, or contains dolomite bodies <br> '<em>z</em>' corresponds to the number of dolomite bodies per clinothem</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>
MCMC simulations from the posterior distributions of European migration flows
<p>This folder contains three RData files each containing MCMC simulations from the posterior distributions of a Bayesian hierarchal model used to estimate European migration flows, developed as part of the project Quantifying Migration Scenarios for Better Policy (QuantMig, <a href="https://eur03.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.quantmig.eu%2F&data=05%7C01%7CP.W.Smith%40soton.ac.uk%7Cc166e7bff7f840a7983b08db94af1244%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C638267252014422806%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=yMDYBngYz%2BnzvXmNj6MqseYwso0ICNbREE8TmqQ%2BRJ0%3D&reserved=0">www.quantmig.eu</a>); see Aristotelous, Smith and Bijak (2022) for details. The first set of estimates are disaggregated by origin, destination and time, a breakdown which we denote as ODT. The second and third sets of estimates are again disaggregated by origin, destination and time, but they are additionally disaggregated by other factors. The second set is further disaggregated by age and sex and the third by just birth region. We respectively denote these breakdowns as ODAST and ODBT. Also, included in this folder is an R script to calculate summaries of these posterior distributions (means, and lower and upper quartiles) and output them to csv files, which are also in the folder. For further details, see deliverable_6_4_v1_2.pdf also in the folder.</p>
Simulated datasets for detector and particle flow reconstruction: CLIC detector
<p><strong>Data description</strong></p> <p>Datasets generated using Key4HEP and the CLIC detector model suitable for particle flow reconstruction studies.</p> <p>The datasets contain generator particles, reconstructed tracks and calorimeter hits, reconstructed Pandora PF particles and their respective links in the <a href="https://doi.org/10.5281/zenodo.7221473">EDM4HEP</a> format.</p> <p>The following processes have been simulated with Pythia 8:</p> <ul> <li>p8_ee_tt_ecm380: ee -> ttbar, center of mass energy at 380 GeV</li> <li>p8_ee_qq_ecm380: ee -> Z* -> qqbar, center of mass energy at 380 GeV</li> <li>p8_ee_ZH_Htautau: ee -> ZH -> Higgs decaying to tau leptons, center of mass energy at 380 GeV</li> <li>p8_ee_WW_fullhad: ee -> WW -> W decaying hadronically, center of mass energy at 380 GeV</li> <li>p8_ee_tt_ecm380_PU10: ee -> ttbar with on average 10 Poisson-distributed events from ee->gg overlayed, center of mass energy at 380 GeV</li> </ul> <p>The following single particle gun samples have been generated with ddsim:</p> <ul> <li>e+/e-: single electron with energy between 1 and 100 GeV</li> <li>mu+/mu-: single muon with energy between 1 and 100 GeV</li> <li>kaon0L: single K0L with energy between 1 and 100 GeV</li> <li>neutron: single neutron with energy between 1 and 100 GeV</li> <li>pi+/pi-: single charged pion with energy between 1 and 100 GeV</li> <li>pi0: single neutral pion with energy between 1 and 100 GeV</li> <li>gamma: single photon with energy between 1 and 100 GeV</li> </ul> <p>The detector simulation has been done with Geant4, the reconstruction with Marlin interfaced via Key4HEP which includes PF reconstruction with Pandora, all using publicly available models and code.</p> <p> </p> <p><strong>Contents</strong></p> <p>This record includes the following files:</p> <ul> <li>*_10files.tar: small archives of 10 files for each data sample, suitable for testing</li> <li>dataset_full.txt: the full list of files, hosted at the Julich HPC courtesy of the Raise CoE project, ~2.5TB total</li> <li>*.cmd: the Pythia8 cards</li> <li>pythia.py: the pythia steering code for Key4HEP</li> <li>run_sim.sh: the steering script for generating, simulating and reconstructing a single file of 100 events from the p8_ee_tt_ecm380, p8_ee_qq_ecm380, p8_ee_ZH_Htautau, p8_ee_WW_fullhad datasets</li> <li>run_sim_pu.sh: the steering script for generating, simulating and reconstructing a single file of 100 events from the p8_ee_tt_ecm380_PU10 dataset</li> <li>run_sim_gun.sh: the steering script for generating the single-particle gun samples</li> <li>run_sim_gun_np.sh: the steering script for generating multi-particle gun samples (extensive datasets have not yet been generated)</li> <li>check_files.py: the main driver script that configures the full statistics and creates submission scripts for all the simulations</li> <li>PandoraSettings.zip: the settings used for Pandora PF reconstruction</li> <li>main19.cc: the Pythia8+HepMC driver code for generating the events with PU overlay</li> <li>clicRec_e4h_input.py: the steering configuration of the reconstruction modules in Key4HEP</li> <li>clic_steer.py: the steering configuration of the Geant4 simulation modules in Key4HEP</li> <li>clic-visualize.ipynb: an example notebook demonstrating how the dataset can be loaded and events visualized in Python</li> <li>visualization.mp4: an example visualization of the hits and generator particles of a single ttbar event from the dataset</li> </ul> <p> </p> <p><strong>Dataset semantics</strong></p> <p>Each file consists of event records. Each event contains structured branches of the relevant physics data. The branches relevant to particle flow reconstruction include:</p> <ul> <li>MCParticles: the ground truth generator particles</li> <li>ECALBarrel, ECALEndcap, ECALOther, HCALBarrel, HCALEndcap, HCALOther, MUON: reconstructed hits in the various calorimeter subsystems</li> <li>SiTracks_Refitted: the reconstructed tracks</li> <li>PandoraClusters: the calorimeter hits, clustered by Pandora to calorimeter clusters</li> <li>MergedRecoParticles: the reconstructed particles from the Pandora particle flow algorithm</li> <li>CalohitMCTruthLink: the links between MC particles and reconstructed calorimeter hits</li> <li>SiTracksMCTruthLink: the links between MC particles and reconstructed tracks</li> </ul> <p>The full details of the EDM4HEP format are available <a href="https://doi.org/10.5281/zenodo.7221473">here</a>.</p> <p> </p> <p><strong>Dataset characteristics</strong></p> <p>The full dataset in dataset_full.txt consists of 43 tar files of up to 100GB each. The tar files contain in total 58068 files, 2.5TB in the ROOT EDM4HEP format.</p> <p>The subset in *_10files.tar for consists of 150 files, 26GB in the ROOT EDM4HEP format.</p> <p> </p> <p><strong>How can you use these data?</strong></p> <p>The ROOT files can be directly loaded with the <a href="https://zenodo.org/record/8239801">uproot</a> Python library.</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>These are simulated samples suitable for conceptual machine learning R&D and software performance studies. They have not been calibrated with respect to real data, and should not be used to derive physics projections about the detectors.</p> <p>Neither CLIC nor CERN endorse any works, scientific or otherwise, produced using these data. All releases will have a unique DOI that you are requested to cite in any applications or publications.</p>
Simulation data and scripts for CFD-DEM simulation of saturated bi-disperse granular flows
<ul> <li>Data set 1 - contains the raw data required to replicate and validate all plots in the main article. </li> <li>Sample case - a .zip file which includes codes which are needed to simulate a CFD-DEM case of a steady granular flow in water with cyclic boundaries in the stream wise direction. Also enclosed is a ReadMe.txt file detailing the implementation instructions for both Esys particle and OpenFOAM codes. Download links for Esys particle and OpenFOAM are also included</li> <li>Geo file generator - a .zip file which includes Esys particle codes that can be used to generate a .geo file specifying the initial position of the particles used in the test simulations. A ReadMe.txt file is enclosed with more detailed implementation instructions.</li> </ul>
Averaged results of blood flow simulations with discrete RBC tracking for microvascular networks
<p>The dataset contains the results for blood flow simulations in 3 cerebral micorvascular networks.The microvascular networks are from the mouse parietal cortex (Blinder et al., 2013) and embedded in a tissue volume of approximately 1 cubic mm. For the blood flow simulations we used a numercial model with discrete tracking of RBCs which is described in Schmid et al., 2017.</p> <p>For each network the following data are provided:<br> - Microvascular network with averaged flow and pressure field, as well as averaged values for the distribution and motion of red blood cells (RBCs).<br> - RBC trajectories describing the motion of individual RBCs through the microvascular networks.<br> - The data is stored as a graph, i.e. vertices connected by edges.<br> - Details regarding the simulation parameters can be found in Schmid et al., 2017.<br> - Data format (pickle - files containing python dictonairies).<br> <br> <strong>Microvascular networks:</strong><br> <strong>edgesDict.pkl:</strong> dictionary with edge related data (dictionary keys: flow [um^3/ms], diameter [um], tuple [-], httBC [-], nkind [-], length [um], htt [-], nRBC [-], diameters [um], points [um])<br> <strong>verticesDict.pkl:</strong> dictionary with vertex related data (dictionary keys: pressure [mmHg], coordinates [um], pBC [mmHg])</p> <p>Additional comments on dictionary keys:<br> - pBC: pressure boundary conditions. 'None' for internal nodes. Assigned based on the hierarchical boundary conditions approach (see Schmid et al. 2017 for details)<br> - tuple: connectivity of graph, tuple of vertices<br> - httBC: tube hematocrit boundary conditions. 'None' for internal nodes. Constant value assigned.<br> - nkind: integere to describe the vessel type. 0: pial artery, 1: pial venule, 2: descending arteriole, 3: ascending venule, 4: capillaries, 5: unknown<br> - htt: tube hematocrit<br> - nRBC: number of red blood cells<br> - points: list of tortuous vessel coordinates per edge<br> - diameters: local diameter measurements associated to the 'points' key.</p> <p><br> <strong>RBC trajectories:</strong><br> <strong>RBC_trajectories.pkl: </strong>dictonary for each RBC with relevant tracking data (dictionary key: RBC index). The relavant tracking data per RBC is stored in another dictionary with the following keys: edges, lengths, times, pressure, nkindsMod, RBCleft</p> <p>Additional comments on dictionary keys per RBC:<br> - RBCleft: bool to indicate that RBC left the computational domain<br> - edges: edge indices through which the RBC moves on its way through the vasculature<br> - pressure: pressure [mmHg] values at the nodes along the RBC trajectory<br> - times: time [ms] the RBC spends in the respective edge segment<br> - nkindsMod: nkind at the nodes along the RBC trajectory <br> - lengths: cummulative length travelled [um]</p>
Investigating the Unsteady Dynamics of a Multi-Jet Impingement Cooling Flow Using Large Eddy Simulation - Promotional Video and Image
<p>Video: Volume rendering of temperature field obtained from a large eddy simulation of 9 inline impinging jets in a narrow channel.</p> <p>Image: Turbulent structures as isosurface of Q-criterion with an illustration of laser sheet used for PIV measurement.</p> <p>The results were presented at the ASME Turbo Expo 2024 (paper number GT2024-122465) and published in the ASME Journal of Turbomachinery (<a href="https://doi.org/10.1115/1.4066508">https://doi.org/10.1115/1.4066508</a>). The accepted manuscript of the paper is available under <a href="https://elib.dlr.de/207257/">https://elib.dlr.de/207257/</a>.</p> <p>The simulations were performed on DLR's HPC system <a href="https://www.dlr.de/en/research-and-transfer/research-infrastructure/hpc-cluster/cara">CARA</a> within the DLR project InnoCool.</p>
Near-inertial wave energetics modulated by background flows in a global model simulation
<p>This data set contains data from a forward global HYCOM simulation (EXPT 19.2) with realistic atmospheric forcing. This is a 4-km simulation with 41 layers. All data is on the native tri-polar grid. Data is stored as netcdf4 classic. The 2D data sets are 7055 x 9000 (lat x lon).</p> <p>More details and context of this data can be found in the article: <a href="https://doi.org/10.1175/JPO-D-21-0130.1">https://doi.org/10.1175/JPO-D-21-0130.1</a>. Please cite this article along with any use of this data.<br> Raja, K. J., Buijsman, M. C., Shriver, J. F., Arbic, B. K., & Siyanbola, O. (2022). Near-Inertial Wave Energetics Modulated by Background Flows in a Global Model Simulation, <em>Journal of Physical Oceanography</em>, <em>52</em>(5), 823-840.</p>
3D Flow Field of a subaqueous cylindrical pendulum from large eddy simulation with fluid structure interaction
<p>Relevant data of fluid structure interaction large eddy simulation of a subaqueous cylindrical pendulum to reproduce the major findings of the article "Fluid structure interaction of a subaqueous pendulum: Analyzing the effect of wake correction via large eddy simulations" published in Physics of Fluids. The article has been published as open access: https://doi.org/10.1063/5.0086557</p>
Dark matter flow dataset from cosmological N-body simulation
<p>Dark matter (DM), if exists, is believed to be cold, collisionless, dissipationless, non-baryonic, barely interacting with baryonic matter except through gravity, and sufficiently smooth on large scales with a fluid-like behavior. The flow of dark matter can be best described by a self-gravitating collisionless fluid dynamics (SG-CFD). The statistics of dark matter density, velocity, acceleration, energy, momentum, and their redshift evolution play essential roles for structure formation and evolution. These information can be systematically extracted from cosmological N-body simulations by either i) a structural (halo-based) or ii) a statistical (correlation-based) approach. In this correlation-based statistical dataset, i) all particle pairs with any given separation r in a N-body system are identified; ii) statistical measures are calculated over all particle pairs with the same separation r (pairwise average); iii) the redshift (z) and scale (r) dependence of all statistical measures (correlation/moment/structure/dispersion/spectrum functions for density, velocity and potential etc.) are presented. </p> <p>Applications of cascade and statistical theory for dark matter and bulge-SMBH evolution:</p> <ol> <li>Dark matter particle mass ,size, and properties from energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.07240">arxiv</a> 2) <a href="https://zenodo.org/record/6640353">zenodo slides</a></li> <li>Origin of MOND acceleration & deep-MOND from acceleration fluctuation & energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2203.05606">arxiv</a> 2) <a href="https://zenodo.org/record/6640386">zenodo slides</a></li> <li>The baryonic-to-halo mass relation from mass and energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2203.06899">arxiv</a> 2) <a href="https://zenodo.org/record/6640355">zenodo slides</a></li> <li>Universal scaling laws and density slope for dark matter haloes: 1) <a href="http://doi.org/10.48550/arXiv.2209.03313">arxiv</a> 2) <a href="https://zenodo.org/record/7059193">zenodo slides</a> 3) <a href="http://doi.org/10.1038/s41598-023-31083-z">paper</a></li> <li>Dark matter halo mass functions and density profiles from mass/energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2210.01200">arxiv</a> 2) <a href="https://zenodo.org/record/7146473">zenodo slides</a> 3) <a href="https://doi.org/10.1038/s41598-023-42958-6">paper</a></li> <li>Energy cascade for distribution and evolution of supermassive black holes (SMBHs): 2) <a href="http://doi.org/10.5281/zenodo.7490502">zenodo slides</a></li> </ol> <p>Condensed slides for all applications "<a href="http://doi.org/10.5281/zenodo.7508310">Cascade Theory for Turbulence, Dark Matter, and bulge-SMBH evolution </a>"</p> <p>The two relevant datasets and accompanying presentation can be found at: </p> <ol> <li><a href="https://doi.org/10.5281/zenodo.6541230">Dark matter flow dataset Part I: Halo-based statistics from cosmological N-body simulation</a> </li> <li><a href="https://doi.org/10.5281/zenodo.6569898">Dark matter flow dataset Part II: Correlation-based statistics from cosmological N-body simulation</a>.</li> <li><a href="https://doi.org/10.5281/zenodo.6569901">A comparative study of Dark matter flow & hydrodynamic turbulence and its applications</a></li> </ol> <p>The same dataset also available on Github at: <a href="https://github.com/ZhijieXu2022/dark_matter_flow_dataset/">Github: dark_matter_flow_dataset</a> and zenodo at: <a href="http://doi.org/10.5281/zenodo.6586212">Dark matter flow dataset from cosmological N-body simulation</a>.</p> <p>Cascade and statistical theory developed by these datasets:</p> <ol> <li>Inverse mass cascade in dark matter flow and effects on halo mass functions: 1) <a href="http://doi.org/10.48550/arXiv.2109.09985">arxiv</a> 2) <a href="https://zenodo.org/record/6639536">zenodo slides</a> </li> <li>Inverse mass cascade and effects on halo deformation, energy, size, and density profiles: 1) <a href="http://doi.org/10.48550/arXiv.2109.12244">arxiv</a> 2) <a href="https://zenodo.org/record/6640337">zenodo slides</a></li> <li>Inverse energy cascade in dark matter flow and effects of halo shape: 1) <a href="http://doi.org/10.48550/arXiv.2110.13885">arxiv</a> 2) <a href="https://zenodo.org/record/6640331">zenodo slides</a></li> <li>The mean flow, velocity dispersion, energy transfer and evolution of dark matter halos: 1) <a href="http://doi.org/10.48550/arXiv.2201.12665">arxiv</a> 2) <a href="https://zenodo.org/record/6640380">zenodo slides</a></li> <li>Two-body collapse model and generalized stable clustering hypothesis for pairwise velocity 1) <a href="http://doi.org/10.48550/arXiv.2110.05784">arxiv</a> 2) <a href="https://zenodo.org/record/6640306">zenodo slides</a></li> <li>Energy, momentum, spin parameter in dark matter flow and integral constants of motion: 1) <a href="http://doi.org/10.48550/arXiv.2202.04054">arxiv</a> 2) <a href="https://zenodo.org/record/6640322">zenodo slides</a></li> <li>Maximum entropy distributions of dark matter in ΛCDM cosmology: 1) <a href="http://doi.org/10.48550/arXiv.2110.03126">arxiv</a> 2) <a href="https://zenodo.org/record/6640373">zenodo slides</a> 3) <a href="http://doi.org/10.1051/0004-6361/202346429">paper</a></li> <li>Halo mass functions from maximum entropy distributions in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2110.09676">arxiv</a> 2) <a href="https://zenodo.org/record/6640325">zenodo slides</a></li> <li>On the statistical theory of self-gravitating collisionless dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.00910">arxiv</a> 2) <a href="https://zenodo.org/record/6640705">zenodo slides</a> 3) <a href="http://doi.org/10.1063/5.0151129">paper</a></li> <li>High order kinematic and dynamic relations for velocity correlations in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.02991">arxiv</a> 2) <a href="https://zenodo.org/record/6640684">zenodo slides</a></li> <li>Evolution of density and velocity distributions and two-thirds law for pairwise velocity: 1) <a href="http://doi.org/10.48550/arXiv.2202.06515">arxiv</a> 2) <a href="https://zenodo.org/record/6640676">zenodo slides</a></li> </ol>
ScienceDex guides
Understand access before you commit
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.