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129 results for “Flow simulation”
Supplementary data for the article "A data-driven simulation of the trophallactic network and intranidal food flow dissemination in ants."
<p>Supplementary figures, tables and raw data for the article " "A data-driven simulation of the trophallactic network and intranidal food flow dissemination in ants."</p> <p><strong>Food sharing can occur in both social and non-social species but is crucial in eusocial species in which only some group members collect food. This food collection but also intranidal food distribution through trophallactic (i.e. mouth-to-mouth) exchanges are fundamental issues in eusocial insects. However, the behavioural rules underlying the regulation and the dynamics of food intake and the resulting networks of exchanges are poorly understood. In this study, we provide new insights </strong><strong>into</strong><strong> the behavioural rules underlying the structure of trophallactic networks and food dissemination dynamics within the colony. We build a simple data-driven model that implements interindividual variability and division of labour to investigate the processes of food accumulation/dissemination inside the nest, both at the individual and collective </strong><strong>levels</strong><strong>. We also test the alternative hypotheses (no variability and no division of labour). Division of labour with inter-individual variability predicts contrary to other models the food dynamics and exchange networks. We establish the links between the interindividual heterogeneity of the trophallactic behaviours, the food flow dynamics and network of trophallactic events</strong>.<strong> Despite the relative simplicity of the model rules, efficient trophallactic networks may emerge as the ones observed in ants leading to better understanding of evolution of such societies.</strong></p> <p> </p>
Uncertainty quantification in cerebral circulation simulations focusing on the collateral flow: Surrogate model approach with machine learning
<p>Data and code underlying the findings reported in the paper titled "Uncertainty quantification in cerebral circulation simulations focusing on the collateral flow: Surrogate model approach with machine learning."</p>
Data supporting paper "Efficient numerical simulation of density-driven flows: Application to the 2- and 3-D Elder problem"
<p>Data supporting paper "Efficient numerical simulation of density-driven flows: Application to the 2- and 3-D Elder problem", Water Resources Research.</p>
Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for multi-capillary dilation scenarios mimicking pericyte ablation
<p>Documentation to reproduce in silico analyses related to the manuscript<br> <strong>Pericyte remodelling is deficient in the aged brain and contributes to impaired capillary flow and structure</strong></p> <p>by</p> <p>Andrée-Anne Berthiaume, Franca Schmid, Stefan Stamenkovic, Vanessa Coelho-Santos, Cara D. Nielson, Bruno Weber, Mark W. Majesky and Andy Y. Shih</p> <p>Published in<br> Nature Communications (doi: 10.1038/s41467-022-33464-w)</p> <p>All simulations are performed based on the in silico blood flow model with discrete red blood cell (RBC) tracking as described in Schmid et al., 2017, PLoS Comp Biol (doi: <a href="https://doi.org/10.1371/journal.pcbi.1005392">10.1371/journal.pcbi.1005392</a>). The bi-phasic blood flow simulations have been performed in two realistic microvascular networks from the somatosensory cortex of the mouse first published in Blinder et al., 2013, Nature Neuroscience (doi: 10.1038/nn.3426). </p> <p>For further information and instructions please contact Franca Schmid (franca.schmid@unibe.ch, orcid.org/0000-0002-0689-9366).</p> <p><br> <strong>Simulation results:</strong></p> <p>All time-averaged simulation results are saved as vascular graphs building on the python library igraph and stored as python pickle files (Python 2.7). For each simulation two files are available: <em>verticesDict.pkl</em> and <em>edgesDict.pkl</em>containing all vertex and edge specific data, respectively. A summary of the vertex and edge attributes is provided below. The folder <em>Baseline</em> contains the simulation results for microvascular network 1 (MVN1) and MVN2 for the reference simulation, i.e. without any dilation. Folder <em>Dilated</em> contains the simulation results mimicking the four pericyte ablation scenarios. Subfolders <em>dc_x.x</em> contain the simulation results for the different diameter changes. Note that, folder <em>dc_0.0</em> contains no new simulation results but is a dummy folder containing the information about the vessels to be dilated for the different dilation scenarios (namely edge attribute: <em>toDilate</em> and <em>base_capillary</em>). </p> <p> </p> <p><strong>Reproducing figure 8:</strong></p> <p>Panels a-c: created by illustrating the simulation results with the open source software Paraview (v5.7.0).<br> Panels d-f & h: can be generated by executing make_all_figures.py in Python 2.7 within the provided folder structure.<br> Panel g: can be generated by executing make_figure_8g.py after installation of the the vgm-framework (further information see below). </p> <p><br> Output: All created Figures are saved in the folder <em>Figures</em>. The associated source data is available in Excel format in the folder <em>SourceData</em>.</p> <p> </p> <p><strong>Edge attributes:</strong></p> <p>diameter: vessel diameter [µm]<br> mainAV: 1 if ascending venule main branch, 0 otherwise<br> connectivity: vertex tuple to define location of edge<br> flow: flow rate [µm<sup>3</sup>/ms]<br> mainDA: 1 if descending arteriole main branch, 0 otherwise<br> nkind: 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending venule, 4: capillary<br> htt: tube hematocrit [-]<br> toDilate: 1 if vessel is dilated for the current dilation scenario, 0 otherwise<br> base_capillary: 1 if vessel is the base capillary of the current dilation scenario, 0 otherwise</p> <p> </p> <p><strong>Vertex attributes:</strong></p> <p>index: vertex index<br> pressure: pressure [mmHg]<br> nkind: 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending venule, 4: capillary<br> coords: vertex coordinates x,y,z [µm]<br> pBC: pressure boundary conditions at inflow vertices [mmHg], None at internal nodes</p> <p> </p> <p><strong>Obtaining simulation results:</strong><br> General:</p> <ul> <li>Running bi-phasic blood flow simulations requires setting-up the vgm-framework available at: <a href="https://github.com/Franculino/vgm.git">https://github.com/Franculino/vgm.git</a> (v.1.0).</li> <li>vgm is written in Python 2.7 and builds on standard python libraries.</li> <li>vgm has been used on macOS, Ubuntu and Windows Systems.</li> <li>Installation time < 5min. Further details available within the vgm README.</li> <li>Runtime depends on the network size, the chosen blood flow model and the initial conditions (e.g. ~8hrs for a Restart simulation of MVN1 with the bi-phasic blood flow model, see Restarty.py).</li> <li>scripts/Test.py provides an example how a simulation can be initiated. A Demo case is provided (details see below).</li> <li>Output: sampledict_BackUp_xx.pkl</li> <li>The bi-phasic blood flow model can be applied on all kind of microvascular graphs.</li> </ul> <p>Specific for current application:</p> <ul> <li>Simulations are a restart on the statistical steady state of the baseline cases.</li> <li>All relevant pre-processing functions for the current study are available in scripts/find_stroke_locations.py. Further details are available from the definition of the different functions.</li> <li>The simulations are initiated with scripts/Restart.py.</li> <li>To obtain the time-averaged simulation results scripts/01_put_together_sampledicts.py and scripts/02_convergenceDiscrete.py need to be executed. This results in the file G_averaged.pkl that is used for further analyses.</li> </ul> <p>Demo:</p> <ul> <li>Contains a small hexagonal microvascular network to test the code.</li> <li>1) Run Test.py to start the simulation</li> <li>2) Run 01_put_together_sampledicts.py</li> <li>3) Run 02_convergenceDiscrete.py to obtain time-averaged results (<em>G_averaged.pkl</em>)</li> </ul>
A Hydrodynamic-Based Physical Unified Modeling for Simulating Shallow Landslide Local Failures, Mass Release and Debris Flow Run-out Extent Behavior
<p>Simulation results for manuscript "A Hydrodynamic-Based Physical Unified Modelling Framework for Simulating Shallow Landslide Local Failures, Mass Release and Debris Flow Run-out Extent Behaviour", submitted to Water Resources Research.</p>
A Hydrodynamic-Based Physical Unified Modeling for Simulating Shallow Landslide Local Failures, Mass Release and Debris Flow Run-out Extent Behavior
<p>Simulation results for manuscript "A Hydrodynamic-Based Physical Unified Modelling Framework for Simulating Shallow Landslide Local Failures, Mass Release and Debris Flow Run-out Extent Behaviour", submitted to Water Resources Research.</p>
Dataset used for "End-to-end simulation of particle physics events with Flow Matching and generator Oversampling" , https://arxiv.org/abs/2402.13684
Open the record for dataset details and reuse information.
GITM simulations about the impact of flow bursts on I/T
<p>Global Ionosphere-Thermosphere model (GITM), a non-hydrostatic model with flexible resolution, has been utilized to evaluate the influence of flow bursts, one type of localized strong enhancements of forcing on the global dynamics of the upper atmosphere. Specifically, three idealized configurations of flow burst have been examined, one flow burst with the typical spatial scale seen in the auroral zone, two neighboring flow bursts with the same spatial scale, and one flow burst with doubled spatial scale. The comparison between the cases with and without the flow burst illustrates the impact of flow burst. </p>
Hydrograph and Recession Flows Predictions Simulations using Deep Learning: Watershed Uniqueness and Objective Functions
<p>This resource contains the results presented in the paper titled 'Hydrograph and Recession Flows Simulations using Deep Learning: Watershed Uniqueness and Objective Functions' by Abhinav Gupta and Sean McKenna.<span><span><br></span></span></p> <p>'</p>
Continuous Flow Synthesis and Simulation-Supported Investigation of Tunable Plasmonic Gold Patchy Nanoparticles
<p>This is the dataset for the following manuscript "Continuous Flow Synthesis and Simulation-Supported Investigation of Tunable Plasmonic Gold Patchy Nanoparticles".</p> <p>Abstract</p> <p><span>Plasmonic nanoparticles have intriguing optical properties which make them suitable candidates for sensing or theranostic applications. Anisotropic patchy particles, where metal is locally deposited on the surface of a core particle, exhibit plasmon resonances that can be specifically adjusted for these applications. However, many existing synthesis routes are complex, yield too little material, or provide particles with limited optical tunability. In this work, we present a simple and scalable continuous flow synthesis of gold-on-polystyrene patchy particles with widely adjustable optical properties. By increasing the chloride concentration in the electroless deposition of gold, we slow down the redox reduction kinetics and obtain a dense patch morphology as well as a reduced nucleation rate. The latter is counteracted by introducing a low-level seeding approach where a small number of gold nanocrystals heterocoagulate with the core particles prior to patch growth. Seeding and patch growth are performed in a continuous flow set-up with two T-shaped milli-mixers. The resulting patchy particle samples exhibit a tunable dipolar plasmon peak between 600 nm and 1100 nm. We also investigate the structure-property relationship for our gold patchy particles using Finite Element Method simulations. After identifying a suitable patch shape model, we elucidate the influence of individual geometric parameters on the optical properties and show that the relationship holds true for a large range of patch coverages. Finally, we apply the relationship to explain the time-dependent change in the optical properties of as-synthesized patches by correlating it with the patch shape transformation revealed by electron microscopy.</span></p> <p> </p> <p>The uploaded data are sorted by figure.</p>
Animations depicting 2D prescribed-flow particle-based simulations described in arxiv.org:2308.05015
<p>File names contain the follwing information:</p> <ul> <li>singular=True or singular=False - setting controlling the type of immersion freezing parameterization</li> <li>seed=0 - value of the random seed used</li> <li>rhod_w_max=value - value of the amplitude of the prescribed-flow eddy stream function (dimension of density times velocity in SI units)</li> <li>"cld" or "ice" - set of variables plotted (either those pertinent to cloud water or ice water) </li> </ul>
Use of WRF-Hydro in postfire debris-flow hazard simulation
<p>This is the dataset used in a study named "Use of WRF-Hydro to Simulate Runoff-Generated Debris Flow Hazards in Burn Scars" by C. Li<sup>1</sup>, A. L. Handwerger<sup>2,3</sup>, J. Wang<sup>4</sup>, W. Yu<sup>5,6</sup>, X. Li<sup>7</sup>, N. J. Finnegan<sup>8</sup>, Y. Xie<sup>9,10</sup>, G. Buscarnera<sup>7</sup>, and D. E. Horton<sup>1</sup></p> <p><sup>1 </sup>Department of Earth and Planetary Sciences, Northwestern University</p> <p><sup>2 </sup>Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, CA, 90095, USA</p> <p><sup>3 </sup>Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA</p> <p><sup>4 </sup>Environmental Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA</p> <p><sup>5 </sup>Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder</p> <p><sup>6 </sup>NOAA/Global Systems Laboratory, 325 Broadway, Boulder, Colorado</p> <p><sup>7 </sup>Department of Civil and Environmental Engineering, Northwestern University</p> <p><sup>8 </sup>University of California Santa Cruz, Department of Earth and Planetary Sciences, Santa Cruz, CA, 95064, USA</p> <p><sup>9</sup> Program in Environmental Sciences, Northwestern University, 2145 Sheridan Road, Evanston, IL, 60208, USA</p> <p><sup>10 </sup>Department of Biological Sciences, Purdue University, 915 W State St, West Lafayette, IN 47907, USA</p>
Nonsmooth simulations of 3D Drucker-Prager granular flows and validation against experimental column collapses
<p> </p> <p># About</p> <p>This archive aims to reproduce the results of the article entitled: "***Nonsmooth simulations of 3D Drucker-Prager granular flows and validation against experimental column collapses***" *by Gauthier Rousseau, Thibaut Métivet, Hugo Rousseau, Gilles Daviet, and Florence Bertails-Descoubes*</p> <p> </p> <p>## Contents</p> <p> </p> <p>- The experimental data: raw videos, experimental information, velocity and profiles</p> <p>- The `sand6py` code which is a fork of [Daviet PhD][1] `sand6` code including [diphasic feature][2] and other new features developed for the paper (collapses scenarios with frictional door, hysteresis, python binding with `pybind11`)</p> <p>- Python scripts:</p> <p>- for performing velocimetry measurements</p> <p>- for running the `sand6` 3D simulations of granular column collapses</p> <p>- for plotting paper figures</p> <p><br> </p> <p>The git repository corresponding to `sand6py` is available on [https://gitlab.com/groussea/sand6py](https://gitlab.com/groussea/sand6py)</p> <p> </p> <p>A maintained version of sand6 is available on [https://gitlab.inria.fr/elan-public-code/sand6](https://gitlab.inria.fr/elan-public-code/sand6)</p> <p> </p> <p>## Figures</p> <p> </p> <p>the figures of the article are reproducible from the python scripts in the `python/figurejfm` path</p> <p> </p> <p>python dependencies are `matplotlib opencv jupyter vtk h5py tqdm scikit-image opyf`</p> <p> </p> <p>You may obtain all the required dependencies in a convenient conda environment using the following command line in your terminal:</p> <p> </p> <p>```shell</p> <p>conda create --channel conda-forge -n sand6env python=3.9 matplotlib opencv jupyter vtk h5py tqdm scikit-image</p> <p> </p> <p>conda activate sand6env</p> <p>```</p> <p> </p> <p>Corresponding e-mail: gauthier.rousseau@gmail.com</p> <p> </p> <p>[1]: tel.archives-ouvertes.fr/tel-01684673 "*Modeling and simulating complex materials subject to frictional contact: Application to fibrous and granular media*. Diss. Ph. D. Dissertation. Université Grenoble Alpes. , 2016"</p> <p>[2]: https://hal.inria.fr/hal-01458951 "Simulation of Drucker–Prager granular flows inside Newtonian fluids"</p> <p> </p>
Data set for phase-field simulation of epitaxial crystal growth in open fractures with lateral flow
<p>The numerical data in this repository consists of the simulation data of epitaxial crystal growth in open fractures with lateral flow. The simulations were performed using the software package named "Pace3D".</p> <p>The simulation data shows the grain structure, the concentration field and the fluid flow velocity in stream direction (if present) at intermediate stages. It was converted from the Pace3D output data format to VTK data format. The VTK files can be visualized using open source software packages like Paraview. For visualization the data has to be decompressed (e.g. with gzip, 7zip).</p>
Our simulated cerebral blood flow changed from pretreatment to posttreatment status between rehabilitation (see video 1, supplemental data) and ILIB groups (see video 2, supplemental data).
<p>Our simulated cerebral blood flow changed from pretreatment to posttreatment status between rehabilitation (see video 1, supplemental data) and ILIB groups (see video 2, supplemental data).</p> <p>Video S1 demonstrates that the cerebral blood flow changed from pretreatment to posttreatment status in one participant of rehabilitation.</p> <p>Video S2 demonstrates that the cerebral blood flow changed from pretreatment to posttreatment status in one participant of ILIB.</p>
Simulation data for: Semi-continuum modelling of unsaturated porous media flow to explain the Bauters' paradox
<p>This dataset includes the simulation data needed to create the plots for a manuscript: Semi-continuum modelling of unsaturated porous media flow to explain the Bauters' paradox. All data can be reproduced using the code of the semi-continuum model available in https://doi.org/10.5281/zenodo.6837742</p> <p>The readMe file was not included in Version 3 (all simulation data are the same as in version 3).</p>
A tightly coupled river-ocean model for simulating combined flood due to storm surge and river flow in coastal-urban areas
<p>Coastal flooding, resulting from storm surges or extreme river flows, causes significant causalities and damage to properties in low-lying areas. The simultaneous occurrence of river flows and storm surges, termed combined/compound events, exacerbates the flood risk compared to independent occurrences. Combined flood events are simulated with the help of hydraulic and hydrodynamic models using a loosely or tightly coupled approach. In the loosely coupled approach, a hydrodynamic model simulates storm surges first, and a hydraulic model then simulates inland flood due to river overflow considering surge as the boundary condition at the river mouth/estuary. Conversely, the tightly coupled approach involves simultaneous simulation of both river flow and storm surge by coding the mathematical representation of river and ocean flow dynamics in the same numerical model. This allows the interaction between river and ocean flows to be simulated anywhere in the combined river-ocean computational domain, making it highly relevant for simulating combined floods. However, existing models based on this approach encounter numerical instability, especially in inland regions where topography variation is steep and highly uneven. Also, such combined models are highly limited for large scale applications. Therefore, this research focuses on developing a tightly coupled 2D finite volume river-ocean model called IROMS-C2D. The developed model intends to address the limitations of the previous models and provide a stable solution framework for the simulation of combined flooding resulting from the interaction of storm surges and river flows in coastal urban areas. Further, it enhances our understanding of flood risks in coastal areas, particularly in urban settings, and facilitates the formulation of effective measures for flood control and adaptation of coastal infrastructure.</p>
A tightly coupled river-ocean model for simulating combined flood due to storm surge and river flow in coastal-urban areas
<p>Coastal flooding, resulting from storm surges or extreme river flows, causes significant causalities and damage to properties in low-lying areas. The simultaneous occurrence of river flows and storm surges, termed combined/compound events, exacerbates the flood risk compared to independent occurrences. Combined flood events are simulated with the help of hydraulic and hydrodynamic models using a loosely or tightly coupled approach. In the loosely coupled approach, a hydrodynamic model simulates storm surges first, and a hydraulic model then simulates inland flood due to river overflow considering surge as the boundary condition at the river mouth/estuary. Conversely, the tightly coupled approach involves simultaneous simulation of both river flow and storm surge by coding the mathematical representation of river and ocean flow dynamics in the same numerical model. This allows the interaction between river and ocean flows to be simulated anywhere in the combined river-ocean computational domain, making it highly relevant for simulating combined floods. However, existing models based on this approach encounter numerical instability, especially in inland regions where topography variation is steep and highly uneven. Also, such combined models are highly limited for large scale applications. Therefore, this research focuses on developing a tightly coupled 2D finite volume river-ocean model called IROMS-C2D. The developed model intends to address the limitations of the previous models and provide a stable solution framework for the simulation of combined flooding resulting from the interaction of storm surges and river flows in coastal urban areas. Further, it enhances our understanding of flood risks in coastal areas, particularly in urban settings, and facilitates the formulation of effective measures for flood control and adaptation of coastal infrastructure.</p>
Can Erythropoietin Protect the Cerebral Blood Flow and Oxygenation During Simulated Dive?
ClinicalTrials.gov study NCT00265486. IPD Sharing: Not stated. Countries: 1. Publications: 7.
Female Pelvic Blood Flow Under Simulated Microgravity
ClinicalTrials.gov study NCT06633692. IPD Sharing: Not stated. Countries: 1. Publications: 6.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.