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1,028 results for “modelling & simulation”
Simulations scripts for: Strain localization patterns and thrust propagation in 3-D discrete element method (DEM) models of accretionary wedges
<p>High-resolution three-dimensional discrete element method (DEM) simulations of sandbox-scale models of accretionary wedges performed in this study suggest thrusts follow a variety of propagation processes and orientations in the wedges depending on a number of factors including the stage of development of the wedge (precritical vs. critical), basal friction, and type of thrust (forward vs. backward-vergent). In terms of propagation processes, two clear mechanisms are identified. The first involves propagation from the decollement to the wedge top, similar to the standard model of thrust propagation seen in many kinematic models, and in the second, thrusts grow downward from an initial nucleation point just below the top surface of the wedge as well as upward from the decollement joining in the middle. In terms of orientation, forward-vergent thrusts initially form at Roscoe or Arthur orientations, and over shortening, form at Coulomb orientations. To arrive at these results, a wide array of continuum parameters and fields were extracted from the granular assembly of the DEM simulations, including stress, strain, strain rate, kinetic energy, Mohr-Coulomb parameters, and proximity to yielding using the Drucker-Prager criterion to visualize thrust nucleation and propagation. Lastly, the advantages and disadvantages of these continuum proxies for discerning failure in the granular assembly are considered, and the spatial and temporal relationship between proximity to yielding and strain localization (both pre-peak and persistent shear banding) in the granular model of an accretionary wedge is explored.</p>
Reference LES Simulations for Ocean Models Validation
<p>All Large-Eddy Simulations (LES) computations are conducted with the open-source PALM solver (<a href="https://palm.muk.uni-hannover.de/trac">https://palm.muk.uni-hannover.de/trac</a>) and version 5.0 of the Los Alamos National Laboratory (<a href="https://github.com/lanl/palm_lanl">https://github.com/lanl/palm_lanl</a>) branch. The simulations use a cubical domain with length L=128m, three grid resolutions (N=128<sup>3</sup>, 256<sup>3</sup>, or 512<sup>3</sup>), and mimic four single-column canonical oceanic regimes in which the surface flux and background stratification profiles are imposed. These simulations are named cooling (c), stratification (s), evaporation (e), and mixed (m):</p> <ul> <li><strong>cooling cases</strong> test different surface heat fluxes: Q<sub>h</sub>=-1.185x10<sup>-5</sup> (c<sub>01</sub>), -2.371x10<sup>-5</sup> (c<sub>02</sub>), -4.742x10<sup>-5</sup> (c<sub>04</sub>), and -18.966x10<sup>-5</sup> (c<sub>16</sub>) [K.m/s]. The background stratification is set through a vertical temperature gradient (T<sub>z</sub>) equal to 0.1 [K/m], and both the salinity surface flux (Q<sub>s</sub>) and the background stratification due to vertical salinity gradient (S<sub>z</sub>) are equal to zero.</li> <li><strong>stratification cases</strong> are similar to the cooling c<sub>02</sub> case, but Q<sub>h</sub>=-2.371x10<sup>-5</sup> [K.m/s], and T<sub>z</sub>=0.01 (s<sub>01</sub>), 0.1 (s<sub>10</sub>), or 0.2 (s<sub>20</sub>) [K/m].</li> <li><strong>evaporation cases</strong> define Q<sub>h</sub>=T<sub>z</sub>=0, S<sub>z</sub>=-0.025 [PSU/m], and Q<sub>s</sub>=3.115x10<sup>-6</sup> (e<sub>01</sub>) or 1.225x10<sup>-5</sup> (e<sub>04</sub>) [PSU/(m<sup>2</sup>.s)].</li> <li><strong>mixed cases</strong> combine heat and salinity surface fluxes, and the background stratification is imposed by both vertical temperature and salinity gradients. There are four mixed cases in which Q<sub>h</sub>=-1.185x10<sup>-6</sup> [K.m/s], T<sub>z</sub>=0.05 [K/m], S<sub>z</sub>=-0.025 [PSU/m], and Q<sub>s</sub> is either 0 (m<sub>01</sub>), 3.115x10<sup>-6</sup> (m<sub>02</sub>), 9.10x10<sup>-6</sup> (m<sub>03</sub>), or 4.55x10<sup>-5</sup> (m<sub>04</sub>) [PSU/(m<sup>2</sup>.s)].</li> </ul> <p>The simulations run 96h at a latitude of 43.29<sup>o</sup>, and utilize a linear equation of state where the reference temperature and salinity are 293.15 K and 35 PSU. The subgrid turbulent scales are modeled through the Moeng and Wyngaard SGS closure (see PALM documentation), and the flow is perturbed during the first 150 s of simulation with normally distributed fluctuations with a maximum amplitude of 10<sup>-4</sup>. The dataset provides 1D and 3D statistics. The 1D profiles are time-averaged during 3600 s and written every 3600 s. The 3D fields are not time-averaged and written every 3600 s, except those obtained with the grid having 512<sup>3</sup> points. In this case, the data are written every 21,600 s. The 3D fields are not in the tar file but can be requested by emailing the authors (fmsoarespereira@lanl.gov). The PALM input decks of the simulations are included in the shared files.<br> <br> <strong>Note:</strong> the PALM version used in this work requires the 1D salinity flux profiles to be normalized by the product of N<sub>x </sub>and<sub> </sub>N<sub>y </sub>(number of grid points in x and y)</p> <p> </p> <p><strong>LA-UR-22-32996</strong></p>
Lidar and model data for manuscript submitted to GRL "First Simultaneous Observation of Secondary and Tertiary Gravity Waves by Lidar and Investigation with HIAMCM simulations"
<p>This dataset contains the lidar and model data used in the manuscript submitted GRL titlled '<strong>First Simultaneous Observation of Secondary and Tertiary Gravity Waves by Lidar and Investigation with HIAMCM simulations'</strong></p>
Last glacial cycle simulations forced by PMIP3 climate with a matrix and index method using a 3D thermodynamical ice-sheet model IMAU-ICE
<p>IMAU-ICE 2.0 model output of the ice evolution during the last glacial cycle at a 10 ka temporal resolution, as described in Scherrenberg at al., 2023.</p>
Dataset for "Optimization and Evaluation of Stochastic Unified Convection Using Single-Column Model Simulations at Multiple Observation Sites"
<p>SCAM5 and LES outputs from "Optimization and Evaluation of Stochastic Unified Convection Using Single-Column Model Simulations at Multiple Observation Sites". Includes simulation outputs of stochastic UNICON and original UNICON. The LES intercomparison data of DYCOMSRF01 is available at https://gcss-dime.giss.nasa.gov/pub/DYCOMS-II/GCSS7-RF01/gcss7.nc, and the data of CGILS is available at http://www.atmos.washington.edu/~bloss/CGILS2data.tar.</p>
Studying the wide range of relative humidity in cirrus clouds with large-ensemble parcel model simulations
<p>The model codes, data, and plot scripts used in the paper, "Studying the wide range of relative humidity in cirrus clouds with large-ensemble parcel model simulations".</p> <ul> <li>parcel model code.zip contains model code.</li> <li>Results.zip contains output data of each experiment in this study.</li> <li>Figs and scripts.zip are the NCL scripts used for figures.</li> </ul>
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>
Video simulations for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks"
<p>Videos of the comparison between numerical and deep learning simulations for test datasets 1, 2, and 3 for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks".</p>
Simulation models for cancer immunotherapy and chemotherapy trials
<p>This repository contains code and data related to the following manuscript:</p> <p><em>In silico</em> cancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome Jeroen H.A. Creemers, Kit C.B. Roes, Niven Mehra, Carl G. Figdor, I. Jolanda M. de Vries, Johannes Textor medRxiv 2021.09.09.21263319; doi: <a href="https://doi.org/10.1101/2021.09.09.21263319">https://doi.org/10.1101/2021.09.09.21263319</a></p> <p>The manuscript (the revised version of which can be found in this repository) describes three simulation models for cancer patient survival data with different immunotherapy treatments. This repository contains the source code of the simulation models, which are written in C++, as well as an R wrappers using the Rcpp package. These can be found in the directory models/TumorImmuneModels/.</p> <p>There is also a web-based implementation, written in JavaScript, available at <a href="https://computational-immunology.org/models/immunotherapy-trials/">https://computational-immunology.org/models/immunotherapy-trials/</a>.</p> <p>Finally, see the folder "figures" for the code used to perform the analyses and generate the figures shown in the manuscript.</p>
LiftWEC deliverable 3.5 - Dataset from extreme wave impact simulations on LiftWEC rotor using a high-fidelity RANS model
<p>This dataset contains numerical simulation results obtained from extreme wave impact in the LiftWEC rotor obtained using the high-fidelity RANS model employed in the LiftWEC project. An elaborate description of the numerical simulation setup as well as the discussion of the obtained results is available in LiftWEC deliverable D3.5 Extreme Load Analysis Report, also available in the LiftWEC zenodo-community or via the project website liftwec.com.</p> <p>The RANS-based investigation looked into maximum forces obtained using different extreme wave generation mechanisms as well as for different configurations of rotor and foil angle and rotor submergence. The dataset features images of different flow field variables throughout the simulation and time series of obtained forces in horizontal and vertical direction. In combination with the report, the datasets should allow to replicate all investigated design wave cases.</p>
Modelling and simulating age-dependent pedestrian behaviour with an autonomous vehicle
<p>Video "1_SimulationWithoutAgel" shows the simulation before the modifications of the model and the implementation.</p> <p>Video "2_SimulationWithAge" shows the simulation after the modifications of the model and the implementation depending on the age data.</p> <p> </p> <p>Videos of the article: "Modelling and simulating age-dependent pedestrian behaviour with an autonomous vehicle"</p> <p>Abstract: In shared spaces, autonomous vehicles (AVs) will have to move efficiently and safely, without normal road signage, and with other users such as pedestrians, cyclists and drivers. To achieve this, AVs need to anticipate the behaviours of other road users in order to adapt their navigation accordingly. This paper focuses on age-related pedestrian behaviours with an autonomous vehicle. Looking at age as one of the main factors determining behaviour, a literature review is conducted. The results are used to integrate age-dependent pedestrian behaviours into a model for simulating more realistic pedestrian behaviours in shared spaces with an AV.</p>
Bicelle size and lipid/surfactant ratio screening - Gwalp tail anchor dimer simulation - 80 Lipids - q0.38 - PBS neutralized - CHARMM36m - 310K - OPC water model
<p>Bicelle size and lipid to surfactant ratio screening to investigate the influence on spin relaxation data with monomers of a given peptide.</p>
Bicelle size and lipid/surfactant ratio screening - Gwalp tail anchor dimer simulation - 60 Lipids - q0.38 - PBS neutralized - CHARMM36m - 310K - OPC water model
<p>Bicelle size and lipid to surfactant ratio screening to investigate the influence on spin relaxation data with monomers of a given peptide.</p>
Micelle size screening - Gwalp tail anchor dimer simulation - 45 SDS - Na neutralized - CHARMM36m - 310K - OPC water model
<p>Micelle size screening by varying the amount of SDS to investigate the influence on spin relaxation data with dimers of a given peptide.</p>
Bicelle size and lipid/surfactant ratio screening - Magaining 2 tail anchor monomer simulation - 80 Lipids - q0.38 - PBS neutralized - CHARMM36m - 310K - OPC water model
<p>Bicelle size and lipid to surfactant ratio screening to investigate the influence on spin relaxation data with monomers of a given peptide.</p>
Bicelle size and lipid/surfactant ratio screening - Magaining 2 tail anchor monomer simulation - 60 Lipids - q0.38 - PBS neutralized - CHARMM36m - 310K - OPC water model
<p>Bicelle size and lipid to surfactant ratio screening to investigate the influence on spin relaxation data with monomers of a given peptide.</p>
Bicelle size and lipid/surfactant ratio screening - Magaining 2 tail anchor monomer simulation - 120 Lipids - q0.5 - PBS neutralized - CHARMM36m - 310K - TIP3P water model
<p>Bicelle size and lipid to surfactant ratio screening to investigate the influence on spin relaxation data with monomers of a given peptide. Special iteration to investigate the influence of the water model</p>
Bicelle size and lipid/surfactant ratio screening - Gwalp tail anchor monomer simulation - 80 Lipids - q0.38 - PBS neutralized - CHARMM36m - 310K - OPC water model
<p>Bicelle size and lipid to surfactant ratio screening to investigate the influence on spin relaxation data with monomers of a given peptide.</p>
Bicelle size and lipid/surfactant ratio screening - Gwalp tail anchor monomer simulation - 120 Lipids - q0.38 - PBS neutralized - CHARMM36m - 310K - OPC water model
<p>Bicelle size and lipid to surfactant ratio screening to investigate the influence on spin relaxation data with monomers of a given peptide.</p>
Bicelle size and lipid/surfactant ratio screening - Gwalp tail anchor monomer simulation - 60 Lipids - q0.42 - PBS neutralized - CHARMM36m - 310K - OPC water model
<p>Bicelle size and lipid to surfactant ratio screening to investigate the influence on spin relaxation data with monomers of a given peptide. Reduced saved frequency could not be generated and no-water was directly generated by hand and added here.</p>
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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.
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.