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70 results for “mechanical simulation”
Data associated to the manuscript "Simulating the charging mechanism of a realistic nanoporous carbon-based supercapacitor using a fully polarizable model"
<p>Contains input files and data used to generate the figures of the article:</p> <p>Simulating the charging mechanism of a realistic nanoporous carbon-based supercapacitor using a fully polarizable model</p> <p>Camille Bacon, Patrice Simon, Mathieu Salanne and Alessandra Serva</p> <p><em>ChemRxiv, </em>10.26434/chemrxiv-2024-9577m, 2024</p> <p>The folder <em>input_files</em> contains typical MetalWalls input files used to perform the simulations.</p> <p>The folder <em>raw_data</em> contains the processed data used to plot all the figures of the paper.</p>
Effects of Grass Cover on the Overland Soil Erosion Mechanism under Simulated Rainfall
<p><span>Grass cover has a complex influence on overland soil erosion. This study quantified the impact of grass cover on overland soil erosion using a dimensionless water flow path index. It systematically analyzed the response mechanism among overland soil erosion, slope gradient, rainfall intensity, and hydrodynamic parameters, aiming to identify the optimal hydrodynamic parameters capable of characterizing overland soil erosion. A predictive model for soil erosion was constructed based on general dimensionless water flow intensity parameters, comprehensively evaluating the mechanism of soil erosion on grass-covered overland under simulated rainfall conditions. The results indicate that the model constructed using dimensionless parameters exhibits strong adaptability and can be effectively validated in other experiments.</span></p>
All-atom simulations elucidate the molecular mechanism underlying RNA-membrane interactions
<p>Topology files and frames extracted from the minimum of the free energy profile F(d_z) (or F(d_min) for single-stranded RNAs), within 2.5kBT. These files can be used to reproduce the hydrogen bond analyses in the manuscript.</p> <p>Scripts which were used to extract hydrogen bond information are available on <a href="https://github.com/salvatoredimarco/rna-membrane">https://github.com/salvatoredimarco/rna-membrane</a></p> <p><strong>Systems:</strong></p> <p>4xN: nucleosides</p> <p>4xN2: dinucleotides</p> <p>4xN3: trinucleotides</p> <p>4xN_OPC: nucleosides simulated with OPC water model. Energy threshold is here 1.0*kBT, because of weaker binding.</p> <p>1xGA, 1xGU, 1xGC, 1xCU</p> <p>1xGGC, 1xGCG</p> <p>1xquadruplex: G-quadruplex</p> <p>1xstrand: 19-mer RNA strand</p> <p>1xhairpin: 16-mer folded hairpin</p> <p>1x16mer_elong: 16-mer unfolded, restrained</p> <p>2x16mer_loose1/2: 16-mer unfolded, unrestrained</p>
A Tungsten Deep Neural-Network Potential for Simulating Mechanical Property Degradation Under Fusion Service Environment
<p>The DP-HYB and DP-SE2potential and the W training database.</p>
Reaction Mechanism of the PET Degrading Enzyme PETase Studied with DFT/MM Molecular Dynamics Simulations
<p>Raw simulations of the acylation step by PETase on a PET dimer model substrate, ran with CP2K 6.1 software at the PBE:AMBER level. Details can be found in the original manuscript (<a href="https://doi.org/10.1021/acscatal.1c03700">https://doi.org/10.1021/acscatal.1c03700</a>): Molecular topology in AMBER Parameter Topology format and Trajectories in CHARMM binary coordinate format DCD.</p> <p>RESIDUE LIST:<br> GLY57<br> TYR58<br> SER131<br> MET132<br> TRP156<br> ASP177<br> SER178<br> ILE179<br> ALA180<br> HID208<br> MOL262</p> <p>VMD selection:<br> (name CA C O HA2 HA3 and resname GLY and resid 57) or (name N CA CB H HA HB2 HB3 and resname TYR and resid 58) or (name CA C O OG CB HA HB2 HB3 HG and resname SER and resid 131) or (name N CA SD CE CB CG H HA HB2 HB3 HG2 HG3 HE1 HE2 HE3 and resname MET and resid 132) or (name CB CG CD1 CD2 CE2 CE3 NE1 CZ2 CZ3 CH2 HB2 HB3 HD1 HE1 HE3 HZ2 HZ3 HH2 and resname TRP and resid 156) or (name CG OD1 OD2 CB HB2 HB3 and resname ASP and resid 177) or (name C O and resname SER and resid 178) or (name N CA C O CG2 CD1 CB CG1 H HA HB HG12 HG13 HG21 HG22 HG23 HD11 HD12 HD13 and resname ILE and resid 179) or (name N CA H HA and resname ALA and resid 180) or (name CB CG CD2 ND1 CE1 NE2 HB2 HB3 HD1 HD2 HE1 and resname HID and resid 208) or (name C1 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C2 C20 C3 C4 C5 C6 C7 C8 C9 H1 H10 H11 H12 H13 H14 H15 H16 H17 H2 H3 H4 H5 H6 H7 H8 H9 O1 O2 O3 O4 O5 O6 O7 O8 O9 and resname MOL and resid 262)</p> <p>PYMOL selection:<br> (name CA+C+O+HA2+HA3 & resn GLY & resi 57) | (name N+CA+CB+H+HA+HB2+HB3 & resn TYR & resi 58) | (name CA+C+O+OG+CB+HA+HB2+HB3+HG & resn SER & resi 131) | (name N+CA+SD+CE+CB+CG+H+HA+HB2+HB3+HG2+HG3+HE1+HE2+HE3 & resn MET & resi 132) | (name CB+CG+CD1+CD2+CE2+CE3+NE1+CZ2+CZ3+CH2+HB2+HB3+HD1+HE1+HE3+HZ2+HZ3+HH2 & resn TRP & resi 156) | (name CG+OD1+OD2+CB+HB2+HB3 & resn ASP & resi 177) | (name C+O & resn SER & resi 178) | (name N+CA+C+O+CG2+CD1+CB+CG1+H+HA+HB+HG12+HG13+HG21+HG22+HG23+HD11+HD12+HD13 & resn ILE & resi 179) | (name N+CA+H+HA & resn ALA & resi 180) | (name CB+CG+CD2+ND1+CE1+NE2+HB2+HB3+HD1+HD2+HE1 & resn HID & resi 208) | (name C1+C10+C11+C12+C13+C14+C15+C16+C17+C18+C19+C2+C20+C3+C4+C5+C6+C7+C8+C9+H1+H10+H11+H12+H13+H14+H15+H16+H17+H2+H3+H4+H5+H6+H7+H8+H9+O1+O2+O3+O4+O5+O6+O7+O8+O9 & resn MOL & resi 262)</p>
A novel technique to simulate and characterize a yarn's mechanical behavior based on a geometrical fiber model extracted from micro-CT imaging: geometry and simulation data
<p>This dataset contains the original µCT scan data, the scripts and intermediate results for the generation of the geometrical fiber model, as well as the structural simulation files and their experimental validation data described in the paper <a href="https://journals.sagepub.com/doi/10.1177/00405175221137009">"A novel technique to simulate and characterize a yarn's mechanical behavior based on a geometrical fiber model extracted from micro-CT imaging"</a>, published in Textile Research Journal.</p>
Molecular Dynamics (MD) Simulation Data for Dynamics Underlie the Drug Recognition Mechanism by the Efflux Transporter EmrE
<p>MD simulations on the proton bound (PDB 8UWU), deprotonated on E14A (PDB 8UWU), TPP Bound (PDB 8UWU) on our NMR derived structures.</p> <p> </p> <p>MD simulations on the proton bound (7MH6) and deprotonated on E14A (7MH6) on X-ray structures. </p> <p> </p> <p>Total raw simulation data would be too large for uploading to repositories. To reduce size of file, starting structure and tpr files are uploaded. Final structure at 2.5 μs are also uploaded. </p>
Molecular Simulation Elaborating the Structural Mechanism of Spiri forming nitrilase from Bacillus safensis
<p><span>Nitrilases are indispensable in the biocatalytic hydrolysis of nitrile compounds, which presents promising applications in industrial biocatalysis and environmental remediation. In this study, we conduct an extensive investigation into the structural and functional properties of <em>Bacillus safensis </em>nitrilase (<em>BsNIT</em>), highlighting its assembly, substrate binding mechanisms, stability, evolutionary relationships, and active site conservation. Using a combination of molecular modeling and extensive molecular dynamics simulations, we unveil the intricate architecture of <em>BsNIT</em>, which exhibits a left-handed spiri-forming structure stabilized by interchain interactions and salt-bridge formations. The substrate-binding pocket, surrounded by aromatic residues, displays multifaceted accessibility through distinct channels. Our simulations reveal substrate-specific catalytic orientations, with glutaronitrile and 4-cyanobutanoic acid displaying stable binding, while benzonitrile exhibits a propensity for rapid product diffusion. Analysis of active site conservation emphasizes the functional significance of preserved catalytic residues, providing insights into <em>BsNIT</em>'s catalytic efficiency. This comprehensive study offers a thorough understanding of <em>BsNIT</em>'s structure-function relationships, paving the way for future advancements in enzyme engineering and bioremediation strategies targeting nitrile pollutants.</span></p>
Studying gastrulation by invagination: the bending of a cell sheet by mechanical cell properties using 3D deformable cell based simulations
<p>This dataset contains the scripts and end results of invagination simulation experiments.<br>The data are the results of a 3D cell based model that was used to investigate invagination: the bending of a cell sheet, and uses cell-cell adhesion, apical constriction, cell volume conservation, collision detection and handeling.</p>
Simulation data of Schmidt et al., An Electro-Chemo-Mechanic Model Resolving Delamination between Components in Complex Microstructures of Solid-State Batteries, 2024, DOI: https://doi.org/10.1149/1945-7111/ad76dc
<p>This data set includes the simulation results of the relevant simulations published in the paper: "Schmidt et al., An Electro-Chemo-Mechanic Model Resolving Delamination between Components in Complex Microstructures of Solid-State Batteries, 2024, DOI: https://doi.org/10.1149/1945-7111/ad76dc".</p> <p>Please refer to the paper for the details of the model as well as the parameterization of the model for the respective simulations.</p> <p>The data is provided in a zip archive. After extracting you find a short README.txt with further hints on the structure and available data.</p>
Hydro-mechanical simulation of CO2 Injection into a faulted aquifer
<h2>Summary</h2> <p>This dataset contains the results of geomechanical simulations conducted on a faulted aquifer under conditions of CO2 injection. The primary focus of the simulations is the pressure evolution within the rock matrix and along the fault, as well as the associated changes in the mechanical state, including rock deformation and fault slip. Additionally, the simulations explore the sensitivity of fault stability under varying orientations of far-field stress.</p> <p>The dataset includes raw data in VTK format, as well as an illustrative Jupyter notebook that provides a comprehensive explanation of the problem's geometry, boundary and initial conditions, and an interpretation of the observed physical phenomena. The Jupyter notebook is designed to be run both online and locally.</p> <p>These simulations were performed using an open-source FEM-based geomechanical simulator. Detailed instructions for running the notebook, along with a link to the geomechanical simulator, are provided in the description below.</p> <h2>Contributions</h2> <ul> <li>Emil Gallyamov did contribute to the production of the dataset and its visualisation.</li> <li>Ismaël Gomes did contribute to the development of the visualisation interface through Jupyter Notebooks and Streamlit.</li> <li>Guillaume Anciaux did contribute to the development of the visualisation interface and data curation.</li> </ul> <h2>Data collection: period and details</h2> <ul> <li>From 20 April, 2024 to 30 August, 2024, the .pvt and .npy files were generated, curated, and visualisation routines were developed.</li> </ul> <h2>Funding sources</h2> <ul> <li>ENAC Interdisciplinary Cluster Grant project <a href="https://www.epfl.ch/schools/enac/osgeocgs/">OSGEOCGS</a>.</li> </ul> <h2>Notebook demonstration</h2> <h3>Online</h3> <p>An interactive notebook showcasing visualisations of the dataset is available on <a href="https://renkulab.io/projects/phamba/geology-data-visualization/sessions/new?autostart=1">RenkuLab</a>.</p> <h3>Running locally</h3> <p>Alternatively, you can launch the notebook on your computer. Download the dataset, install dependencies, and launch <em>Jupyter notebook</em>:</p> <p><code>pip install -r requirements_freeze.txt</code></p> <p><code>jupyter notebook</code></p> <p>Then, open <code>notebooks/DataVisualisation.ipynb</code>.</p> <h2>Reproducing the dataset</h2> <p>To recreate the results found in this dataset, install the <a href="https://archive.softwareheritage.org/browse/origin/directory/?origin_url=https://gitlab.com/emil.gallyamov/akantu-geomechanical-solver">solver</a> and go through the example at <a href="https://archive.softwareheritage.org/browse/origin/directory/?origin_url=https://gitlab.com/emil.gallyamov/akantu-geomechanical-solver&path=examples/injection_fault"><code>examples/injection_fault</code></a>.</p> <h2>Data structure and information</h2> <p>The repository has the following structure:</p> <ul> <li>data: simulation results <ul> <li>reservoir_vs_time: bulk, solid, cohesive and fault fields for the duration of the simulation <ul> <li>paraview: vtk files</li> <li>*.npy: numpy arrays to store numerical data at specified locations</li> </ul> </li> <li>fault_vs_angle: only cohesive fields for 180 degrees of rotation <ul> <li>cohesive_0*.vtu: vtu files containing absolute values of the fields</li> <li>cohesive_init_0*.vtu: all the fields in these files are frozen to the initial (at rest) system state</li> <li>cohesive_parsed.pvd</li> <li>cohesive_init_parsed.pvd: postprocessing of Ismaël is substracting one set of data from the other one and plots the difference - increase of slip</li> </ul> </li> </ul> </li> <li>notebooks: data visualisation through Jupyter Notebooks <ul> <li>images: images used for illustration in notebooks (e.g. schema of stress rotation, model geometry, etc.) <ul> <li>stress_rotation: contains images with scheme of rotation for angles from 0 to 180</li> </ul> </li> <li>DataVisualisation.ipynb: main notebook with visualized DataVisualisation</li> </ul> </li> <li>library: all the scripts needed to visualize DataVisualisation</li> </ul>
Data set for bionic simulation of double clap-and-fling wing mechanism with SPH FSI method
<p>Data set containing the rigid-body based flapping wing models coupled with smoothed particle hydrodynamics (SPH), by means of <a href="https://github.com/DualSPHysics/DualSPHysics/wiki/9.-New-in-DualSPHysics#new-in-dualsphysics-v50">DualSPHysics version 5.0 </a>and <a href="https://projectchrono.org/download/">Project Chrono</a>.</p><p>These models are part of the paper:</p><blockquote><p>Yanwei Zhang, Zhonglai Wang, Saullo G. P. Castro. Bionic simulation of double clap-and-fling wing mechanism with SPH FSI method. EngXriv Preprint, 2022. <a href="https://doi.org/10.31224/2652">DOI: 10.31224/2652</a></p></blockquote><p>File "simulations.zip" contains the simulation files.</p><p>File "DualSPHysics_v5.0.zip" contains the compiled DualSPHysics software.</p><p>Procedure to run the simulations on a Windows machine:</p><ul><li>Working directory: .\simulations\flappingwing\case01</li><li>Step 1: Obtain rigid bodies by modeling of SOLIDWORKS and Macro command of FreeCAD (e.g. external_wing111.stl)</li><li>Step 2: Run ".bat" file (e.g. flapping01.bat) to start simulation and force acquisition</li><li>Step 3: Revise ".bat" and ".xml" (e.g. flapping01.bat and flapping01_Def.xml)to adapt to the next case. If required, change the model and Macro command of step 1. Common modification items:</li></ul><p><geometry>-<definition> <floatings>- <floating><angularvel> <floatings>- <floating><property> <properties>-<propertyfile> <initials> <execution>-<special>-<chrono> <execution>-<special>-<inout> <parameters> </p><ul><li>Step 4: Run "Filter.m" to handle force data by filters. Step 5: Compare forces of all cases. PS: Other files are revised function files derived from DualSPHysics 5.0.</li></ul><p> </p><p>Abstract: Three-dimensional numerical simulations of flexible flapping wings based<br>on the fluid-structure interaction in biological and bioinspired flow have become a<br>vibrant and challenging research topic. The present paper focuses on a parametric<br>study of the aerodynamic performance of a bionic flexible clapping wing. The proposed<br>model deforms the wing in spanwise and chordwise directions based on the six rigid<br>bodies connected along the wing veins using ball links and springs. Unsteady effects of<br>flapping wing micro air vehicles with a double clap-fling configuration are investigated<br>using an air-solid interaction model based on smoothed particle hydrodynamics and<br>rigid multi-body dynamics. A validation experiment determined the convergence<br>conditions and computational model accuracy. The proposed numerical model is<br>evaluated in terms of flexible variation law and aerodynamic performance. The results<br>indicate that the flapping frequency, angle of attack, and wind velocity significantly<br>influence the lift. Furthermore, increasing the frequency will monotonically expand<br>the maximum and time-averaged lift curve values. When the angle of attack is less<br>than 30 ◦, the influence on the time-averaged and maximum lift is proportional to the<br>angle of attack. When the angle of attack is larger than 45 ◦, a stall-like condition<br>is detected. To broaden the applicability of the present findings, a dimensionless<br>parameter, reduced frequency, is defined, and its influence on the maximum and time-<br>averaged lift is investigated. This parametric study shows that as the reduced frequency<br>increases, the maximum and time-averaged lift increases and then decreases. The<br>present study could reach a modeling framework that better explains the clapping<br>wing aerodynamics.<br> </p>
Molecular mechanism underlying SNARE-mediated membrane fusion enlightened by all-atom molecular dynamics simulations
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Scheduling Mechanisms to Control Spread of Covid-19 (Simulation Results)
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Tolerance of novel toxins through generalized mechanisms: simulating gradual host shifts of butterflies
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A Publicly Available Virtual Cohort of Four-chamber Heart Meshes for Cardiac Electro-mechanics Simulations
<p><strong>Motivation: </strong> Computational models of the heart are increasingly being used in the development of devices, patient diagnosis and therapy guidance. While software techniques have been developed for simulating single hearts, there remain significant challenges in simulating cohorts of virtual hearts from multiple patients.</p> <p><strong>Dataset Description: </strong>We present the first database of four-chamber heart models suitable for electro-mechanical simulations. Our database consists of twenty-four four-chamber heart models generated from end-diastolic CT acquired from heart failure patients recruited for cardiac resynchronization therapy upgrade. We also provide a higher resolution version for each of the twenty-four meshes.</p> <p>We segmented end-diastolic CT. The segmentation was then upsampled and smoothed. The final multi-label segmentation was used to generate a tetrahedral mesh. The resulting meshes had an average edge length of 1.1mm. The elements of all the twenty-four meshes are labelled as follows: 1) Left ventricle myocardium 2) Right ventricle myocardium 3) Left atrium myocardium 4) Right atrium myocardium 5) Aorta wall 6) Pulmonary artery wall 7) Left atrium appendage ring 8) Left superior pulmonary vein ring 9) Left inferior pulmonary vein ring 10) Right inferior pulmonary vein ring 11) Right superior pulmonary vein ring 12) Superior vena cava ring 13) Inferior vena cava ring 14) Mitral valve plane 15) Tricuspid valve plane 16) Aortic valve plane 17) Pulmonary valve plane 18) Left atrial appendage valve plane 19) Left superior pulmonary vein valve plane 20) Left inferior pulmonary vein valve plane 21) Right inferior pulmonary vein valve plane 22) Right superior pulmonary vein valve plane 23) Superior vena cava valve plane 24) Inferior vena cava valve plane.</p> <p>Ventricular fibres were generated using a rule-based method, with a fibre orientation varying transmurally from endocardium to epicardium from 80˚ to -60˚, respectively. We defined a system of universal ventricular coordinates on the meshes, see Figure 1B: an apico-basal coordinate varying continuously from 0 at the apex to 1 at the base; a transmural coordinate varying continuously from 0 at the endocardium to 1 at the epicardium; a rotational coordinate varying continuously from – π at the left ventricular free wall, 0 at the septum and then back to + π at the left ventricular free wall; intra-ventricular coordinate defined at -1 at the left ventricle and +1 at the right ventricle. This coordinate system was assigned to the ventricles in the four-chamber meshes and all the other labels were assigned with -100. </p> <p>We also refined each mesh from 1.1mm resolution down to 0.39mm resolution. Each refined mesh has tags defined on its elements (same numbering as described above) and ventricular fibres.</p> <p><strong>Database format: </strong>We provide a zipped folder for each mesh. Each folder contains the coarse and the finer versions of the same mesh. All twenty-four 1mm-meshes are supplied in case format, readable with paraview. All binary files containing the meshes data (ens and geo formats) are provided within the zipped folder. Points coordinates are given in mm. Element tags are assigned to the elements of the mesh as well as fibres and sheet directions. Fibres and sheet directions are assigned to the ventricles according to a rule-based method, while non-ventricular elements are assigned with default vectors [1; 0; 0] and [0; 1; 0]. UVCs are assigned to the nodes of the meshes. We also provide the location of the cardiac resynchronisation therapy right-ventricular electrode used to initiate ventricular excitation. This is given as a label on the nodes called electrode endo rv, which is 1 at the stimulated nodes. Finer meshes are provided in vtk format, also readable in paraview. For these meshes, we provide element tags, fibres and sheet directions on the ventricles, all in the same file.</p>
Pore-Opening and Ion-Conduction Mechanism in Channelrhodopsins C1C2, ChR2, and iChloC by Computational Electrophysiology and Constant-pH Simulations
<p>The simulation run input files, comprising the starting configuration and all necessary parameters for performing the<br>MD simulations.</p>
Data for A weak coupling mechanism for the early steps of the recovery stroke of myosin VI: a free energy simulation and string method analysis
<p>Representative frames, topology for MD simulation with NAMD and GROMACS, data and notebook to reproduce analyses in the paper "A weak coupling mechanism for the early steps of the recovery stroke of myosin VI: a free energy simulation and string method analysis" (Blanc, Houdusse, Cecchini, PLOS Computational Biology 2024).</p>
Collected data from finite element simulations to calculate the mechanical properties of innovative CLT using ABAQUS
<p>A dataset is collected from finite element computation using ABAQUS for different configurations of innovative CLT. In the dataset, we have the inputs (w, t1, t2, t3, t4, s, E_L, G_LZ, G_CZ) describing the microstructure of innovative CLT, and the elastic properties (Membrane stiffness: A11, A22, In-plane Poisson effect stiffness: A12, In-plane shear stiffness: A33, Bending stiffness: D11, D22, Out of plane Poisson effect stiffness: D12, Torsional stiffness: D33, out of plane Bending Gradient shear compliances: h11, h12, h16, h22, h26, h33, h34, h35, h44, h45, h55, h66) deriving from the FE computations. We have also the results of closed-form solutions that predicts these elastic properties of innovative CLT. </p>
Research data supporting: "Exploring RNA destabilization mechanisms in biomolecular condensates through atomistic Simulations"
<p>This repository contains the data to reproduce the results shown in the bioRxiv preprint "Exploring RNA Destabilization Mechanisms in Biomolecular Condensates through Atomistic Simulations" (DOI: 10.1101/2024.09.13.612876).</p>
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.