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146 results for “Numerical modelling”
Data from: Numerical modelling of bridges in 2D shallow water flow simulations
<p>This repository includes the experimental dataset colleted in the Hydraulics Laboratory of the University of Zaragoza in 2014.</p> <p>The experiments were carried out with different bridge configurations in a straight flume for both steady and transient flow regimes.</p> <p>The data were published originally in:</p> <p>Ratia, H., Murillo, J. and García-Navarro, P. (2014), Numerical modelling of bridges in 2D shallow water flow simulations. Int. J. Numer. Meth. Fluids 75, pp. 250-272. <a href="https://doi.org/10.1002/fld.3892">https://doi.org/10.1002/fld.3892</a></p> <p>This dataset has been made available online to the research community thanks to the support of the project PID2022-137334NB-I00 funded by MCIN/AEI/10.13039/<br>501100011033 and by “ERDF/EU”.</p>
Data from Deng et al. (2025) "Secular Change of Preservation of Lunar Sinuous Rilles: Constraints from Lava Flow Numerical Modeling"
<p>These are the updated supplemental materials of our article, adding more groups of simulation and a sensitivity analysis of two parameters, cell width and plane slope.</p>
Inputs to an idealized numerical model based on MITgcm and model results
<p>This dataset contains input files for model runs used in Leng et al. 2021, "Temporal Evolution of a Geostrophic Current under Sea Ice: Analytical and Numerical Solutions" (to be submitted to JPO). The model used in this study is an idealized channel configuration based on the Massachusetts Institute of Technology general circulation model (MITgcm). Original source code is the MITgcm_c66m. Some representative output is also included.</p> <p>code: contains code configuration.</p> <p>input: contains parameters, initial conditions, forcing, etc.</p> <p>output: contains model output.</p>
Dataset related to Balazs et al. The dynamics of forearc – back-arc basin subsidence: numerical models and observations from Mediterranean subduction zones
<p>Additional model data to publication by Balazs et al. The dynamics of forearc – back-arc basin subsidence: numerical models and observations from Mediterranean subduction zones</p>
Dynamics of the euphotic zone in the Black Sea: The synergy of data from profiling floats, machine learning and numerical modeling
<p>The datasets contain input data and data emulated by Neural networks (NN) used in the study 'Dynamics of the euphotic zone in the Black Sea: The synergy of data from profiling floats, machine learning and numerical modeling'</p> <p>- <strong>NN2018_CMEMS_ARGO.tar.gz</strong>: archive contains Matlab binary files consisting of NN-derived BGC variables (Chlorophyll-a, Oxygen and backscatter at 700nm) along ARGO float paths in 2018 with vertical resolution taken from CMEMS (13 depth levels in the depth range studied here); NN was applied either on CMEMS physics ('C') or on ARGO physics ('A'); additionally, CMEMS BGC model data (Chlorophyll-a and Oxygen) along these paths are included; (filenames follow the names of floats given in Table 1:<em> floatname</em>_2018_NNARGOCMEMS.mat)</p> <p>- <strong>NNalongARGO.tar.gz</strong>: archive contains Matlab binary files consisting of NN-derived BGC variables (Chlorophyll-a, Oxygen and backscatter at 700 nm) along ARGO float paths together with input ARGO data (time, latitude, longitude, salinity, temperature, sigma_T and BGC variables); all variables are mapped with 1m vertical resolution; depth range is [1m 150m] ; additionally, float ogs7 data include NO3, float hzg1 data does not contain Chlorophyll-a and backscatter at 700 nm; (filenames follow the names of floats given in Table 1:<em> floatname</em>_euph_1mRes_150mALLINCLNN.mat)</p> <p>- <strong>NNReconBlackSea.tar.gz</strong>: archive contains Matlab binary files consisting of basin wide NN derived BGC variables (Chlorophyll-a, Oxygen and backscatter at 700nm) for the years 2015-19 and 2010/11 (weekly mean data); additionally CMEMS data (time, latitude, longitude, salinity, temperature, sea surface height) are provided for the photic zone; (filenames are reconNNCMEMS_2015_2019.mat and reconNNCMEMS_2010_2011.mat, respectively)</p>
Numerical model-informed testbed for surface PM2.5 concentration over China and its estimates during 2013-2021
<p>This is an updated version of https://doi.org/10.5281/zenodo.11122294, the data provided in NetCDF format.</p> <p>In addition to the long-term PM2.5 dataset created from Li et al (2024), which can be used for health assessments and studying air pollution influences, here we also provide testbed data crucial for evaluating machine learning-based retrieval methods, especially in scenarios where no ground-truth data is available.<br>The testbed dataset includes all inputs and outputs following the physical model simulation, which naturally correlates with physical laws such as emissions, diffusion, advection, and deposition, representing typical conditions that any prediction method should meet. This data can be used to evaluate and compare methods using the same dataset, allowing for continuous improvement. Besides traditional cross-validation, the proposed testbed validation is highly recommended to examine a method’s predictive ability. We will continue updating the testbed data for other pollutants and with different resolutions and regions in future studies.</p>
Experimental raw data and numerical model
<p>Experimental raw data and numerical models for the work of Siyuan Qiang et, al., (2023), which focuses on the relationship between the saturation exponents and pore water distribution.</p> <p>Run the main.m file for numerical simulation<br>The simulation schemes could be easily changed by updating the SIG1.txt file.</p> <p>.mat file:<br>variable 'SIP':measured SIP frequency magnitude and phase (in degree) in experiments<br>variable 'Sw':measured water saturation in experiments</p>
Observation data and numerical model files for river plumes in Pallanza Bay of Lake Maggiore, Italy
<p>Data includes observation data collected from three deployments in Pallanza Bay of Lake Maggiore, Italy. Observation data includes velocity and thermistor chain data from deployments in 2012 and 2014, which captured a series of river plume discharges from the Toce River. Also included are numerical model source files and scenario files for simulations of river plumes into Pallanza Bay. </p>
Lithosphere removal (delamination) following continental collision: shape and complexity of observables predicted from 3D numerical models
<p>Upload contains most essential data files from the manuscript. The raw data amount to several TB and cannot be uploaded.</p> <p><strong>1 Model data</strong></p> <p><em>1.1 Models covered</em></p> <p>Relatively frequent output is available for the reference model (<em>R</em> in paper, see Table S2), prefixed <em>dp10</em>. Limited output is uploaded for models with geometric variations (<em>OSM -> dp19, TSM->dp26</em>).</p> <p><em>1.2 Types of data</em></p> <p>Most files are visualisation files (ending *.<em>vtr</em>). These are intended for loading into the visualisation software Paraview. Files starting with <em>comp_</em> contain the lithological composition field; other visualisation files hold the fields of selected physical parameters. Note that as of the design of the study, the maximum file size allowed for loading into Paraview is ca. 2 GB. This limits the number of fields available according to model size - larger models (geometrical variations) hold therefore less fields.</p> <p>Binary files with ending *.<em>prn</em> are saved model states, from which programs can be rerun.</p> <p>Time information mapping step numbers to years is provided in <em>times...txt</em>.</p> <p> </p> <p><strong>2 Software for reproduction</strong></p> <p>The numerical code is not in the public domain, and its use is restricted. A pre-compiled binary is provided and allows reproduction of the main/reference model. We present the input files; however, these cannot be changed freely (equivalent to software distribution), and changes will lead to termination of the program.</p> <p><em>2.1 Requirements, preparation and use</em></p> <p>The code is compiled on CentOS with gcc 4.8.2. There is no library dependency, apart from the intrinsic OpenMP and glibc (>= 2.17). The following requirements <em>must</em> be satisfied in order to run it:</p> <ul> <li>Linux OS (tested on CentOS and Fedora)</li> <li>> 160 GB shared memory</li> </ul> <p>The following provisions are recommended:</p> <ul> <li>16 cores</li> <li>high bandwidth storage</li> <li>storage space <em>O</em>(TB)</li> </ul> <p>To prepare, simply unzip the provided snapshot <em>clsd_reproduce_gcc_CentOS.zip</em> in an appropriate directory (cluster). The input file <em>init.t3c</em> sets the initial conditions; the input file <em>mode.t3c </em>controls solver and file output. <em>file.t3c</em> is the pointer to the last output step; if a model snapshot is available as binary dump (*.prn file), setting the pointer to its number allows restarting.</p> <p>For normal usage, create initial condition with executable <em>in3mg</em>, and subsequently run <em>i3mg</em>. This will create snapshots and *.vtr visualisation files.</p> <p> </p> <p>High-performance computing facilities, runtime (months), proper software environment, and training in use of the code, or in the use of the visualisation software, are not provided.</p> <p> </p>
Model output: Unraveling the mechanisms that cause cyclic channel-shoal dynamics of ebb-tidal deltas: a numerical modeling study
<p>Output from model runs for Lenstra et al., “Unraveling the mechanisms that cause cyclic channel-shoal dynamics of ebb-tidal deltas: a modeling study”. The dataset contains two types of model output, namely (1) the default model runs and (2) the sensitity runs with waves+tides, waves only, and tides only.</p> <p>The files covering the default model runs contain:</p> <p>ModeledDays - time vector for the depth series [days]</p> <p>XT - east-west location of grid points high resolution domains [m]</p> <p>YT - north-south location of grid points high resolution domains [m]</p> <p>depth - depth series for the high resolution domains [m]</p> <p>XSea - east-west location of grid points outer sea domain [m]</p> <p>YSea - north-south location of grid points outer sea domain [m]</p> <p>depthSea - depth series for the outer sea domain [m]</p> <p>XBasin - east-west location of grid points basin domain [m]</p> <p>YBasin - north-south location of grid points basin domain [m]</p> <p>depthBasin - depth series for the basin domain [m]</p> <p>ST_Days - time vector for the sediment transport series [days]</p> <p>ST_Inlet - tidally-averaged total sediment transport through the inlet (positive seaward) [10<sup>6</sup> m<sup>3</sup>/year]</p> <p>ST_Up - tidally-averaged total sediment transport through the cross-section at the updrift coast (positive eastward) [10<sup>6</sup> m<sup>3</sup>/year]</p> <p>ST_Down - tidally-averaged total sediment transport through the cross-section at the downdrif coast (positive eastward) [10<sup>6</sup> m<sup>3</sup>/year]</p> <p>The files covering the sensitivity runs contain for the high resolution domains:</p> <p>XT - east-west location of grid points [m]</p> <p>YT - north-south location of grid points [m]</p> <p>TA_STX - east-west component of the tidally-averaged total sediment transport [m<sup>3</sup>/s/m]</p> <p>TA_STY - north-south component of the tidally-averaged total sediment transport [m<sup>3</sup>/s/m]</p> <p>TA_U - east-west component of the tidally-averaged flow velocities [m/s]</p> <p>TA_V - north-south component of the tidally-averagedf low velocities [m/s]</p> <p>M2 - amplitude of the semi-diurnal tidal flow velocities (not for wave only runs) [m/s] </p>
Numerical model code, input files and output data for publication "Rapid mixing and exchange of deep-ocean waters in an abyssal boundary current"
<p>Contains numerical model data (code, input files, selected output, matlab diagnostic routines) to supplement publication ``Rapid mixing and exchange of deep-ocean waters in an abyssal boundary current'', by Naveiro Garabato and co-authors. All numerical model data, including any errors, is the responsibility of Sonya Legg. This data set will allow reproduction of simulations, and reproduction of diagnostics shown in plots in the above-referenced paper.</p>
Topography Response to Horizontal Slab Tearing and Oblique Continental Collision: 3D Thermomechanical Numerical Modelling
<p>This repository submission provides the 3D thermo-mechanical numerical modelling code I3ELVIS developed by the workgroup of Prof. Taras Gerya (ETH Zürich). The code is based on finite-difference and marker-in-cell methods (Gerya & Yuen, 2003, 2007; Gerya, 2019). The code solves the momentum, continuity, and energy equations on the fixed Eulerian grid and transports the physical properties by Lagrangian markers using the velocity field. The code also accounts for the major phase transitions in the Earth’s mantle and internal heat sources arising from adiabatic, radiogenic, and frictional heating. Partial melting and melt extraction processes are neglected for the sake of simplicity. A more detailed description of the code, including the governing equations and the adopted rheological model can be found in various published literature (Andrić-Tomašević et al., 2023; Boonma et al., 2023; Maiti et al., 2024). Interested users are recommended to contact Prof. Taras Gerya (taras.gerya@erdw.ethz.ch). </p> <p>The repository also provides input and output files to run and reproduce model results and manuscript figures of Maiti et al., 2024 (JGR Solid Earth). Paraview States for processing .vtr result files and visualizing the model output data. Matlab script to visualise the topography from .grd files. </p>
Refractory materials used in the numerical model of an aluminothermic reduction vessel
<p>This constitutes underlying data for a publication titled "Numerical Model of Aluminothermic Reduction Vessel Preheating and Charge Heating with Graphite Heating Rods" by S. Semenov, P. Namy, A. Kale, S. Tsebe. </p>
Refractory materials used in the numerical model of TBRC
<p>This constitutes underlying data for a publication titled "Numerical Model of a Top-Blown Rotary Converter Preheating and Charge Heating with an Oxy-Fuel Burner" by S. Semenov, P. Namy, A. Kale, S. Tsebe.</p>
Movies of the simulations performed in Baïsset et al. paper entitled « Weakening induced by phase nucleation in metamorphic rocks: insights from numerical models »
<p>Here you can find the movies of the evolution of the accumulated plastic strain of the simulations performed in the study. You will also find the table that summarizes the conditions of the different simulations (Table 1), as well as the colorbar (legend.png) corresponding to all the movies.</p>
Dataset of numerical model for enhancing stimulated Brillouin scattering in optical fibers
<p>Stimulated Brillouin scattering (SBS) is useful, among others for generating slow light, sensing and amplification. SBS was previously viewed as a penalty due to the limitation on optical power in high-powered photonic applications. However, considering the many possible applications using SBS, it is now of interest to enhance SBS in areas of Brillouin frequency shift together with Brillouin Gain. A numerical model, using a fully vectorial approach, by employing the finite element method, was developed to investigate methods for enhancing SBS in optical fiber. This paper describes the method related to the numerical model and discusses the analysis between the interactions of horizontal, shear and hybrid acoustic modes; and optical modes in optical fiber. Two case studies were used to demonstrate this. Based on this numerical model, we report the influence of core radius, clad radius and effective refractive index on the Brillouin frequency shift and gain. We observe the difference of Brillouin shift frequency between a normal silica optical fiber and that of a tapered fiber where nonlinearities are higher. Also observed, the different core radii used and their respective Brillouin shift. For future work, the COMSOL model can also be used for the following areas of research, including simulating "surface Brillouin shift" and also to provide in-sights to the Brillouin shift frequency v<sub>B</sub><span><span></span></span> of various structures of waveguides, e.g circular, and triangular, and also to examine specialty fibers, e.g. Thulium and Chalcogenide doped fibers, and their effects on Brillouin shift frequency.</p>
The interpretation of temperature and salinity variables in numerical ocean model output and the calculation of heat fluxes and heat content - ACCESS-CM2 data and code
<p>This dataset contains post-processed ACCESS-CM2 PI control CMIP6 climate model output and code used to produce Figs. 5, 6 and 9 in the published article:</p> <p>McDougall, T., J., Barker, P.M., Holmes, R.M., Pawlowicz, R., Griffies, S. and Durack, P. (2021): The interpretation of temperature and salinity variables in numerical ocean model output and the calculation of heat fluxes and heat content, <strong>Geoscientific Model Development</strong>, 14, 1–21, <a href="https://doi.org/10.5194/gmd-2020-426">https://doi.org/10.5194/gmd-2020-426</a></p> <p>The processed ACCESS-CM2 data is included as .mat files and is accompanied by Matlab processing routines (including code from the TEOS-10 Gibbs SeaWater Oceanographic Toolbox, https://www.teos-10.org/software.htm#1) to produce the figures. A more detailed description of the data are included in README.md. The code and data is also available under version control at <a href="https://github.com/rmholmes/ACCESS_CM2_SpecificHeat/tree/GMD_Published">https://github.com/rmholmes/ACCESS_CM2_SpecificHeat/tree/GMD_Published</a>.</p>
Data from: Voice efficiency for different voice qualities combining experimentally derived sound signals and numerical modeling of the vocal tract
<p>This dataset contains Stereo-Lithographic (STL) surface models of a human vocal tract, derived Finite-Element-Models, numerical results, and scripts for analyzing these results and (re-)running the computation.</p> <p> </p> <p><strong>In the main folder, this dataset contains:</strong></p> <p>1) Python files (*fig*.py) for the creation of figures and tables (*tab*.py)</p> <p>2) Python files (*.py) for analyzing Finite-Element (FE) calculations (x_resonances.py, x_libs.py, x_fem2excel.py)</p> <p>3) Python-files (*.py) for analyzing stl-data (x_analyzeSTL.py)</p> <p>4) Python files (*.py) for deriving Infinite-Impulse-Response (IIR) filter and their impulse responses (x_IIR.py)</p> <p>5) Excel files (*.xlsx) containing Volume-velocity-transfer-functions (Vlg.xlsx), Pressure-transfer-functions at the lips (Hlg.xlsx), and the glottis (Hgg.xlsx) based on FE, the sound spectra of audio signals (sound_spectra.xlsx), the polynomials describing the IIR (IIR_polynomial.xlsx) and their impulse responses (IIR_impulse_responses.xlsx), and glottal waveforms (glottal_waveform.xlsx) and spectra (glottal_spectra.xlsx)</p> <p>6) Several figures (*.pdf)</p> <p> </p> <p><strong>In folder „x_fenics/x_Subject-1“ (and sub-folders), this data set contains:</strong></p> <p>1) Surface models of the human vocal tract for different voice qualities (glottis.stl, wall.stl, lips.stl)</p> <p>2) Sub-volumes of the vocal tract cavities (*ET.stl, *HPl.stl, *HPu.stl, *OPf.stl, *OPr.stl, *SP.stl, *.VV.stl)</p> <p>3) Derived gmsh volume meshes (*.msh) (www.gmsh.info)</p> <p>3) Derived volume models applicable to FE-Solvers (*.h5, *.xdmf)</p> <p>4) Results of the FE-calculation (*pvtf*.txt, *vvtf*.txt, *pglottis*.txt)</p> <p>5) Formant frequencies computed by inverse filtering (*.for)</p> <p> </p> <p><strong>In folder „x_fenics/x_misc“ the data set contains:</strong></p> <p>1) Python-files (*.py) for (re-)running the calculations using the FE-Method</p>
Elucidating the Magma Plumbing System of Ol Doinyo Lengai (Natron Rift, Tanzania) Using Satellite Geodesy and Numerical Modeling; Model input and output files
<p>These are the model input and output associated with the manuscript "Elucidating the Magma Plumbing System of Ol Doinyo Lengai (Natron Rift, Tanzania) Using Satellite Geodesy and Numerical Modeling" in consideration for publication in the Journal of Volcanology and Geothermal Research.</p>
LiftWEC deliverable 3.6 - Part II: Dataset from numerical simulations of full-scale LiftWEC device using a high-fidelity RANS model
<p>This dataset contains the numerical simulation results for a full-scale LiftWEC device in regular and irregular wave conditions. The regular wave cases occur at static pitch and fixed rotational velocity. The parameters used for these cases are described in the corresponding *CaseParameters.csv file. These simulations were used to derive a first estimate of the maximum conversion efficiency of the rotor from wave power to shaft power. A detailed description of the employed numerical model and the case setup can be found in LiftWEC deliverable 3.6 Hydrodynamic Validation of Final Design, which was also uploaded to the LiftWEC community on zenodo. In accordance with the coordinate system definition used in the validation case, a relative phase angle of 270° degree corresponds to foil1 at 3 o'clock position while the wave crest passes over the rotor axis position. Rotation is clockwise, Rotor rotates in the direction of orbital wave particle velocities.</p> <p>The control reference case describes the first test case of a cyclorotor in irregular waves under active control of angular velocity and foil pitch. This case is also documented in deliverable D3.6. The case files can be used to recreate wave conditions and motion signal. The load-file can be used to validate the obtained tangential and radial forces on foil 1 as well as the total power output over time. More information on the control model can be found in the corresponding project deliverables D5.X.</p> <p>All loads given as values per unit span length (e.g. for forces [N/m]).</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.