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87 results for “energy simulation”
Future Projection of Solar Energy Over China Based on Multi-Regional Climate Model Simulations
<p>Data for article "Future Projection of Solar Energy Over China Based on Multi-Regional Climate Model Simulations"</p>
Source molecular simulation data for calculating energy and friction profiles and permeability coefficients through model lipid membranes
<p>Energy files from GROMACS molecular dynamics simulations with enhanced free energy sampling contain time-dependent evolution of the free energy profiles and friction profiles (and other energies and simulation properties) that were used for calculating permeability coefficients in the publication https://www.biorxiv.org/content/10.1101/2021.07.16.452599v1</p> <p>Simulation system contains a lipid POPC or DPPC bilayer with a varying amount of cholesterol (specified as mol% in the file name). Hydrophobic level of the permeating particle is specified as "level-I", "level-II" etc. When unspecified in the file name, the particle is hydrophobic level "III". Lipids D-C14-PC denote PC lipids with both tails monounsaturated of length 14 carbon atoms. DOPC is equivalent to D-C18-PC. (Detailed description in the publication)</p> <p>Adaptive Weighted Histogram (AWH) method was used to sample the free energy profile of translocating small molecule through the lipid bilayer.</p> <p>GROMACS tool `gmx awh` reads the files and provides the described profiles.</p> <p>Files were generated by GROMACS `mdrun` simulation engine version 2019.3.</p> <p> </p> <p>Coarse-grained MARTINI 3.0 model was used for modeling the biomolecular interactions.</p> <p>Scripts to perform the simulations and the files with initial configurations and simulation settings are stored in a public GitHub repository depozited on Zenodo.org: <a href="https://doi.org/10.5281/zenodo.5082249">https://doi.org/10.5281/zenodo.5082249</a>.</p> <p> </p> <p>Abraham, M. J. et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015).</p> <p>Lindahl, V., Lidmar, J. & Hess, B. Accelerated weight histogram method for exploring free energy landscapes. J. Chem. Phys. 141, 044110 (2014).</p> <p>Souza, P. C. T. et al. Martini 3: a general purpose force field for coarse-grained molecular dynamics. Nat. Methods 18, 382–388 (2021).</p> <p>Melcr, J. Git repository with analysis scripts for MD simulations of permeability through lipid membranes. (2021) doi:<a href="https://doi.org/10.5281/zenodo.5082249">https://doi.org/10.5281/zenodo.5082249</a>.</p>
Simulation Results of the Distributed Schedule Optimization with Energy Storages using EO-COHDA
<p>This dataset contains the result of the evaluation of an approach to integrate energy storages in distributed flexibility negotiations. The data was created using the implemented approach on https://gitlab.com/digitalized-energy-systems/models/eo-cohda. To work with this results we highly recommend to use eo-cohda as well, because it provides a lot convenient utility functions for this.</p> <p>The dataset has been divided in two parts:</p> <ol> <li>the result of the negotiation, <ul> <li>format: hdf, readable using hdf-viewers/python</li> </ul> </li> <li>the generated schedules of the energy storages used for the negotiation. <ul> <li>format: binary, pickled real power schedules, readable using eo-cohda's utility methods.</li> </ul> </li> </ol> <p> </p>
Supplemental data for the report "Optimisation of lattice simulations energy efficiency"
<p>Supplemental data for the report <a href="http://doi.org/10.5281/zenodo.7057319">"Optimisation of lattice simulations energy efficiency"</a>. Also available as a <a href="https://git.dev.dirac.ed.ac.uk/portelli/tursa-energy-efficiency">git repository</a>.</p> <p>It contains:</p> <ul> <li>Full copy of benchmark run directories</li> <li>Power monitoring scripts</li> <li>Power monitoring raw measurements</li> <li>Power monitoring data analysis and results used in the report</li> </ul> <p>For a more complete description, please see the README.md file.</p>
Modeling and simulation of a new Urban Lightweight Electric Vehicle concept based on the optimized use of renewable energies and the reduction of CO2 emissions
<p>This work has produced a series of scientifc contributions. This library develops different mathematical expressions and assumptions for the dynamic modelling of an smart-grid located within a solar-powered ULEV are derived. The code was developed using Dymola</p>
The Diurnal Cycle of Integrated Kinetic Energy and Wind Radii in a Simulated Tropical Cyclone
<p>Model source code and output of a 340-day-long Cloud Model 1 simulation of a tropical cyclone and associated post-processing scripts.</p>
Experimental and simulated data for the article "Reassessing the role and lifetime of Qx in the energy transfer dynamics of Chlorophyll a"
<p>The .zip file contains:</p> <ul> <li>Linear absorption spectra of Chl a in EtOH, acetone, and benzonitrile (BN)</li> <li>Low-temperature emission and excitation anisotropy datasets of Chl a in isopropanol</li> <li>Transient absorption (TA) datasets of Chl a in EtOH, acetone, and BN after B- and Q-band excitation (including pump spectra)</li> <li>Transient absorption anisotropy (TAA) datasets of Chl a in acetone after B-band excitation (including a pump spectrum)</li> <li>Optimized geometries for the Q-band ESA calculations</li> <li><span>Geometries for normal modes used for PES construction</span></li> </ul>
Stochastic Simulation of the Suspended Sediment Deposition in the Channel with Vegetation and Its Relevance to Turbulent Kinetic Energy
<p>This data deposit contains all the datasets needed to draw Figures 8 and 9 in the paper "Stochastic Simulation of the Suspended Sediment Deposition in the Channel with Vegetation and Its Relevance to Turbulent Kinetic Energy", which is now under review for potential publication in Water Resources Research. </p>
``HiPen'': a new dataset for validating (S)QM/MM free energy simulations
<p>Calculating free energy differences between levels of theory (i.e., <span class="math-tex">\(\Delta A^{low \to high}\)</span>) is integral to performing indirect (S)QM/MM free energy simulations. However, connecting levels of theory via free energy simulations has proved difficult due to (1) bond/angle degrees of freedom, (2) dihedral degrees of freedom, and (3) solvent arrangement differences between levels of theory, largely due to partial charge differences between levels of theory. In order to improve calculation of (S)QM/MM free energy simulations, the free energy simulation community should begin to compare methods based on convergence success relative to overall computational time and resource requirements. We have begun to compile such a dataset by calculating <span class="math-tex">\(\Delta A^{MM \to SCC-DFTB}\)</span> in gas phase for 22 drug-like molecules, as seen in our recent publication, Kearns, et al. <strong>2018</strong>, <em>Molecules</em>, Submitted, and we hope that future practitioners will do the same. With this work we hope to provide a standard for comparison for future FES methodologies; additionally, in the near future we hope to continue to add to this dataset including results in more complicated environments such as in solution and in enzyme. All data can be found in our publication and in the accompanying Supporting Information; raw data (such as simulation trajectories and raw data files) can be made available upon request. The purpose of this dataset publication is to make available all starting coordinates, topologies, parameter sets, and input files necessary to replicating the results published in our work.</p>
Simulation and calculation data for the Lorenz energy cycle
<p>The data are for the article "Lorenz Energy Cycle: Another Way to Understand the Atmospheric Circulation on Tidally Locked Terrestrial Planets". The data are simulated by the general circulation model ExoCAM. The horizontal resolution is 4<span>°x5°.</span></p> <p><strong><span>Note: ST: in standard coordinates; TL: in the tidally locked coordinates.</span></strong></p> <ol> <li><span>The simulation data is daily-mean (i.e., '<strong>*.daily.nc</strong>'), including: </span><strong>ST_5d_tidally_locked.cam.daily.nc, ST_60d_tidally_locked.cam.daily.nc </strong>(in the standard coordinates), and <strong>TL_60d_tidally_locked.cam.daily.nc</strong> (in the tidally locked coordinates). </li> <li>The LEC data are calculated by the LEC code released at <em>https://doi.org/10.5281/zenodo.7472396</em>, including:</li> </ol> <ul> <li>Earth: <strong>LEC_energy.1979-*.nc</strong> and <strong>LEC_converse_rate.1979-*.nc</strong></li> <li>Rapidly rotating tidally locked planet (in standard coordinates): <strong>ST_LEC_energy_5d_tidally_locked.nc</strong> and <strong>ST_LEC_conv-rate_5d_tidally_locked.nc</strong></li> <li>Slowly rotating tidally locked planet (in tidally locked coordinates): <strong>TL_LEC_energy_60d_tidally_locked.nc</strong> and <strong>TL_LEC_conv-rate_60d_tidally_locked.nc</strong></li> <li>Slowly rotating tidally locked planet (in standard coordinates): <strong>ST_LEC_energy_60d_tidally_locked.nc</strong> and <strong>ST_LEC_conv-rate_60d_tidally_locked.nc</strong> </li> </ul> <p> </p> <p> </p>
Linearly and Nonlinearly Implicit Schemes for Energy-Stable Simulation of String Vibrations with Collisions: Refinement, Analysis, and Comparison
<p>Sound examples accompanying the manuscript submitted to the Journal of Sound and Vibration</p>
Influence of Adaptive Coupling Points on Coalition Formation in Multi-Energy Systems: Simulation Result Tables
<p>The dataset contains the result tables used for the paper "Influence of Adaptive Coupling Points on Coalition Formation in Multi-Energy Systems". </p> <p>Short description of the tables:</p> <ul> <li>all_in_one_tab.csv: contains all calculated metrics and attributes of the graph over time and adaptation rate</li> <li>event_tab.csv: contains all toggle events of the coupling points</li> <li>impact_dict.csv: contains the calculated impact values for every coupling point</li> <li>node_region.csv: contains region attributes for every node over adaptation rate</li> <li>static_component_properties.csv: contains static attributes </li> <li>static_node_component_properties.csv: contains static node attributes</li> </ul>
Variability of Eddy Kinetic Energy in the Eurasian Basin of the Arctic Ocean inferred from a Model Simulation at 1-km Resolution (data)
<p>Data for "Variability of Eddy Kinetic Energy in the Eurasian Basin of the Arctic Ocean inferred from a Model Simulation at 1-km Resolution"</p>
Accurately Measuring Energy Consumption of Large Cosmological Simulations
<p><a href="https://event.pasc23-conference.org/session/sess138">https://event.pasc23-conference.org/session/sess138</a></p> <p>Minisymposium</p> <p>MS6G - Green Computing Architectures and Tools for Scientific Computing</p>
Simulation data for the office cell building energy model with the attached overhang
<p>Simulation data for 729,000 variants of the office cell building model with the overhang attached over the window. The variants are determined by the overhang depth and height, location, presence of obstacles, orientation and cooling and heating set points. The office cell model is described in the manuscript "Predicting the shape of loads for an office cell with an overhang from a small number of building energy simulations".</p>
Energy simulation outputs for different mitigation scenarios
Open the record for dataset details and reuse information.
Simulation output from "Multiscale MHD-Kinetic PIC Study of Energy Flux Caused by Reconnection"
<p>Results from an implicit particle-in-cell simulation (IPIC3D) of the energy fluxes from magnetic reconnection in Earth's magnetotail. The results consist of six files in HDF5 format that contain the electric field, magnetic field, the density, velocity and energy flux. Each file contains one time step. </p> <p>The IPIC3D simulation is described in</p> <p>Markidis, S., Lapenta, G. and Rizwan-uddin (2010) Multi-scale simulations of plasma with IPIC3D . Mathematics and Computers and Simulation, 80, 1509-1519.</p> <p>The energy conserving version of IPIC3D used in this study is described in</p> <p>Lapenta, G., (2017) Exactly energy conserving semi-implicit particle in cell formulation, J. Computational Physics, 334, 349-366.</p> <p>The application of IPIC3D to Earth's magnetosphere is described in </p> <p>Walker, R., Lapenta, G., Berchem, J., El-Alaoui, M., and Schriver, D., (2019) Embedding particle-in-cell simulations in global magnetohydrodynamic simulations of the magnetosphere, Journal of Plasma Physics, 85(1). </p>
STEG data of manuscript "Electrical Generation of a Ground Level Solar Thermoelectric Generator: Experimental Tests and One-year Cycle Simulation" submitted to Energies
<p>Figure_7_data: laboratory data of TEG output power working at low temperature differences. Data used in Figure 7 of manuscript "Electrical Generation of a Ground Level Solar Thermoelectric Generator: Experimental Tests and One-year Cycle Simulation" submitted to Energies.</p> <p>Figure_9_data: experimental data of TEG temperature differences from July 24 to July 31, 2017. Data used in Figure 9 of manuscript "Electrical Generation of a Ground Level Solar Thermoelectric Generator: Experimental Tests and One-year Cycle Simulation" submitted to Energies.</p> <p>Figures_11_14_data: input and output data of the STEG model. One-year cycle data. Used to obtain figures 11 to 14 of manuscript "Electrical Generation of a Ground Level Solar Thermoelectric Generator: Experimental Tests and One-year Cycle Simulation" submitted to Energies</p>
Data from: Energy expenditure of adult green turtles (Chelonia mydas) at their foraging grounds and during simulated oceanic migration
Measuring the energy requirements of animals under natural conditions and determining how acquired energy is allocated to specific activities is a central theme in ecophysiology. Turtle reproductive output is fundamentally linked with their energy balance so a detailed understanding of marine turtle energy requirements during the different phases of their life cycle at sea is essential for their conservation. We used the non-invasive accelerometry technique to investigate the activity patterns and energy expenditure (EE) of adult green turtles (Chelonia mydas) foraging year-round at a seagrass meadow in Mayotte (n = 13) and during simulated oceanic migration (displacement from the nesting beach) off Mohéli (n = 1), in the south-western Indian Ocean. At the foraging site, turtles divided their days between foraging benthically on the shallow seagrass meadow during daylight hours and resting at greater depth on the inner side of the reef slope at night. Estimated oxygen consumption rates (sinline image) and daily energy expenditures (DEE) at the foraging site were low (sinline image during the day was 1·6 and 1·9 times the respective resting rate at night during the austral summer and winter, respectively), which is consistent with the requirement to build up substantial energy reserves at the foraging site, to sustain the energy-demanding breeding migration and reproduction. Dive duration (but not dive depth) at the foraging site shifted significantly with season (dive duration increased with declining water temperatures, Tw), while overall activity levels remained unchanged. In parallel with a significant seasonal decline in Tw (from 28·9 ± 0·1 °C to 25·3 ± 0·4 °C), there was a moderate (˜19%) but significant decline in DEE of turtles during the austral winter (901 ± 111 kJ day−1), when compared with the austral summer (1117 ± 66 kJ day−1). By contrast, the turtle moved continuously during simulated oceanic migration, conducting short/shallow dives in the day, which (predominately at night) were interspersed with longer and deeper 'pelagic' dives. Estimated oxygen consumption rates during a simulated migration (1·25 ± 0·16 mL O2 min−1 kg−0·83) were found to be significantly increased over the foraging condition, equal to ˜3 times the resting rate at night (0·42 ± 0·02 mL O2 min−1 kg−0·83), and daily energy expenditure amounted to 2327 ± 292 kJ day−1, underlining the tremendous energetic effort associated with breeding migration. Our study indicates that the accelerometry technique provides a new and promising opportunity to study marine turtle energy relations in great detail and under natural conditions.
About prediction of vehicle energy consumption for eco-routing: simulation results
<p>Supplementary materials, experiment results, processing scripts</p>
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Allen Brain Atlas
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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.