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17 results for “energy dissipation”
On the Moreau–Jean scheme with the Frémond impact law: energy conservation and dissipation properties for elastodynamics with contact, impact and friction — data
<p>This deposit contains the data output of the systems described in <a href="https://hal.science/hal-04230941">On the Moreau–Jean scheme with the Frémond impact law. Energy conservation and dissipation properties for elastodynamics with contact impact and friction.</a> The codes that generated this data are available in another <a href="../records/10953181">deposit</a> archived on Zenodo, as well as in a GitHub repository archived on <a href="https://archive.softwareheritage.org/swh:1:dir:33ff6d960b70505c7939c0ce21c039cabbe1351c;origin=https://github.com/nickcollins-craft/On-the-Moreau-Jean-scheme-with-the-Fremond-impact-law;visit=swh:1:snp:72aede3d3a464732a36ef79c20ef07eebd1f9918;anchor=swh:1:rev:b63b68c25e72d23d7d9ee30225165fa0ebffb3c2">Software Heritage</a>, which is the preferred method of obtaining the codes. Two of the files in this deposit ("deformed_sliding_block_mesh.png" and "sliding_block_mesh.png") are required for one of the codes in the code deposit to run successfully ("block_mesh_plot.py", with the files assumed to be located in the folder specified in the data_folder variable of the file "path_file.py"), but the deposits are otherwise independent.</p>
Energy transfers and reflexion of infragravity waves at a dissipative beach under storm waves.
<p>%%% Author: Xavier Bertin (xbertin@univ-lr.fr) %%%<br> %%% Date: 15/04/2020 %%% <br> %%% Purpose: This repository provides the field observations and XBeach model input %%%<br> %%% required to reproduce the results presented in paper referred below. %%%<br> %%% Reference: Bertin, X., Martins, K., de Bakker, A., Guérin, T., Chataigner, T., %%%<br> %%% Coulombier, T. et de Viron, O., 2020. Energy transfers and reflexion of %%%<br> %%% infragravity waves at a dissipative beach under storm waves. In press %%%<br> %%% to Journal of Geophysical Research-Ocean. %%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p>*The directory Obs includes:<br> -The wave bulk parameters computed as explained in the paper for the 10 sensores used in this<br> study: the offshore ADCP1, the intertidal PT1, PT2, ADCP2/PT3, PT4, PT5, ADV/PT6, PT7/Altus, PT8<br> and PT9. Each file has the same format and includes: the date (YYYY MM DD), the time (HH MM SS), <br> the mean water depth, the spectral significant wave height Hm0, mean wave periods Tm01 and Tm02, <br> the discrete and continuous peak periods, the energetic wave period Tm0,-2 and the spectral<br> significant height of IG waves Hm0,IG. <br> -The spectral significant height Hm0,IG+ and mean wave period Tm02,IG+ of incoming IG waves<br> separated at the ADCP2 and ADV using the method of Guza et al. (1984). The two files have the same <br> format and includes the date (YYYY MM DD), the time (HH MM SS), Hm0,IG+ and Tm02,IG+.<br> -The position of each sensore measured with a geodetic GNSS and provided in the same datum as the <br> bathymetry used in the model (Lambert93 and mean sea level). </p> <p>*The directory XBeach includes all the necessary files required to reproduce the simulations presented <br> in this study<br> -The bathymetry interpolated over a rectilinear grid, with X and Y given in Lambert93 coordinates (files<br> X_L93.grd and Y_L93.grd) and Z referred with respect to mean sea level (Z_L93.grd).<br> -The water level fluctuations measured at ADCP1 (WLevel_ADCP_201702.dat).<br> -The XBeach input file (params.txt) and a file providing the list of directional wave spectra<br> provided in the directory "Spectra_WWIII". These spectra were computed from a regional application of <br> WaveWatchIII over the North Atlantic Ocean and forced with CFSR wind fields but they were converted<br> in the format of SWAN, readable by XBeach.</p>
Scripts and datas for "Global Estimation of the Eddy Kinetic Energy Dissipation from a Diagnostic Energy Balance"
<p>Input and output datasets used for a global reconstruction of the eddy kinetic energy (EKE) dissipation rate in relation to a submitted work :</p> <p><strong>R. Torres, R. Waldman, J. Mak and R. Séférian </strong>: <em>Global Estimation of the Eddy Kinetic Energy Dissipation from a Diagnostic Energy Balance</em>.</p> <p>Inputs datas include a merge of 2 datasets from the World Ocean Atlas 2018 (WOA18, Garcia et al., 2019) and cover the 1995-2017 (95B7) period. Folder structure for the surface altimetry L4 datasets from the EU-Copernicus Marine Services (2021) is kept empty in order to limit the archive size. Datas can be download <a href="https://data.marine.copernicus.eu/product/SEALEVEL_GLO_PHY_L4_MY_008_047/services">here</a>.</p> <p>Optional datasets include CMEMS MDT product (<em>CMEMS/SEALEVEL_GLO_PHY_MDT_008_063/P20Y</em>) downloaded <a href="https://data.marine.copernicus.eu/product/SEALEVEL_GLO_PHY_MDT_008_063/">here</a> and ocean masks (<em>misc/basins/doi_10.5281</em>) from Martinez-Moreno et al. (2021).</p> <p>In addition, simulation outputs from the NEMO-OMIP2 model runned with the GEOMETRIC parameterization are processed (mainly time-averaged) and stored in <em>CNRM/runs/omip2_LR.Geom_Emin0-alpha01_1cyc-trd/post</em>. These files are used in the uncertainties and errors quantification.</p> <p>Outputs and published results are stored in each individual product post-processing folder while final EKE dissipation computation are located in the <em>EKE_dissipation_rate</em> folder since it results from a combination of multiple products.</p> <p>IPython notebooks for computing and plotting global maps are also provided :</p> <ul> <li><em>1-post_process_climato.ipynb</em> : compute from the climatology (e.g. WOA18 datas) the EKE dissipation timescales (units in days) and the surface modes with rough topography (LaCasce and Groeskamp, 2020).</li> <li><em>2-post_process_altimetry.ipynb</em> : compute from altimetry (CMEMS) datasets the EKE at surface and eventually coarsen the grid from 0.25 to 1 degree in order to match the climatology grid.</li> <li><em>3-compute_global_eke_dissipation.ipynb</em> : combine both outputs from the two above scripts to compute the global EKE dissipation. The script also plots new maps.</li> <li><em>0-plot_global_maps.ipynb</em> : plot the global maps for climatology and altimetry products.</li> <li><em>0-plot_lbekedis_ogcm.ipynb</em> : plot and analyse EKE timescale errors from the NEMO-OMIP2 simulation outputs.</li> </ul> <p>Note however that these scripts use the author python library XOCE availbale on GitHub: https://github.com/torresr-cnrm/xoce. All the scripts have been runned using the version 0.2 of XOCE. Feel free to contact (romain.torres@meteo.fr) for any help in installing and using this library.</p>
Constraining an eddy energy dissipation rate due to relative wind stress for use in energy budget-based eddy parameterisations
<p>Code and data to reproduce results in the EGU Ocean Science journal paper, entitled 'Constraining an eddy energy dissipation rate due to relative wind stress for use in energy budget-based eddy parameterisations'.</p> <p>The data and corresponding scripts in this repository are:</p> <ul> <li>MITgcm simulation data for an anticyclone and cyclone under absolute and relative wind stress. <ul> <li>ACE_eta_{absolute or relative}_10km_A25_v2_ext.nc</li> <li>ACE_tau_{absolute or relative}_10km_A25_v2_ext.nc</li> <li>ACE_temp_{absolute or relative}_10km_A25_v2_ext.nc</li> <li>CE_eta_{absolute or relative}_10km_A25_v2.nc</li> <li>CE_tau_{absolute or relative}_10km_A25_v2.nc</li> <li>CE_temp_{absolute or relative}_10km_A25_v2.nc</li> </ul> </li> <li>Mean energetics computed using eddy_energy_mean.m. User may need to comment out lines of code relating to wvel. This also requires geostrophic_uv.m, rho_ref.mat, and ocean_vertical_grid.nc. <ul> <li>{ACE or CE}_energy_{absolute or relative}_total_10km_A25_mean.nc</li> </ul> </li> <li>Predicted eddy energy computed using predict_*.m scripts. n.b. viscous term left in absolute scripts, though we only need the first index of total eddy energy in abs cases. The abs cases are really just making the abs.mat files, see time_series_plots.py.</li> <li>Time-series figures (3 and 5) produced using time_series_plots.py.</li> <li>Initial figures (1 and 2) produced using initial_plots.py.</li> <li>Relative vorticity in Fig. 4 computed and plotted using eddy_evolution_plot.py.</li> <li>Dissipation rates computed in calc_dissipation_rate.m. Requires dynmodes.m and GSW toolbox to run. Also needs data Chelton2011_Le.csv and Chelton2011_Rd.csv. <ul> <li>diss_rate*.nc</li> </ul> </li> <li>Dissipation rate and climatology figures (6, 7, 8, and 9) made in diss_rate_plot.py</li> </ul> <p>Information and data needed to run the MITgcm can be found <a href="https://github.com/thomaswilder/jpo_eddy-scripts">here</a>.</p>
Data from: Can IR images of the water surface be used to quantify the energy spectrum and the turbulent kinetic energy dissipation rate?
Open the record for dataset details and reuse information.
Data: relation between soft tissue energy dissipation and leg stiffness in running at different step frequencies
<p>Data set - paper: <span>Relation between soft tissue energy dissipation and leg stiffness in running at different step frequencies </span></p>
Heating by Dissipation of Energy from Absorbed Light: Molecular Mechanisms Underlying the Survival Strategy of Polar Algae
<p>The original data, which served as the source material for the scientific article on the polar alga <em>Pediastrum orientale, co</em>llected from Reindeer Lake on Spitsbergen. Consists of datasets obtained using various techniques: fluorescence microscopy (microscope_fluo), fluorescence lifetime microscopy (FLIM), high-performance liquid chromatography (HPLC), atomic force microscopy (AFM), Raman microspectroscopy (RAMAN), fluorescence lifitime spectroscopy (lifetime) and fluorescence spectroscopy (Fluo).<br><br>HPLC - Nexera LC-40 (Shimadzu, Japan)<br>FLIM - OLYMPUS IX71 confocal microscope MicroTime 200 with SymPhoTime 64 software package (PicoQuant, GmbH, Germany)<br>RAMAN - inVia Reflex confocal Raman microscope with the WiRE 5.5 software package (Renishaw, UK)<br>AFM - JPK Nanowizard 3 system with JPKSMP data processing software (Bruker, USA)<br>Lifetime - FluoTime 300 spectrometer with FluoFit Pro v 4.5.3.0 (PicoQuant, Germany)<br>Microscope_Fluo - Zeiss LSM980 confocal microscope with Airy2 and Elyra7 detectors and ZEN 3.1 software (Zeiss, Germany)<br>Fluo - OLYMPUS IX71 confocal microscope MicroTime 200 system with the spectrograph SR-163 and the Newton 970 EMCCD camera (Andor Technology)<br><br></p> <p> </p> <p> </p>
Precipitation-induced dissipation limits storm kinetic energy in a warming climate
<p>This zip file contains data and codes to reproduce the figures of a manuscript on precipitation-induced dissipation in X-SHiELD.</p> <p>Contact mbolot@princeton.edu for questions.</p>
Energy dissipation of a carbon monoxide molecule manipulated using a metallic tip on copper surfaces
<p>Computational dataset for our research papers on energy dissipation of a carbon monoxide molecule manipulated using a metallic tip on copper surfaces:<br><br></p> <p>N. Okabayashi, T. Frederiksen, A. Liebig, and F. J. Giessibl<br><em>Dynamic friction unraveled by observing an unexpected intermediate state in controlled molecular manipulation</em><br><a href="https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.131.148001">Phys. Rev. Lett. <strong>131</strong>, 148001 (2023)</a></p> <p>N. Okabayashi, T. Frederiksen, A. Liebig, and F. J. Giessibl<br><em>Energy dissipation of a carbon monoxide molecule manipulated using a metallic tip on copper surfaces</em><br><a href="https://journals.aps.org/prb/abstract/10.1103/PhysRevB.108.165401">Phys. Rev. B <strong>108</strong>, 165401 (2023)</a></p>
Codes and datasets associated with the paper "Parameterizing the Energy Dissipation Rate in Stably Stratified Flows"
<p>Here, you will find some of the codes and datasets utilized in the article: "Parameterizing the Energy Dissipation Rate in Stably Stratified Flows". (<a href="https://arxiv.org/abs/2001.02255">https://arxiv.org/abs/2001.02255</a>)</p> <p>The direct numerical simulations were performed using the HERCULES code (<a href="https://github.com/friedenhe/HERCULES">https://github.com/friedenhe/HERCULES</a>). For all the simulations, the bulk Reynolds number is kept at 20,000. The bulk Richardson number is varied from 0.1 to 0.5. The zip files Re20000RiXX.zip contains the various turbulence statistics. </p> <p>Re20000Ri10: Ri_b = 0.1</p> <p>Re20000Ri20: Ri_b = 0.2</p> <p>Re20000Ri30: Ri_b = 0.3</p> <p>Re20000Ri40: Ri_b = 0.4</p> <p>Re20000Ri50: Ri_b = 0.5</p> <p>Every dat file (within the zip archives) is in ascii format and has 10 rows and 286 columns. Each row corresponds to normalized time (ranging from 1 to 10). Each column represents vertical model levels. </p> <p> </p>
Simulation data for "Tuning Adhesion and Energy Dissipation in Polymer Films between Solid Surfaces via Grafting and Cross-Linking"
<p>LAMMPS input and data files, Jupyter notebooks used for the analysis of the MD simulations.</p>
Mechanical data of rotary shear experiments, temperature measurements, and temperature numerical models for the manuscript: "Mechanical energy dissipation during seismic dynamic weakening in calcite-bearing faults"
<p>All data included in this data repository is ancillary to the manuscript "Energy dissipation during dynamic weakening in calcite-bearing fault rocks", submitted to Journal of Geophysical Research: Solid Earth. </p><p>The data consists in time series of high velocity friction experiments run with SHIVA (INGV, Rome), time series acquired from a two-color pyrometer (UC3M), the synchronization of the two, and numerical models. The data format is .mat, proprietary to Matlab, but they can be easily accessed with Python (see <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html">link</a>). Each .mat contains vector of the measured variables when opened from Matlab, or dictionaries when opened using the scipy.loadmat() function. </p><p>SHIVA and PYRO red data (calibrated data), fin data (synchronized data), and shivaRED vect data (numerical model data) are included in this data repository in separate folders. Numerical models are grouped in subfolder by type of model (the relation fin data to model is 1:n). We included the scripts to convert SHIVA raw data into SHIVA red data (<a href="https://github.com/aretu/shivaUNIX">link to shivaUNIX</a>), SHIVA and PYRO red data into fin data (/scripts/syncing2021.m), to obtain numerical models from fin data (<a href="https://github.com/aretu/shivaRED">link to shivaRED</a>), and to plot data (/scripts/making plots.ipynb).</p>
The dynamics of tropical storm Mulan-induced destruction in 3D structure and dissipation in kinetic energy of an anticyclonic eddy in the northern South China Sea
<p>dataset for The dynamics of tropical storm Mulan-induced destruction in 3D structure and dissipation in kinetic energy of an anticyclonic eddy in the northern South China Sea</p>
Adrenergic-induced ERK3 pathway drives lipolysis and suppresses energy dissipation
GEO Series GSE142424. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Protein kinase D1 deletion in adipocytes enhances energy dissipation and protects against adiposity
GEO Series GSE104797. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Supplementary data to 'Do beach profiles under non-breaking waves minimize energy dissipation?'
<p>This repository contains the following files:</p> <p>1. 'data_for_tables.xlsx' summarises the results of using the codes in doi:10.5281/zenodo.3725621 for all profiles considered in this associated paper. These data feed into the tables of the paper. Additional notes can be found in the spreadsheet.</p> <p>2. Three files with extension *.sfit are sessions for the 'cftool' toolbox of Matlab (version 2017b) that give the results of the best-fits to the measured profiles.</p>
Imaging of Non-local Hot Electron Energy Dissipation via Shot Noise: Data deposit
<p>This content of this data deposit is the following:</p> <p>Archives ‘Data (Fig.1-3, and Fig.S1-S11)’: Raw data and executable files related to figures 1 to 3, and figures S1 to S11 of the publication</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.