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364 results for “Convection”

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zenodo32/100

Dataset for "Investigation of convective transport in the gas diffusion layer used in polymer electrolyte fuel cell"

<p>Dataset to all the figures in the paper (including Supplementary Material):</p> <p>Investigation of convective transport in the gas diffusion layer used in polymer electrolyte fuel cell</p> <p>Beruski, O., Lopes, T., Kucernak, A. R., Perez, J.</p> <p>Phys. Rev. Fluids, 2, 103501, 2017.</p> <p> </p>

opencc-by-4.0Sep 2017View details →
zenodo32/100

Observational data of temperature and oxygen for the study "High-frequency observations of temperature and dissolved oxygen reveal under-ice convection in a large lake"

<p>Matlab data for both temperature and oxygen time series between December 1, 2014, 0:00 (EST) and April 26, 2015, 0:00 (EST). Temperature profiles are sampled every 20 seconds. Dissolved oxygen are sampled every 30 minutes. The depth of each time series are written on the file name.</p>

opencc-by-4.0Oct 2017View details →
zenodo32/100

Supporting data and code for High Concentrations of Nanoparticles from Isoprene Nitrates Predicted in Convective Outflow Over the Amazon

<p>The Archive includes the CloudChem model code, which is used to simulate gas-phase chemical reactions and gas interactions with clouds. Additionally, it includes the simulated gas and particle concentration dataset used to produce figures for the paper "High Concentrations of Low Volatility Isoprene Nitrates Predicted in Convective Outflow Over the Amazon". For more information about the simulation setup and the results, please refer to the paper.</p>

opencc-by-4.0Apr 2024View details →
zenodo32/100

The spatiotemporal pH and carbon concentration during density-driven convection of CO2 in water

<p>The two supplementary videos showed the spatiotemporal pH and carbon concentration in the Hele-shaw cell by a new experimental technique. They are amied to facilitate the understanding of the density-driven convection of CO2 in water. More details of the research could be found on the manuscript of 'arXiv' named:&nbsp; 'Mapping dissolved carbon in space and time: An experimental technique for the measurement of pH and total carbon concentration in density driven convection of CO2 dissolved in water.'</p>

opencc-by-4.0May 2024View details →
zenodo32/100

Summer mesoscale convective systems in convection-permitting simulation using WRF over East China

<p>Mesoscale convective systems (MCSs) are active in East China during the summer, causing significant precipitation and extreme weather. Increasing MCS frequency and intensity highlight the need for better simulation and forecasting. Traditional global and regional models with coarse resolution unable to explicitly resolve convection fail to represent MCSs and their precipitation accurately. This study conducted a 22-year (2000--2021) JJA simulation at a convection-permitting resolution (4km) using the WRF model (WRF-CPM) over East China. The data generated and the code used to analyze are uploaded here.</p>

opencc-by-4.0May 2024View details →
zenodo32/100

Shadowgraph measurements of rotating convective planetary core-style flows (data, code, and figures)

<p>Data files and accompanying matlab analysis code for the paper: "Shadowgraph Measurements of Rotating Convective Planetary Core-Style Flows"</p>

opencc-by-4.0May 2024View details →
zenodo32/100

Solutal convection in 3D porous media - Horizontal and vertical concentration distributions over time at Rayleigh number 10000.

<p>Solutal convection in 3D porous media - Horizontal and vertical concentration distributions over time at Rayleigh number 10000.&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Jul 2024View details →
zenodo32/100

Model simulation output for New Configuration for Impact of microphysics and convection schemes on the mean-state and variability of clouds and precipitation in the E3SM Atmosphere Model

<p>Simulation output from the new configuration model used in the manuscript Impact of microphysics and convection schemes on the mean-state and variability of clouds and precipitation in the E3SM Atmosphere Model</p>

opencc-by-4.0Jul 2024View details →
zenodo32/100

'Reconciling Surface Deflections From Simulations of Global Mantle Convection' : Numerical model dataset

<p><strong>Output data from TERRA simulations included in 'Reconciling Surface Deflections From Simulations of Global Mantle Convection'</strong></p> <p>Dataset includes:</p> <ul> <li>Full density field (`NC_visc_dens_037.tar.gz`)</li> <li>Radial stresses (directory `radial_stresses`)</li> <li>Spherical harmonic coefficients for density field (`density_sph.037`)</li> <li>Radial viscosity factors (`visc.dat`)</li> </ul> <p>Radial stresses are calculated at depths of:</p> <ul> <li>0 km (surface)</li> <li>45 km</li> <li>180 km</li> <li>270 km</li> </ul> <p>and with various amounts of shallow structure removed:</p> <ul> <li>NC_DT_rmir0 - No shallow structure removed</li> <li>NC_DT_rmir1 - 45 km removed</li> <li>NC_DT_rmir2 - 90 km removed</li> <li>NC_DT_rmir3 - 135 km removed</li> <li>NC_DT_rmir5 - 225 km removed</li> <li>NC_DT_rmir7 - 270 km removed</li> </ul>

opencc-by-4.0Jul 2024View details →
zenodo32/100

Flux of density-driven convective dissolution in heterogeneous porous media

<div> <p>Dissolution of CO2 in to brine can increase the density of CO2 rich brine and can cause density driven convection, which can enhance the dissolution of CO2 into brine. Permeability heterogeneity in natural saline aquifers cuases uncertaintis in dissolutio rates, which is measured with mass flux at the&nbsp; CO2-brine interface. Both laboratory experiments and numerical simiulations were conducted to investigate the impact of permeability heterogeneity on convective dissolution. This dataset includes the flux at the CO2-brine interface against time of 48 experiments and 2000 numerical simulations.</p> <p>&nbsp;</p> <p>&nbsp;</p> </div>

opencc-by-4.0Jul 2024View details →
zenodo32/100

Dataset: Mantle convection and water transport

<p>This dataset contains simulation results used for Figures in Nakao et al. (2018, GRL)<br> &ldquo;Roles of hydrous lithospheric mantle in deep water transportation&nbsp;<br> and subduction dynamics&rdquo;.</p>

opencc-by-4.0Mar 2018View details →
zenodo32/100

The Role of Convective Gustiness in Reducing Seasonal Precipitation Biases in the Tropical West Pacific

<p>Necessary outputs and scripts for recreating the figures for the journal article with the same title.</p>

opencc-by-4.0Mar 2018View details →
zenodo32/100

Videos of electrochemiluminescence response for paper "Direct visualization of reactant transport in forced convection electrochemical cells and its application to Redox Flow Batteries"

<p>Videos showing the real time electrochemiluminescent light production in a serpentine flow field of a redox flow battery type system. The two videos show the difference when only an ITO electrode is used compared to when an ITO and carbon paper electrode is used.</p>

opencc-by-4.0Jun 2018View details →
zenodo32/100

Data file for paper: Javier Rubio-Garcia; Anthony R J Kucernak, and Alexandra Charleson, "Direct visualization of reactant transport in forced convection electrochemical cells and its application to Redox Flow Batteries, Electrochemistry Communications, 2018

<p>Excel Data file containing the data presented in the figures of the paper:</p> <p>Javier Rubio-Garcia; Anthony R J Kucernak, and Alexandra Charleson, &quot;Direct visualization of reactant transport in forced convection electrochemical cells and its application to Redox Flow Batteries</p> <p>Electrochemistry Communications, 2018,</p> <p>DOI:10.1016/j.elecom.2018.07.002</p> <p>Please cite the above reference if you wish to use this data</p>

opencc-by-4.0Jul 2018View details →
zenodo32/100

Representation of model error in convective-scale data assimilation: additive noise, relaxation methods and combinations

<p>ModelError_1_dpsdt.tar.xz for Fig. 11<br> <br> ModelError_1_innovstat.tar.xz for Fig. 4, 10 and 13<br> <br> ModelError_1_KESpectrum.tar.xz for Fig. 1<br> <br> ModelError_1_pre_veri.tar.xz for Fig. 8 and 9<br> <br> ModelError_1_precipobs_tau.tar.xz for Fig. 2<br> <br> ModelError_1_refl_veri.tar.xz&nbsp; for Fig. 6, 12 and 14</p> <p>For plotting, MATLAB (R2017b) and python are used</p> <p>&nbsp;</p>

opencc-by-4.0Sep 2018View details →
zenodo32/100

Mushy layer growth and convection, with application to sea ice - Plot Data

<p>&nbsp;This data set contains data and source code for figures 3 and 4 in the draft of paper</p> <p>Mushy layer growth and convection, with application to sea ice&nbsp;</p> <p>Andrew J. Wells, Joseph R. Hitchen&nbsp;and</p> <p>James R. G. Parkinson</p> <p>Department of Physics, University of Oxford,</p> <p>Clarendon Lab, Parks Road, Oxford, OX1 3PU, UK.</p> <p>&nbsp;</p> <p>Figure 3 &nbsp;- MATLAB code available in:</p> <p>Figure3Code.zip</p> <p>- call &quot;Figure3PhilTrans.m&quot; to produce figure 3 of the manuscript.</p> <p>&nbsp;</p> <p>Figure 4 data files are available in:</p> <p>Figure4Data.zip</p> <p>and produced by the source code available on github:</p> <p>&nbsp;<a href="https://github.com/jrgparkinson/mushy-layer/tree/96f2de50c93ef60b982c2dd9197a5c4fe47755e4">https://github.com/jrgparkinson/mushy-layer/tree/96f2de50c93ef60b982c2dd9197a5c4fe47755e4</a></p> <p>&nbsp;</p> <p>N.B. Figure 2 was generated by plotting the analytical solutions described in the paper.&nbsp;</p> <p>This data repository may be updated with latest details during review of the manuscript.</p> <p>&nbsp;</p>

opencc-by-nc-nd-4.0Nov 2018View details →
zenodo32/100

Input files and data for paper "Modules for Experiments in Stellar Astrophysics (MESA): Pulsating Variable Stars, Rotation, Convective Boundaries, and Energy Conservation"

<p>This entry contains input files to reproduce the results of the paper:</p> <p>Modules for Experiments in Stellar Astrophysics (MESA): Pulsating Variable Stars, Rotation, Convective Boundaries, and Energy Conservation.</p> <p>Each zip archive corresponds to a section of the paper, and includes README files in ASCII format with a description. Raw output data and plotting tools are also provided for some of the results.</p>

opencc-by-4.0Mar 2019View details →
zenodo32/100

Deep convective cloud air parcels

<p>The dataset consists of 500 files describing the idealized deep convective cloud air parcels. Values for coordinate (X, Y, Z), velocity components (U, V, W), potential temperature (PTIL), total water content (QT), water vapor (QV), condensate density (QC), precipitation density (QR), net precipitation change (QR_FLUX), relative humidity (RH), buoyancy (BUOY), temperature (T), pressure (P), resolved TKE (KRES), SGS TKE (SGS_TKE), turbulent diffusion (TDIFF) are provided for each parcel at every time step of the simulation.</p>

opencc-by-4.0Oct 2019View details →
zenodo32/100

Differential Rotation in Convective Envelopes: Constraints from Eclipsing Binaries

<p>Data, inlists, and scripts associated with doi:10.1093/mnras/stz2983</p>

opencc-by-4.0Nov 2019View details →
zenodo32/100

Data and script for "Characterizing Wet Season Precipitation in the Central Amazon Using a Mesoscale Convective System Tracking Algorithm"

<p>This is a placeholder for Data and script for "<strong>Characterizing Wet Season Precipitation in the Central Amazon Using a Mesoscale Convective System Tracking Algorithm</strong>" submitted to JGR-atmosphere.</p>

opencc-by-4.0Aug 2024View details →

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International Brain Laboratory public data

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