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34 results for “heat transport”
Lagrangian Decomposition of the Meridional Heat Transport at 26.5N - Water Parcel Crossings of the RAPID 26.5N Array
<p>This dataset contains the initial and final positions and properties of Lagrangian trajectories evaluated using 5-day mean velocity and tracer fields output from the ORCA0083-N06 ocean sea-ice model hindcast (1958-2015). Numerical water parcels are initialised to sample the full-depth southward transport across the RAPID 26.5N array every month during 2004-2015. Water parcels are advected backwards-in-time using a bespoke version of TRACMASS v7.1 Lagrangian particle tracking tool which enables users to specify a custom domain using a mask netCDF file.</p><p>Particles are initialised on the first-available day of each month (based on the centre of the model 5-day mean field windows) between 2004 and 2015 (inclusive) before being advected backwards-in-time within the North Atlantic Ocean until any one of four termination conditions are met: (1) water parcels reach the RAPID 26.5N array, (2) water parcels reach the OSNAP (West or East) arrays in the subpolar North Atlantic, (3) water parcels reach either the English Channel or Gibraltar Strait, or (4) particles reach the maximum advection time of 25-years. The 25-year maximum advection time ensures that we adequately resolve the subtropical gyre circulation north to the RAPID 26.5N array. The pathway transporting dense North Atlantic Deep Water from the OSNAP arrays to RAPID at 26.5N is not fully resolved in this Lagrangian experiment since these water parcels transit on multi-decadal timescales.</p><p>The number of water parcels initialised in each model-grid cell scales with the total northward transport through that cell, such that the maximum possible transport conveyed by any single particle is 5.0 mSv (mSv == 10-3 Sv), enabling the calculation of robust Lagrangian statistics. In reality, the average. water parcel has an associated volume transport of 3.3 mSv which is conserved throughout its circulation.</p><p>Water parcel locations (converted to geographical coordinates) and properties (conservative temperature, absolute salinity, potential density [TEOS-10]) are output on every model-grid cell crossing. TRACMASS determines particle properties on grid-cell crossings by taking the average of the properties stored at the nearest two T-grid points. Here, we provide the initial and final locations and properties of all water parcels initialised from RAPID 26.5N.</p><p>All Lagrangian experiments were completed using the JASMIN High-Performance Computing facility (<a href="https://jasmin.ac.uk">https://jasmin.ac.uk</a>).</p><p><strong>For a complete description of the ORCA0083-N06 hindcast configuration see:</strong> Moat et al. (2016).</p><p><strong>For a complete description of TRACMASS v7.1 see</strong>: <a href="https://www.tracmass.org">https://www.tracmass.org</a></p>
How to transport veterinary drugs in insulated boxes to avoid thermal damage by heating or freezing
<p><strong>Documentation of the empiric data, measured in the heating and cooling chamber to determine the thermal constant for the investigated transport boxes.</strong></p> <p> </p> <p><strong>Background: </strong>The transport of veterinary drugs must comply with the general standards for drug storage. Although many vehicles are equipped with active heating and/or cooling devices assuring recommended storage conditions, simple insulated transport boxes are also often used. In this study, measurements for typical transport boxes were performed under laboratory conditions by the use of a climate chamber for a temperature of -20°C and 45°C to investigate the impact of box size, insulation material, liquid vs. dry filling products, filling degree and other parameters on the thermal performance of insulated boxes. Model calculations and instructions are presented to predict the retention time of recommended drug storage temperatures.</p> <p><strong>Results: </strong>The measurements and the model calculations showed that the loading of the transport boxes with additional water bottles to increase the heat capacity is appropriate to prolong the retention time of the recommended temperature range of the drugs. Insulated transport boxes are not suitable to store drugs over a period of more than approximately 12 hours. For practical use, a recipe is presented to measure the thermal properties of a transport box and the related retention time for which the recommended storage temperatures can be assured.</p> <p><strong>Conclusions: </strong>The following principles for drug transportation in vehicles are recommended: (1) Before transfer into boxes, drugs should always be thermally preconditioned (2) Increase the filling degree of the boxes with thermally preconditioned water bottles or re-usable thermal packs will increase the heat capacity. Do not deep-freeze the bottles or packs below 0°C to avoid drug freezing due to contact. (3) Open the lid of the boxes only to uncase drugs that are immediately needed. (4) The bigger the box and the higher the filling degree, the longer the retention time of the transport box. (5) Wherever possible, place the drug box at a cool site inside the vehicle. (6) The monitoring of the inside temperature of the transport boxes is recommended. <br> By the proper use of such transport boxes the recommended temperatures can be maintained over one working day.</p> <p> </p>
A semi-analytical solution for heat transport in rock with parallel fractures and a heat source in both fracture and matrix
<p>In this study, we propose a two-dimensional semi-analytical solution framework based on a Green’s function approach for a flexible heat source definition, including source dimensions, energy delivery strength and duration, and the presence of a heat source in the matrix and/or fracture. The solution fully accounts for heat conduction, advection, dispersion and transient heat exchange between the mobile and immobile phases in a system of parallel fractures. The solution having a strip heat source extending from a fracture into the matrix indicates that one-dimensional heat conduction in the matrix underestimates and overestimates temperature responses at early and later times, respectively.</p> <p>The dataset is for the figures 2-7 in the journal paper. </p>
Efficiency and heat transport processes of low-temperature aquifer thermal energy storage systems: new insights from global sensitivity analyses - Supporting Dataset
<p>This dataset contains the files used to substantiate the outcomes of the publication <em>"Efficiency and heat transport processes of low-temperature aquifer thermal energy storage systems: new insights from global sensitivity analyses"</em>. </p> <p>It includes the output of 250 random model realizations of an aquifer thermal energy storage system in a thick productive aquifer (Case 1). It also includes the output of 500 random model realizations of an aquifer thermal energy storage system in a shallow alluvial aquifer (Case 2 part 1 and part 2).</p> <p>If there is interest in generating new output, the datset also includes the model input files for both cases.</p> <p>(Scripts to process the output data or to generate new output data can be found in the corresponding GitHub repository: https://github.com/lukatas/ATES_SensitivityAnalyses.git )</p>
Modeling ocean heat transport to the grounding lines of Pine Island, Thwaites, Smith, and Kohler glaciers, West Antarctica
Open the record for dataset details and reuse information.
Heat transport through diurnal warm layers: datasets
<p>Datasets associated with the paper "<strong>Heat transport through diurnal warm layers</strong>" by Hughes, Moum, and Shroyer.</p> <p>Full details in readme.pdf</p> <p> </p>
CESM1-SOM Climatologies used for "Climate Sensitivity is Sensitive to Changes in Ocean Heat Transport" (published in Journal of Climate, Mar 2022)
<p>CESM1-SOM climatologies.</p> <p>Pre-industrial control run = SOM_Control.cam5.0030-0059.ann.nc</p> <p>CO2-doubling experiments:</p> <ul> <li>OHT + 30% = SOM_OHFC_P30_2XCO2_032019.cam5.0030-0059.ann.nc</li> <li>OHT + 15% = SOM_OHFC_P15_2XCO2_032019.cam5.0030-0059.ann.nc</li> <li>Control OHT = SOM_2XCO2_032019.cam5.0030-0059.ann.nc</li> <li>OHT - 15% = SOM_OHFC_M15_2XCO2_032019.cam5.0030-0059.ann.nc</li> <li>OHT - 30% = SOM_OHFC_M30_2XCO2_032019.cam5.0030-0059.ann.nc</li> </ul>
A semi-analytical solution for heat transport in rock with parallel fractures and a heat source in both fracture and matrix
<p>In this study, we propose a two-dimensional semi-analytical solution framework based on a Green’s function approach for a flexible heat source definition, including the heat source dimensions, energy delivery strength and duration, and the presence of a heat source in the matrix and/or fracture. The solution fully accounts for heat conduction, advection, dispersion, and transient heat exchange between the fracture fluid and rock matrix in a system of parallel fractures. </p> <p>The dataset is for the figures 2-9 in the journal paper. </p> <p>The computation code is available to generate the temperature in rock matrix or fracture using Matlab. </p>
Data for: Influence of Anomalous Ocean Heat Transport on the Extratropical Atmospheric Circulation in a High-Resolution Slab-Ocean Coupled Model
<p>This dataset, provided in NetCDF format, supports the research presented in the paper titled "Influence of Anomalous Ocean Heat Transport on the Extratropical Atmospheric Circulation in a High-Resolution Slab-Ocean Coupled Model." Please contact Dr. Sun (ltsun@rams.colostate.edu) if you have any questions.</p>
CMIP6 HighResMIP HadGEM3-GC3.1 LL, MM and HH datasets for the heat transport in the lower limb of the AMOC
<p>These data include datasets for "Resolving mesoscale eddies is crucial for the heat transport in the lower limb of the AMOC", submitted to <em>Geophysical Research Letters</em>.</p> <p>Each file is prefixed with model resolution (LL, MM and HH) and experiment details (control, SSP5-8.5). Files ending with '_velocities' and '_age_pressure' contain diagnostics projected on the isopycnal surface (sigma2=36.95), depicted in Figures 2 and 3 of the submitted paper. </p>
Meridional and zonal eddy-induced heat and salt transport in the Bay of Bengal and their seasonal modulation
<p>CTD (conductivity-temperature-depth) and ADCP (Acoustic Doppler Current Profiler ) data in the Bay of Bengal.</p>
Heat transport across the Antarctic Slope Front controlled by cross-slope salinity gradients
<p>Feb 2023 updates: </p> <ul> <li>Add code for EKE spectral analysis to MITgcm_ASF-heat-ver3/analysis/spectrum/</li> <li>Add products of 5km and 10km runs to products_new-ver3</li> <li>Add MITgcm source code, copied from <a href="http://mitgcm.org/">http://mitgcm.org</a></li> </ul> <p>This release contains updates on analysis code and products.</p> <ul> <li>MITgcm_ASF-heat-ver3/<strong>newexp</strong>/: the Matlab scripts used to generate and run the MITgcm simulations</li> <li>MITgcm_ASF-heat-ver3/<strong>analysis</strong>/<strong>cross_slope</strong>/ and MITgcm_ASF-heat-ver2/<strong>analysis</strong>/<strong>plots</strong>/: the Matlab scripts used to analyze model output and make plots.</li> <li>MITgcm_ASF-heat-ver3/analysis/<strong>spectrum</strong>/: the<strong> </strong>Matlab<strong> </strong>scripts to calculate EKE spectra<strong> </strong></li> <li>exps_configuration.zip: the configurations of the MITgcm simulations.</li> <li><strong>products_new-ver3.zip</strong>: the products calculated from MITgcm diagnostics, including 7-year means of all the model outputs, overturning streamfunctions, neutral density, shoreward heat transport, kinetic energy, temporal decomposition, isopycnal thickness fluxes of the 5km and 10km runs, etc. </li> <li>ThicknessFlux_FreshShelf.zip: products of isopycnal thickness flux, used to calculate the decomposition of eddy/tidal heat advection/diffusion, for the "fresh-shelf" simulation. </li> <li>ThicknessFlux_ref.zip: as above, but for the reference simulation.</li> <li>ThicknessFlux_DenseShelf.zip: as above, but for the "dense-shelf" simulation. </li> </ul> <p>The source code of the Massachusetts Institute of Technology General Circulation Model (MITgcm) is available at: <a href="http://mitgcm.org/">http://mitgcm.org</a>.</p> <p><strong>All the raw data of the model output are available at: <a href="https://doi.org/10.15144/S47P49">https://doi.org/10.15144/S47P49</a>.</strong></p> <p>To reproduce MITgcm_ASF simulations: </p> <ol> <li>Start each simulation with a 20-year spin-up integration. Before running each simulation, you need to substitute <em>&OBCS_PARM04</em> with <em>&OBCS_PARM05 </em>in the file <em>input/<strong>d</strong>ata.obcs</em>, and substitute <em>&EXF_NML_05 </em>with <em>&EXF_NML_OBCS</em> <em> </em>in the file <em>input/data.exf</em>. For simulations with very fresh shelf waters (e.g., shelf salinity = 33 psu), you need to spin up the simulation with a very small time step (e.g., 60s) for ~ two months, and then use a larger time step. </li> <li> <p>Initialize the production run from the corresponding spin-up run, using the Matlab script <em>initialize.m</em> in the folder<em> MITgcm_ASF-heat-ver2/newexp/. </em>When using the LAYERS package, you need to substitute<em> numperlist = 1</em> with <em>numperlist = 2 </em>in the file<em> code/DIAGNOSTICS_SIZE.h</em> before running the simulations.</p> </li> </ol> <p> </p> <p>Notes on calculationg the overturning streamfunction and its mean/eddy/tidal decomposition using the MITgcm LAYERS package: </p> <ul> <li>avg_t: Calculate time averages. It has been modified since the vertical number of layers can be different from Nr. </li> <li>calc_Overturning_pt, usscar_plot_overturning_pt: calculate and plot eddy/mean/isopycnal overturning streamfunction using potential temperature layer fluxes.</li> </ul> <ul> <li>calc_Overturning_rho, usscar_plot_overturning_rho: calculate and plot eddy/mean/isopycnal overturning streamfunction using potential density layer fluxes.</li> </ul> <ul> <li>calc_Overturning_pt_Aocean, usscar_pt_overturning_rho_Aocean (<strong>recommended if your bathymetry is not flat</strong>): calculate and plot eddy/mean/isopycnal overturning streamfunction using <em>potential temperature</em> layer fluxes. For each latitude, use the total ocean area below a certain level to interpolate the streamfunction from pt space to z space. </li> </ul> <ul> <li>calc_Overturning_rho_Aocean, usscar_plot_overturning_rho_Aocean (<strong>recommended <strong>if your bathymetry is not flat</strong></strong>): calculate and plot eddy/mean/isopycnal overturning streamfunction using <em>potential density</em> layer fluxes. For each latitude, use the total ocean area below a certain level to interpolate the streamfunction from potential density space to z space.</li> </ul> <ul> <li>calc_decomposition_OT, plot_OT_rho_Aocean_TidalEddyMean: decompose the isopycnal overturning streamfunction into <strong>tidal</strong>/eddy/mean components, using potential density layer fluxes.</li> </ul> <p>Feel free to contact Yidongfang Si via <strong>ysi@g.ucla.edu</strong> if you have any questions.</p>
Data for The influence of bathymetry over heat transport onto the Amundsen Sea continental shelf
<p>Data and code for producing figures for the paper 'The influence of bathymetry over heat transport onto the Amundsen Sea continental shelf'</p>
Dissimilarity of turbulent transport of momentum and heat under unstable conditions linked to convective circulations
<p><span>The dissimilarity between the turbulent transport of momentum and heat under unstable conditions and its physical mechanisms are investigated in this study, based on the multiple-level turbulence observation from Tianjin's 255-m meteorological tower. The transport dissimilarity is observed from the surface layer to the lower part of the mixed layer as atmospheric instability increases. Although the transport dissimilarity is accompanied by the development of plumes and thermals under unstable conditions, plumes and thermals can produce intense transport of momentum and heat simultaneously. It is convective circulations related to vigorous thermals that cause transport dissimilarity. The horizontal divergence induced by convective circulations imposes a dominant large-scale reduction in the along-wind velocity component near the surface, while the temporal variations in temperature mainly reflect the role of plumes and thermals. This difference in respective physical processes subsequently leads to dissimilar transport between momentum and heat under unstable conditions. Therefore, the influence of convective circulations on the momentum-flux estimation should be considered in</span><span> atmospheric numerical models, </span><span>particularly for the simulation and prediction of severe convective weather.</span></p>
Dataset for the Figure 2 in manuscript "Numerical study of coupled water and vapour flow, heat transfer, and solute transport in variably-saturated deformable soil during freeze-thaw cycles"
<p>This dataset includes the gathered experimental measurements of a freezing test by Wu (2017) for the model's verification shown in Figure 2 of the manuscript entitled 'Numerical study of coupled water and vapour flow, heat transfer, and solute transport in variably-saturated deformable soil during freeze-thaw cycles'' by Huang, X., and Rudolph, D.L.</p>
Dataset associated with paper "On the contribution of transient diabatic processes to ocean heat transport and temperature variability"
<p><strong>Data repository for Yung & Holmes (2023) </strong><i><strong>On the contribution of transient diabatic processes to ocean heat transport and temperature variability</strong></i><strong>, submitted to Journal of Physical Oceanography</strong></p><p>This repository contains:</p><p>- processed global ocean model data (including all data required to reproduce figures)</p><p>- analysis scripts to create figures (extra analysis and computation scripts are available at https://github.com/claireyung/Eddy_Diathermal_Code)</p><p>- 1D mixing model code (also available at https://github.com/claireyung/Diff1D/tree/Claire_2022)</p><p>Files: </p><p>- Diff1D.zip contains matlab code, figure code and output data</p><p>- DHT-MHT-Paper_Plots.ipynb contains figure code for most paper figures</p><p>- Seasonal_range_SST_SHF_MLD.ipynb contains figure code for Fig. 6</p><p>- 10Year-025-Monthly_decomp.ipynb, 1Year-025-Daily_decomp.ipynb demonstrate analysis binning process to go from model data to processed data</p><p>Processed data:</p><p>- ocean_grid.nc, output000fwmass_m_copy.nc have grid information</p><p>- ocean_heat_mean.nc, ocean_month_mean.nc, ocean_wmass_mean.nc contain 10 year means of model diagnostics</p><p>- seasonal_climatology_monthly_temp_SF_MX_mld_compress.nc contains monthly climatology used for Fig. 6</p><p>- min_SST_10yr_daily_data.nc and max_SST_10yr_daily_data.nc have the maximum and minimum daily average SST</p><p>- 10day_mean_10year_means.nc, 2mo_mean_10year_means.nc, sc_2mo_mean_10year_means.nc, 1day_mean_10year_means.nc, Mean_10year_means.nc, True_10year_means.nc are processed and binned data at the various timescales for plotting.</p><p>The model used is the 0.25 degree RYF version of ACCESS-OM2 (Kiss et al. 2020, GMD). The configuration used to run the model can be found here: https://github.com/claireyung/025deg_jra55_ryf/tree/norediGM_run_diaT_exdiags_mixingterms_smallterms . The zenodo repository https://zenodo.org/records/10052149 contains the files needed to run the model.</p><p>Contact: claire.yung@anu.edu.au</p>
Dissimilarity of turbulent transport of momentum and heat under unstable conditions linked to convective circulations
Open the record for dataset details and reuse information.
E3SM - Drake Passage Transport, 26.5 AMOC, Northward Heat Transport at 26.5N, from Hewitt et al 2020
<p>E3SM monthly output has been annually averaged and processed to derive the maximum overturning at 26.5 N, the northward heat transport at 26.5N and the drake passage transport. The model output includes the low resolution configuration and high resolution configuration. Both runs use the 1950 control from the HighResMIP protocol.</p>
Ocean Meridional Heat Transport Values
<p>This dataset includes data for Figures 2, 4, 6, 10 and 12 of the "Observation-based Estimate of Global and Basin Ocean Meridional Heat Transport Time Series" paper. This paper is published through the Journal of Climate (2019) and by authors, Kevin E. Trenberth, Yongxin Zhang, John T. Fasullo and Lijing Cheng. The data files associated with this dataset provides tabular values and are for general use.</p>
Supplementary Data: Heat transport in pristine and polycrystalline single-layer hexagonal boron nitride
<p>This is part of the supplementary material for the manuscript ``Heat transport in pristine and polycrystalline single-layer hexagonal boron nitride'' by Haikuan Dong, Petri Hirvonen, Zheyong Fan, and Tapio Ala-Nissila. Phys. Chem. Chem. Phys. <a href="http://dx.doi.org/10.1039/C8CP05159C">http://dx.doi.org/10.1039/C8CP05159C</a> [https://arxiv.org/abs/1806.07936]</p> <p>There are 6*8=48 files, each corresponding to a flat polycrystalline h-BN sample before molecular dynamics relaxation.</p> <p>In each file, the first row gives the lengths Lx and Ly (Lx=Ly) in the x and y directions, which will be used to set up the box in molecular dynamics simulations.</p> <p>Starting from the second row, the first column gives the atom types (0 for B and 1 for N), the second column gives the x coordinates and the third column gives the y coordinates.</p> <p>The number of atoms equals the number of rows in the file minus 1.</p> <p>The files are named as poly-a-b.xy, where "a" roughly corresponds to the linear size Lx and "b" labels the 8 samples for each size.</p> <p>The grain size d and the linear size Lx are related by d=Lx/4, so the 6 grain sizes are 10, 12.5, 17.5, 25, 37.5, and 50 nm.</p> <p> </p>
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