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2,762 results for “Heating”
Development and analysis of entropy stable no-slip wall boundary conditions for the Eulerian model for viscous and heat conducting compressible flows
<p>The database used in the submission of "Development and analysis of entropy stable no-slip wall boundary conditions implementation of the Eulerian model for viscous and heat conducting compressible flows."</p> <p>Abstract: Nonlinear entropy stability analysis is used to derive entropy stable no-slip wall boundary conditions for the Eulerian model proposed by Svärd ( <em>Physica A: Statistical Mechanics and its Applications, 2018 </em>). and its spatial discretization based on entropy stable collocated discontinuous Galerkin operators with the summation-by-parts property for unstructured grids. A set of viscous test cases of increasing complexity are simulated using both the Eulerian and the classic compressible Navier–Stokes models. The numerical results obtained with the two models are compared, and differences and similarities are then highlighted.</p>
Valuation of heat related mortality risk and tick-borne diseases
<p>Monetary impacts of premature mortality due to heat waves</p> <p>Preferences for public programmes against spread of ticks due to climate change and a new vaccine against Lyme disease, prevalence of tick-borne diseases and exposure to ticks</p>
Resarch data for common faults tested on a variable-speed propane-charged heat pump on heating mode
<p>Experimental data of common faults emulated on a 10 kW water-to-water variable-speed heat pump charged with propane. The faults emulated are evaporator fouling, compressor valve leakage, liquid line restriction and refrigerant overcharge. The faults are tested with 10 kW and 12 kW load demand.</p> <p>This data can be used to develop fault detection and diagnosis systems.</p>
MOM6-COBALTv2 model result for ecosystem response to marine heat waves
<p>0.5 × 0.5 resolution, Northeast Pacific ocean (184.8-120.2W, 28.06-64.97N). Monthly results from 1958-2019, all detrended with linear trend over full period removed. </p>
An Innovative Scheme to Confront the Trade‐Off Between Water Conservation and Heat Alleviation With Environmental Justice for Urban Sustainability: The Case of Phoenix, Arizona
<p><em><strong>The manuscript for this dataset is accepted by AGU Advances and can be accessed here: <a href="https://doi.org/10.1029/2022AV000816">link</a>. Please cite the literature when using the datasets.</strong></em></p> <p><strong>How to cite this article: Yuanhui Zhu, Soe Myint, Xin Feng, Yubin Li. An Innovative Scheme to Confront the Trade‐Off Between Water Conservation and Heat Alleviation With Environmental Justice for Urban Sustainability: The Case of Phoenix, Arizona. AGU Advances, 4, e2022AV000816. <a href="https://doi.org/10.1029/2022AV000816">https://doi.org/10.1029/2022AV000816</a></strong></p> <p>This study aims to develop a practical and integrated framework to tackle the tradeoff between land surface temperature (LST) reduction and water conservation for heat mitigation and resilience planning in Phoenix, Arizona. We developed a multi-objective framework of spatial optimization for priority areas that considers environmental justice. We employed the priority areas (i.e., residential districts, socio-economically disadvantaged neighborhoods, hotspot regions, and opportunity areas), ECOSTRESS-based LST, actual evapotranspiration (ETa, as a proxy to water use), Landsat-based LST and ETa changes (2000–2020), and the evaporative stress index (ESI). These datasets are used to identify the priority areas in which environmental conditions need to be improved seriously and (2) spatially optimize the placement of new green space (tree %, grass %) in the priority areas to realize the most significant LST reduction and minimum OWU. We provide the results of the new green space configurations with the scenarios for the percentage of new vegetation coverage (including trees and grass) overall increased to 25%, 35%, and 45% within the entire study areas, residential districts, socio-economically disadvantaged neighborhoods, and hotspot regions.</p> <table> <caption>The dataset summarization</caption> <tbody> <tr> <td>Category</td> <td>Dataset</td> <td>Resolution</td> <td>Source/method</td> <td>Time</td> </tr> <tr> <td>Environmental database</td> <td>Summer daytime LST</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer nighttime LST</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer ETa</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer ESI</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental change database</td> <td>Trends of summer LST changes</td> <td>30m</td> <td>Landsat-based Statistical Mono-Window algorithm</td> <td>2000-2020</td> </tr> <tr> <td>Environmental change database</td> <td>Trends of summer ETa changes</td> <td>30m</td> <td>Landsat-based Simplified Surface Energy Balance</td> <td>2000-2020</td> </tr> <tr> <td>The results of new green space configurations</td> <td>The spatial distributions of new green space</td> <td>--</td> <td>Spatial optimization</td> <td>--</td> </tr> </tbody> </table> <p>note: LULC: Land use and land cover; LST: Land Surface Temperature; ETa: Actual Evapotranspiration; ESI: Evaporative Stress Index</p> <p>We provide the different scenarios in shapefile format for spatial distributions of new space configurations. The naming convention for attribute tables in shapefile is :</p> <p>VV_new_perNN_LSTWW</p> <p>where:</p> <ul> <li>VV = New vegetation for tree or grass</li> <li>NN = The scenarios with new vegetation increased to 25%, 35%, or 45% (unit: %)</li> <li>WW = The weight values of land surface temperature range from 0 to 1 (unit: %) when executing spatial optimization for the tradeoff between land surface temperature reduction and outdoor water use conservation with vegetation coverage. The weight of 0 represents that our spatial optimization models only focus on outdoor water use conservation, and the weight of 1 denotes that we only consider land surface temperature reduction. </li> </ul> <p>Example: grass_new_per25_LST65 means -- new vegetation for grass; the scenario is set up by new vegetation increased to 25%; the weight of land surface temperature is 0.65. </p> <p> </p>
Global Ocean Heat Content Anomalies based on Argo data
<p><strong>NOTE for users: please use the latest version of the product at https://zenodo.org/doi/10.5281/zenodo.10182972. </strong>Ocean Heat Content Anomalies (OHCA) are calculated (during 2005-2022) subtracting the mean over the period 2005-2021 from the monthly time series. Yearly OHCA time series are then calculated. OHC fields are mapped using locally stationary Gaussian processes with data-driven decorrelation scales (Kuusela and Stein, 2018). A linear time trend was included in the estimate of the mean field (along with spatial terms and harmonics for the annual cycle). In the present version, mapping is done in latitude and longitude with monthly subsets of data (a future version will add time to the mapping). Mapping is done separately for different vertical sections. Different vertical sections are combined to estimate: 1. Global OHC timeseries (e.g., for level 0-2000m: GCOS_0000_2000_OHCA_J_m2_oc, for OHC in J/m2; GCOS_0000_2000_OHCA_ZJ, for OHC in ZJ; the attribute “GCOS_area” is included for both variable types in the netcdf file and it tells the corresponding surface area); 2. Volume averaged temperature anomaly (global) timeseries (e.g., for level 0-2000m: GCOS_0000_2000_vol_ave_temp_anom, in degC; the attribute “GCOS_volume'' is included for this variable type in the netcdf file and it tells the corresponding volume). Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included.</p>
Increased impact of heat domes on 2021-like heat extremes in North America under global warming
<p>The key codes and processed data for the paper.</p>
Research data for Refined heat pump design and results of final testing
<p>In this dataset, the data of the second experimental test campaign of the CO2-ice heat pump is shared. The report, which analyzes the data and makes the necessary explanations, has already been shared as a "Refined heat pump design and results of final testing (Deliverable: D5.6)". The report has already been published on ZENODO.</p>
Research data for Critical review of heat pump prototype operation and required modications
<p>In this dataset, the data of the first experimental test campaign of the CO2-ice heat pump is shared. The report, which analyzes the data and makes the necessary explanations, has already been shared as a "Critical review of heat pump prototype operation and required modifications (Deliverable: D5.5)".</p>
Electronic Supplement / Data Archive for "Global Variations in the Time Delays Between Polar Ionospheric Heating and the Neutral Density Response"
<p>These files provide supplemental data to accompany the paper "Global Variations in the Time Delays Between Polar Ionospheric Heating and the Neutral Density Response" submitted to AGU journal <em>Space Weather</em>, with manuscript number 2022SW003410. Details are provided in the file <strong>ReadMe_DataArchive.pdf</strong>.<br> </p>
Data and software: Heat flux for semi-local machine-learning potentials
<p><br> This repository contains data, code, and related artefacts supporting the following publication:</p> <p>"Heat flux for semi-local machine-learning potentials"<br> by Marcel F. Langer, Florian Knoop, Christian Carbogno, Matthias Scheffler, and Matthias Rupp<br> arXiv: TBD<br> doi: TBD<br> </p> <p>More details can be found in the main README.md file, and the README.md files in the subfolders.</p> <p><br> For any further questions, feel free to contact mail@marcel.science, @marceldotsci on Twitter, or @marcel@sigmoid.social.</p> <p> </p>
Heat Transfer Physics - Flat Plate Model
<p>The dataset includes unprocessed and processed temperature evolution plots for wide range of experimental conditions corresponding to ice crystal icing performed at the icing wind tunnel of TU Braunschweig within the scope of MUSIC-haic project. In addition to temperature plots the dataset also includes information on the design and components of the test article as well as the respective test matrix. It covers wide range of parametric variation including heat flux, wet bulb temperature, flow velocity and ice water content and provides a sound basis for calibration and validation of numerical tools.</p>
Dataset of "Genetically-inspired convective heat transfer enhancement in a turbulent boundary layer"
<p>Dataset of the article "Genetically-inspired convective heat transfer enhancement in a turbulent boundary layer" (<a href="https://doi.org/10.1016/j.applthermaleng.2023.120621">https://doi.org/10.1016/j.applthermaleng.2023.120621</a>). The dataset contains:</p> <p>- the velocity fields, measured with Particle Image Velocimetry, for the case of the boundary layer without actuation, with actuation with a steady jet, and for the best individual obtained after the optimization of the pulsed jet parameters.</p> <p>- the parameters of the individuals generated in the optimization process.</p>
Data for: Heat induces multiomic and phenotypic stress propagation in zebrafish embryos
<p>This contains the data for the manuscript Feugere et al., "Heat induces multiomic and phenotypic stress propagation in zebrafish embryos" (2023). Zebrafish embryos were exposed to thermal stress ("TS") and stress metabolites ("SM") released by heat-stressed conspecifics in a two-way factorial design ("TSxSM"). The folder includes raw molecular data (cortisol levels, HSP70 protein levels, and gene expression acquired with LAMP and RNA-seq) and raw phenotypic data (morphology, hatching, survival, and behaviour) of zebrafish <em>Danio rerio </em>at 1 day and 4 days of development.</p> <p>The .csv files contain all quantitative data, whilst the .tab files contain the gene count data required for gene expression analysis. The data were analysed in R using the code shared in the "TSxSM2.stats.Rmd" file. The "Metadata" document provides the reader with an extensive description of each file.</p>
Energetic costs increase with faster heating in an aquatic ectotherm: Respirometry data
<p>Using closed chamber respirometry we estimated the aerobic metabolic rate of an aquatic ectotherm, the Atlantic ditch shrimp <em>Palaemonetes varians,</em> under varying thermal conditions. We continuously measured oxygen consumption of shrimp during heating, cooling, and constant temperatures, starting trials at a range of acclimation temperatures and exposing shrimp to a variety of rates of temperature change.</p> <p>Data corrections and associated methodologies are detailed fully in the accompanying manuscript. </p> <p>## Description of the data and file structure</p> <p>Stable temperature trial data found in stable_trials_raw.csv</p> <p>Ramping temperature trial data found in ramping_trials_raw.csv</p> <p>Both datasets include the respirometry data for all temperature trials, with dissolved oxygen concentration corrected for background respiration ("doconc_corr") following the methodology outlined in the associated manuscript. Identification number of the individual is stored under "ID", time from start of trial to end of trial (in minutes) is stored under "time_min" and temperature of the chamber (in degrees Celcius) is stored under "temp_degC".</p>
District Heating Study Results (TIMES Ireland Model) 2023
<p>This is the set of District Heating Results used in a study. The results are available to view on a web-based platform also: https://epmg.netlify.app/Jason_DH/results/ </p> <p>The results are in CSV format. </p>
Network Data of the District Heating System for the city of Sønderborg from 2016-2019
<p>The data set contains measurement data for heat load, as well as feed and return flow temperatures, from seven plants for the years 2016-2019 with a 15-minute time resolution. The heating plants belong to the district heating systems of Sønderborg, Denmark.</p>
Supplementary material for the article "High-resolution projections of ambient heat for major European cities using different heat metrics"
<p>This dataset contains the data displayed in the figures or the article "High-resolution projections of ambient heat for major European cities using different heat metrics".</p> <p>The different files contain:</p> <ul> <li>Data_Fig1_DeltaTXx_EURO-CORDEX_1981-2010_to_3K-European-warming_RCP85.nc:<br> Change of yearly maximum temperature in Europe between 1981-2010 and 3 °C European warming relative to 1981-2010.</li> <li>Data_Fig2_timeseries-GSAT-ESAT_EURO-CORDEX_CMIP5_CMIP6_1971-2100_RCP85_SSP585.xlsx:<br> Time series of global mean surface air temperature (GSAT) for CMIP5 and CMIP6 models, and for European mean surface air temperature (ESAT) for EURO-CORDEX, CMIP5, and CMIP6 models for the period 1971-2100.</li> <li>Data_Fig3_TX-distribution_distance-from-city-centre_E-OBS_1981-2010.xlsx:<br> Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for E-OBS for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.</li> <li>Data_Fig3_TX-distribution_distance-from-city-centre_ERA5-Land_1981-2010.xlsx:<br> Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for ERA5-Land for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.</li> <li>Data_Fig3_TX-distribution_distance-from-city-centre_EURO-CORDEX_1981-2010.xlsx:<br> Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for the EURO-CORDEX models for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.</li> <li>Data_Fig3_TX-distribution_distance-from-city-centre_weather-stations_1981-2010.xlsx:<br> Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for GSOD and ECA&D stations for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.</li> <li>Data_Fig4_TX-ambient-heat_EURO-CORDEX_3K-European-warming.xlsx:<br> Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for EURO-CORDEX models.</li> <li>Data_Fig5_Contribution-of-explanatory-variables-to-total-explained-variance.xlsx:<br> Contribution of different explanatory variables (climate and location factors) to the total explained variance of spatial patterns of heat metrics.</li> <li>Data_Fig6_TN-ambient-heat_EURO-CORDEX_3K-European-warming.xlsx:<br> Nighttime heat metrics for the investigated cities: HWMId-TN at 3 °C European warming relative to 1981-2010, TN exceedances above 20 °C at 3 °C European warming relative to 1981-2010, and TNx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for EURO-CORDEX models.</li> <li>Data_Fig7_TX-ambient-heat_CMIP5_3K-European-warming.xlsx:<br> Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for CMIP5 models.</li> <li>Data_Fig7_TX-ambient-heat_CMIP6_3K-European-warming.xlsx:<br> Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for CMIP6 models.</li> <li>Data_Fig8_GCM-RCM-matrix_ambient-heat_3K-European-warming.xlsx:<br> GCM-RCM matrices for the three heat metrics.</li> </ul>
Bischof et al. 2023 - The role of the North Atlantic for heat wave characteristics in Europe, an ECHAM6 study
<p><strong>Data to reproduce the figures in Bischof et al. 2023: "The role of the North Atlantic for heat wave characteristics in Europe, an ECHAM6 study" submitted to GRL in July 2023. </strong></p> <p>Modelling experiments are based on the FOCI model using ECHAM6 in an AMIP-like setup to carry out time slice experiments using 2018 background conditions in the atmosphere and on land (both following the SSP5-8.5 scenario) as well as in the ocean (daily forcing data based on ERA5). A sensitivity experiment without the cold SST anomaly in the subpolar North Atlantic that was observed in 2018 is also carried out.</p> <p><strong>COLD-SH009</strong> refers to the 2018 experiment using 2018 SSTs as observed, <strong>NEUTRAL-RP006</strong> refers to the experiment with altered SSTs in the North Atlantic region only. Details on the method and model setup can be found in the associated publication. </p>
Functional potential and evolutionary response to long-term heat selection of bacterial associates of coral photosymbionts
<p>Sequencing reads were assembled using the genome assembler pipeline Shovill v1.1.0. Briefly, the Shovill pipeline included read trimming using Trimmomatic v0.39, de novo assembly with SPAdes v3.15.5 and genome polishing with Pilon v1.24. After the pipeline, additional polishing was performed by mapping the reads back to the contigs with BWA v0.7.17 and sorting the resulting SAM/BAM files using SAMtools v1.15.1. Pilon v1.24 was then used to correct bases, fix mis-assemblies and fill gaps. The reformat.sh script from the Bbmap package v38.76 (-minlength=1000) was used to filter out contigs less than 1000bp. The draft genome assemblies were then annotated with Bakta v1.7.0. </p> <p>Single nucleotide polymorphism (SNP) detection between WT (WTref) and SS (SSref) samples were then performed using snippy v4.6.0, where both WT and SS samples were inputted as the reference genome in turn.</p> <p>A subset of the snippy output files are uploaded here and contain all variants found.</p>
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