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499 results for “fuels”
Forest reorganisation effects on fuel moisture content can exceed changes due to climate warming in wet temperate forests
<p>48-year modelled dataset of below canopy fuel moisture content (FMC) for field sites established in wet temperate eucalypt forests in south-eastern Australia (allsites.csv). Raw hourly data for input to the model (hrlyallsitesgit.csv). </p>
Contrasting Activation Characteristics of Biomass Burning and Fossil Fuel Combustion Aerosols in Fogs and Clouds: Implications for Regional Air Quality and Climate
<p>The key 'jul' in data use 2021-01-01 as the referece day, for example, 2021-01-02 12:00:00 corresponding to jul of 2.5. </p>
Dataset for plots and results in manuscript: "Reliance on fossil fuels increases during extreme temperature events in the continental United States"
<p>These are the dataset for plots and results in the manuscript titled "Reliance on fossil fuels increases during extreme temperature events in the continental United States".</p><p>Figure 1,2,3,4,5 are the dataset used for analysis and plotting the figures in the manuscript.</p><p>Other dataset are the 34 years' air temperature and population-weighted air temperature thresholds for detecting extreme temperature events in each U.S. states. For example, Ta_threshold(1990-2023)_99th_percentiles.csv is the 99th percentiles for each U.S. states.</p><p>Any questions and further assitance or collaborations are welcome to contact through: wz2481@columbia.edu, happystillwaterzhao@gmail.com</p><p> </p><p> </p>
Replication Data for: Methane emissions decreased in fossil fuel exploitation and sustainably increased in microbial source sectors during 1990–2020
<p>Model and observation data, used to prepare the figures in the main text, are submitted at this repository.</p>
Multi-objective optimization of a renewable fuel supply chain regarding cost, land use, and water use - Supplementary Materials
<p><strong>This repository contains supporting data for: "Multi-objective optimization of a renewable fuel supply chain regarding cost, land use, and water use"</strong></p> <ol> <li> <p><strong>Input Data</strong>: This file includes the model input data.</p> </li> <li> <p><strong>Payoff Matrix Solution</strong>: This file contains all decision variables of payoff matrix solutions (Cost optimal, Land-use optimal, and Water-use optimal solution).</p> </li> <li><strong>Compromise solution</strong>: This file contains all decision variables of the compromise solution.</li> <li> <p><strong>Pareto Curve</strong>: This file presents the Pareto curve solution. </p> </li> <li><strong>Pareto Curve Solution</strong>: This file contains all decision variables in each of the Pareto curve solutions. </li> </ol>
Data for Quantifying functional group compositions of household fuel-burning emissions
<p>Data for reproduction of figures in the paper "Quantifying functional group compositions of household fuel-burning emissions"</p>
dataset figures - Assessing the Performance of Fuel Cell Electric Vehicles Using Synthetic Hydrogen Fuel - Article Energies
<p>Dataset for Table 1 - 2 and for Figure 4</p>
Lightning Declines Over Shipping Lanes Follow Regulation of Fuel Sulfur: Data Analysis
Open the record for dataset details and reuse information.
Results Data for "Price Formation Without Fuel Costs: The Interaction of Elastic Demand with Storage Bidding"
<h2>Abstract</h2> <p>Studies on electricity market design for high shares of wind and solar often predict the breakdown of energy-only markets, citing a lack of fuel costs to set prices. Issues include prolonged zero prices, politically unacceptable scarcity prices, price collapses from minor capacity changes, low market value cost recovery, revenue variability across different weather years, and challenges in long-term storage operation. These issues arise from modeling with perfectly inelastic demand. Introducing even a small amount of short-term elasticity (-5%) significantly mitigates these problems. A simplified model with wind, solar, batteries, and hydrogen storage shows that demand elasticity and storage opportunity costs stabilize pricing, smoothing the price duration curve, reducing zero-price hours from 90% to 30%, and ensuring price stability across capacities and weather years. Green hydrogen-derived fuels replace fossil fuels as backup. The long-term model matches the short-term model prices with identical capacities, guiding storage bidding strategies for short-term operations. A model trained on 35 years of weather data and tested on another 35 years demonstrates the energy-only market's potential in future dispatch and investment coordination.</p> <h2>Data Sources</h2> <p>- <strong>Solar and Wind Time Series (1950-2020):</strong> <a href="https://doi.org/10.17864/1947.000321">Bloomfield and Brayshaw (2021)</a><br>- <strong>Techno-Economic Assumptions:</strong><a href="https://github.com/PyPSA/technology-data/tree/v0.8.1"> technology-data (v0.8.1)</a>, <a href="https://ens.dk/en/our-services/technology-catalogues">Danish Energy Agency</a><br>- <strong>Demand Elasticity Assumptions:</strong> <a href="https://doi.org/10.1016/j.eneco.2024.107652">Hirth et al. (2024)</a>, <a href="https://www.ewi.uni-koeln.de/en/publications/on-the-functional-form-of-short-term-electricity-demand-response-insights-from-high-price-years-in-germany-2/">Arnold (2023)</a></p> <h2>Installation</h2> <p>Use <code>conda</code> environment manager:</p> <p><br><code>conda update conda</code><br><code>conda env create -f workflow/envs/environment.fixed.yaml</code><br><code>conda activate price-formation</code></p> <p><strong>Main Dependencies:</strong></p> <ul> <li>pypsa (v0.27.1)</li> <li>linopy (v0.3.8)</li> <li>snakemake (v8.5)</li> <li>gurobi (v11.0.2)</li> </ul> <h2>Run</h2> <p>From the root of the repository:</p> <p><br><code>snakemake -call --use-conda --conda-frontend conda</code></p> <p>Or with a specific scenario configuration file:</p> <p><br><code>snakemake -call --use-conda --conda-frontend conda --configfile config/config.foo.yaml</code></p> <h2>Cluster</h2> <p>On an HPC cluster, run:</p> <p><br><code>snakemake -call --profile slurm --use-conda --conda-frontend conda</code></p> <h2>Compress Results</h2> <p>Use <code>tar</code> to compress results (excluding the report directory):</p> <p><br><code>tar -cJf price-formation-results.tar.xz \</code><br><code> config data figures results resources workflow \</code><br><code> .gitignore .pre-commit-config.yaml .syncignore-receive \</code><br><code> .syncignore-receive CITATION.cff LICENSE matplotlibrc README.md</code></p> <h2>Licenses</h2> <p>The code in this repository is MIT licensed.</p> <p>The data in this repository is CC-BY-4.0 licensed.</p> <h2>Amendments</h2> <p>In <code>v0.2.0</code>, we added the file <code>revision-1-amendments.tar.xz</code>, which includes additional results for sensitivity runs with cross-elastic terms.</p>
Fuel tax loss in a world of electric mobility: A window of opportunity for congestion pricing
<p>The excel file "Data_Documentation" provides estimations of the passenger car stocks in Germany and the two states Berlin-Brandenburg until 2030 under a scenario of a dynamic diffusion of electric vehicles to the market. From the car stocks, the energy tax revenues are also estimated. The Tableau package book provides data and visualizations of congestion pricing simulation results. This is an updated version of its previous one. The corrections are only the names of the sheets in the files. The data itselft stays the same.</p>
Input files, plotting scripts, and figures for "Smoldering combustion in cellulose and hemicellulose mixtures: Examining the roles of density, fuel composition, oxygen concentration, and moisture content"
<p>This bundle of files contains all the <a href="http://reaxfire.com/trac/gpyro">Gpyro</a> input files, data, plotting scripts, and figures for "Smoldering combustion in cellulose and hemicellulose mixtures: Examining the roles of density, fuel composition, oxygen concentration, and moisture content". The README.txt file contains additional details about the contents.</p>
Benchmark Dataset (2D/3D) of an Industrial Rotary Kiln Combustion Chamber with Refuse-Derived Fuel Particles from a Light-Field-Camera
<p>Benchmark dataset for the detection of fuel particles (refuse-derived fuels - RDF) in 2D and 3D image data in a rotary kiln combustion chamber.</p> <p>Organization:<br> 01_Images: 50 Images (2D)<br> 02_Labels: Labeled ground truth image with rotary kiln, burner flame, burning particle in air, non-burning particle in air and particle on wall.<br> 03_Particle List: Lists of the coordinates of the center of gravity of particles in image coordinates.<br> 04_Point Cloud: 3D point cloud for the 50 images.<br> 05_Matlab: Code and visualization examples.<br> 06_All_Data: Images and 3D point cloud for 2010 images of a sequence containing the images with ground truth (see TXT file).</p>
Dataset for the paper "High Loading of Single Atomic Iron Sites in Pyrolysed Fe-NC Oxygen Reduction Catalysts for Proton Exchange Membrane Fuel Cells", DOI:10.1038/s41929-022-00772-9
<p>The data in this spreadsheet was used to produce the figures in the paper </p> <p>Authors: Asad Mehmood, Mengjun Gong, Frédéric Jaouen, Aaron Roy, Andrea Zitolo, Anastassiya Khan, Moulay-Tahar Sougrati, Mathias Primbs, Alex Martinez Bonastre, Dash Fongalland, Goran Drazic, Peter Strasser, Anthony Kucernak</p> <p>Title: High Loading of Single Atomic Iron Sites in Pyrolysed Fe-NC Oxygen Reduction Catalysts for Proton Exchange Membrane Fuel Cells</p> <p>Journal: Nature Materials</p> <p>DOI: 10.1038/s41929-022-00772-9</p> <p>Please cite the above reference if you wish to use this data </p> <p> </p> <p>DOI of data: 10.5281/zenodo.6411262</p>
Isotopic Fuel Compositions in the Sangamon200 Model
<p>The seven csv files each correspond to a different level of fuel burnup in the Sangamon200 (a 200 MWth pebble-bed HTGR) fuel pebbles. fresh_comp.csv provides the composition for UCO in fresh pebbles. 1pass_comp.csv provides the fuel composition in pebbles that have gone through one six month pass, 2pass_comp.csv have been through two six month passes, and so forth.</p>
Mapping of samples – Fuels from Reliable Bio-based Refinery Intermediates: BioMates, Schulzke et al., 2020, DOI:10.1007/s12649-019-00625-w
<table> <tbody> <tr> <td>In the H2020-project BioMates (www.biomates.eu, Grant Agreement No. 727463), Fraunhofer UMSICHT produced samples from ablative fast pyrolysis (AFP) of herbaceous biomass in a TRL 4-plant. A dedicated document provides identifiers for relevant liquid samples and their blends (DOI: 10.24406/fordatis/156). The document at hand maps it to the AFP-derived substances reported to be used in the article "T. Schulzke, S. Conrad, B. Shumeiko, M. Auersvald, D. Kubička, L. F. J. M. Raymakers; Fuels from Reliable Bio-based Refinery Intermediates: BioMates; Waste and Biomass Valorization (2020) 11:579–598; DOI:10.1007/s12649-019-00625-w", and provides further identifiers for samples not indexed earlier.</td> </tr> </tbody> </table>
Field data synthesis accompanying "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>Synthesis of fuel load and fuel consumption field measurements accompanying the publication:</p><p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p><p>Dave van Wees1, Guido R. van der Werf1, James T. Randerson2, Brendan M. Rogers3, Yang Chen2, Sander Veraverbeke1, Louis Giglio4, and Douglas C. Morton5</p><p>1Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br>2Department of Earth System Science, University of California, Irvine, CA 92697, USA<br>3Woodwell Climate Research Center, Falmouth, MA 02540, USA<br>4Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br>5Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p><p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p><p> </p><p>Units are g C / m2</p>
Adiabatic flame temperatures of common fuels in oxygen at constant pressure
<p><strong>Adiabatic flame temperatures of common fuels in oxygen at constant pressure</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0002-5022-6863, E-mail address: koncjj@gmail.com</p> <p> </p> <p>Combustion is a chemical reaction between substances, usually including oxygen and usually accompanied by the generation of heat and light in the form of flame. The rate or speed at which the reactants combine is high, in part because of the nature of the chemical reaction itself and in part because more energy is generated than can escape into the surrounding medium, with the result that the temperature of the reactants is raised to accelerate the reaction even more. Combustion encompasses a great variety of phenomena with wide application in industry, the sciences, professions, and the home, and the application is based on knowledge of physics, chemistry, and mechanics; their interrelationship becomes particularly evident in treating flame propagation. In general terms, combustion is one of the most important of chemical reactions and may be considered a culminating step in the oxidation of certain kinds of substances. Though oxidation was once considered to be simply the combination of oxygen with any compound or element, the meaning of the word has been expanded to include any reaction in which atoms lose electrons, thereby becoming oxidized. In addition to chemical reactions, physical processes that transfer mass and energy by diffusion or convection occur in gaseous combustion. In the absence of external forces, the rate of component diffusion depends upon the concentration of the constituents, pressure, and temperature changes, and on diffusion coefficients. The latter are either measured or calculated in terms of the kinetic theory of gases. The process of diffusion is of great importance in combustion reactions, in flames, that is, in gaseous mixtures, and in solids or liquids. Diffusion heat transfer follows a law stating that the heat flux is proportional to the temperature gradient. The coefficient of proportionality, called the thermal conductivity coefficient, is also measured or calculated in terms of the kinetic theory of gases, like the diffusion coefficient.</p> <p>Fuel, Oxidizer, Adiabatic flame temperature (degrees Celsius)</p> <p>Acetylene Oxygen 3480</p> <p>Cyanogen Oxygen 4525</p> <p>Dicyanoacetylene Oxygen 4990</p> <p>Methylacetylene Oxygen 2927</p> <p>Anthracite Oxygen 3500</p> <p>Aluminum Oxygen 3732</p> <p>Lithium Oxygen 2438</p> <p>Phosphorus Oxygen 2969</p> <p>Zirconium Oxygen 4005</p>
Adiabatic flame temperatures of common fuels in air at constant pressure
<p><strong>Adiabatic flame temperatures of common fuels in air</strong> <strong>at constant pressure</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0002-5022-6863, E-mail address: koncjj@gmail.com</p> <p> </p> <p>In the study of combustion, the adiabatic flame temperature is the temperature reached by a flame under ideal conditions. It is an upper bound of the temperature that is reached in actual processes. There are two types adiabatic flame temperature: constant volume and constant pressure, depending on how the process is completed. The constant volume adiabatic flame temperature is the temperature that results from a complete combustion process that occurs without any work, heat transfer or changes in kinetic or potential energy. Its temperature is higher than in the constant pressure process because no energy is utilized to change the volume of the system.</p> <p>Fuel, Oxidizer, Adiabatic flame temperature (degrees Celsius)</p> <p>Acetylene Air 2500</p> <p>Butane Air 4074</p> <p>Ethane Air 1955</p> <p>Ethanol Air 2082</p> <p>Gasoline Air 2138</p> <p>Hydrogen Air 2254</p> <p>Magnesium Air 1982</p> <p>Methane Air 1963</p> <p>Methanol Air 1949</p> <p>Naphtha Air 4591</p> <p>Natural gas Air 1960</p> <p>Pentane Air 1977</p> <p>Propane Air 1980</p> <p>Methylacetylene Air 2010</p> <p>Toluene Air 2071</p> <p>Kerosene Air 2093</p> <p>Bituminous Coal Air 2172</p> <p>Anthracite Air 2180</p>
Emission Scenarios used for: Methane emissions decreased in fossil fuel exploitation and sustainably increased in microbial source sectors during 1990–2020
<p>CH<sub>4</sub> emission scenarios, based on bottom-up emission estimates (Chandra et al., CEE, 2024; Fig 2) for simulating the long-term trends and latitudinal gradients of CH<sub>4</sub> and <em>δ</em><sup>13</sup>C-CH<sub>4</sub>. The details can be found at </p> <p>Chandra, N., Patra, P.K., Fujita, R. <em>et al.</em> Methane emissions decreased in fossil fuel exploitation and sustainably increased in microbial source sectors during 1990–2020. <em>Commun Earth Environ</em> <strong>5</strong>, 147 (2024). https://doi.org/10.1038/s43247-024-01286-x</p>
Prioritizing wildfire fuel management in California
<p><strong><span>The resources available for managing wildfire risk are insufficient and ultimately finite , while the risk of catastrophic fires is enormous and growing. Prioritization of responses is thus critical, but the basis for comparing the costs and societal benefits of alternative investments in wildfire mitigation is woefully inadequate. Here, we assess and compare the costs of landscape-scale fuel treatment to the benefits of avoided destruction of property and smoke-related health impacts, and identify areas where the net benefits are greatest statewide. We find that re-prioritizing treatment areas could increase net benefits by a factor of more than 7 relative to historical treatments, with average net benefits in the top decile of areas (i.e., 28,000 km<sup>2</sup>)<sup> </sup>of >$225k per km<sup>2 </sup>(as compared to an estimated $90k per km<sup>2</sup> of past treatments). By integrating physical, epidemiological, and economic methods, our results reveal large opportunities for improving the cost-effectiveness of wildfire fuel treatments, and demonstrate a general framework that can be applied by land managers in all wildfire-prone areas.</span></strong></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.