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499 results for “fuels”
Fuel loads monitoring dataset of Campos Amazônicos Fire Experiment (Amazonas, Brazil)
<p>This dataset presents fuel loads monitoring data related to the Campos Amazônicos Fire Experiment (CAFE) (Amazonas, Brazil) project. Located within a protected area within the largest enclave of tropical savanna in the Southern Amazon, CAFE comprises a careful and systematic experimental design that was conceived from the start to evaluate satellite observations and their potential and limitations in studying spatial and temporal fire dynamics in tropical savannas.</p> <p>More information about the experimental design can be found in the following publication:</p> <p>Alves, D. B.; Fidelis, A.; Pérez-Cabello, F.; Alvarado, S. T.; Conciani, D. E.; Cambraia, B. C.; Silveira, A. L. P.; Silva, T. S. F. Impact of Image Acquisition Lag-Time on Monitoring Short-Term Postfire Spectral Dynamics in Tropical Savannas: the Campos Amazônicos Fire Experiment. Journal of Applied Remote Sensing v. 16, n. 3 (2022) - <a href="https://doi.org/10.1117/1.JRS.16.034507">https://doi.org/10.1117/1.JRS.16.034507</a></p> <p>_____________________________________________________________________________</p> <p>August 08, 2022 - Version 1.0 includes data from 30 monitored experimental plots of 1 hectare each (100×100 m). Three experimental treatments were then established: 12 plots were burned in May (Early-Dry Season – EDS), further 12 plots were burned in August (Mid-Dry Season – MDS), and 6 plots were kept as control by ensuring fire exclusion throughout the duration of the experiment. Controlled burning occurred during two separate field campaigns in 2019, the first between May 19th and 25th, and the second between August 22nd and 26th.</p> <p>Measurements of fuel load were obtained from eight subplots of 0.5 × 0.5 m randomly distributed within each plot. Samples included graminoids, leaves and branches near the ground. Biomass was dried at 70°C for 48 hours, and weighed to determine total fuel load (kg. m<sup>-2</sup>) for each sample. Samples were taken before fire for all control, EDS and MDS burn plots during both field campaigns, and repeated sampling was carried out after fire for the burned plots. In 2020, all 30 monitored plots were sampled again in May and August.</p> <p>The files available include: i) a table (Fuel_load_measures.csv) that contains the measures of fuel loads for each plot; ii) a text file (List_of_variables.rtf), which details each variable available in the table.</p> <p>_____________________________________________________________________________</p> <p>We thank the management team of the Campos Amazônicos National Park, and in particular to its Fire Brigade (squad leaders José Furtado Neto, Genaldo Júnior, Ademilton Carvalho, Simei Limoeiro, José Alexandre Medeiros, Antonio Machado and Leandro Lacerda, and on behalf of them to all other members of the brigade), who ensure safe burning of all fire experiments (SISBIO license number 67210-5). This work was supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, grant numbers 2019/07357-8; 2015/06743-0); the Conselho Nacional de Pesquisa e Desenvolvimento (CNPq, grant numbers 154660/2018-3; 441968/2018-0; 303988/2018-5).</p>
Data_Torrefaction of pulp industry sludge to enhance its fuel characteristics
<p>Recently, under COP26, several countries agreed to phase-out coal from their energy systems. Torrefaction industry can take advantage of this, as the fuel characteristics of the torrefied biomass are comparable to those of coal. However, in terms of economic feasibility, torrefied biomass pellets are not yet competitive with coal without subsidies because of the high price of woody biomass. Thus, there is a need to produce torrefied pellets from low-cost feedstock, and pulp industry sludge is one such feedstock. In that context, this study was focused on torrefaction of pulp industry sludge. Torrefaction experiments were carried out using a continuous reactor, at temperatures 250, 275, and 300 ℃. The heating value of the sludge increased from 19 to 22 MJ/kg after torrefaction at 300 ℃. The fixed carbon content increased from 16 wt.% for dried pulp sludge to 30 wt.% for torrefied pulp sludge. The fuel ratio was in the range of 0.27 to 0.61. The ash content of the pulp sludge is comparable to the agricultural waste, which is around 12 wt.% (dry basis). The cellulose content in the sludge reduced from 35 to 12 wt.% at 300 ℃. The ash related issues such as slagging, fouling and bed agglomeration tendency of the sludge were in the medium range. This study shows that torrefaction treatment can improve the fuel properties of the pulp industry sludge to a level comparable to that of low-rank coal.</p>
Dataset for the publication: Molecularly-controlled high swirl combustion system for ethanol/1-octanol dual fuel combustion
<p>This dataset contains the research data featured in the publication "Molecularly-controlled high swirl combustion system for ethanol/1-octanol dual fuel combustion" in Fuel (DOI: 10.1016/j.fuel.2023.128184)</p>
Minimal data set for: Air-liquid interface exposure of A549 human lung cells to characterize the hazard potential of a gaseous bio-hybrid fuel blend
<p>This minimal data set presents the values behind the means and standard deviation for the publication entitled: "Air-liquid interface exposure of A549 human lung cells to characterize the hazard potential of a gaseous bio-hybrid fuel blend"</p>
Dynamic sparse X-ray nanotomography reveals ionomer hydration mechanism in polymer electrolyte fuel-cell catalyst: Raw data and reconstruction software
<pre>Dynamic sparse X-ray nanotomography reveals ionomer hydration mechanism in polymer electrolyte fuel-cell catalyst: Raw data and reconstruction software Dataset structure: <strong>- Dynamic_PEFC_data.h5</strong> # Raw projection data for dynamic tomography imaging of PEFC catalyst hydration. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /humidity_readout # Relative humidity value at the time each projection is measured, 1D array with axis (Nangle). - /Deform_X # X/Y/Z components for the deformation vector field which characterize nonrigid deformation of the sample. - /Deform_Y - /Deform_Z <strong>- liquid_simulation.h5</strong> # Numerical simulation of dynamic liquid filling process. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /groundtruth_tomograms # Ground truth of the simulated tomograms, 4-dimensional array with axes (Timeframe,Y axis, Z axis, X axis). <strong>- phasetran_simulation.h5</strong> # Numerical simulation of gradual linear density change process. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /groundtruth_tomograms # Ground truth of the simulated tomograms, 4-dimensional array with axes (Timeframe,Y axis, Z axis, X axis). Reconstruction codes: <strong>- astra_nonrigid.zip</strong> # Compressed python package of modified version of astra-toolbox with nonrigid computed tomography implementation. - /astra # Python package folder, need to be added to Python import search path (sys.path). # If the pre-compiled version doesn't work, source code of the pacakge can be downloaded: # https://github.com/zr-gao/astra-toolbox-nonrigid # Follow the instructions and requirements on the website to compile and install the package. <strong>- reconstruction_PEFC.py</strong> # Python script for sparse dynamic tomography of the PEFC dataset. # Need to be in the same folder with Dynamic_PEFC_data.h5 to load data. # Follow the instructions in the code to set reconstruction parameters and export results. # Requirements: cupy, numpy, astra(with nonrigid)*, h5py # * <strong>!!!</strong> Nonrigid computed tomography is used for the reconstruction, therefore the astra package with nonrigid implementation (in astra_nonrigid.zip) is required. <strong>- reconstruction_simulation.py</strong> # Python script for sparse dynamic tomography of numerical simulations. # Need to be in the same folder with liquid_simulation.h5 or phasetran_simulation.h5, loaded filename is selected in the code. # Follow the instructions in the code to set reconstruction parameters and export results. # Requirements: cupy, numpy, astra**, h5py # ** Reconstruction of numerical simulations does not use nonrigid computed tomography, therefore both the astra_nonrigid.zip and the official astra-toolbox package will work. # To download and install the official astra-toolbox refer to the repository: # https://github.com/astra-toolbox/astra-toolbox</pre>
DATASET : Thresholds of fire response to moisture and fuel load differ between tropical savannas and grasslands across continents
<p><strong>Abstract </strong></p> <p><strong>Aim:</strong> An emerging framework for tropical ecosystems states that fire activity is either ‘<em>fuel build-up limited</em>’ or ‘<em>fuel moisture limited</em>’ i.e. as you move up along rainfall gradients, the major control on fire occurrence switches from being the amount of fuel, to the moisture content of the fuel. Here we used remotely sensed datasets to assess whether interannual variability of burned area is better explained by annual rainfall totals driving fuel build-up, or by dry season rainfall driving fuel moisture.</p> <p><strong>Location:</strong> Pantropical savannas and grasslands</p> <p><strong>Time period:</strong> 2002-2016</p> <p><strong>Methods:</strong> We explored the response of annual burned area to interannual variability in rainfall. We compared several linear models to understand how <em>fuel moisture </em>and <em>fuel build-up effect </em>(accumulated rainfall during 6 and 24 months prior to the end of the burning season respectively) determine the interannual variability of burned area and explore if tree cover, dry season duration and human activity modified these relationships.</p> <p><strong>Results:</strong> Fuel and moisture controls on fire occurrence in tropical savannas varied across continents. Only 24% of South American savannas were <em>fuel build-up limited</em> against 61% of Australian savannas and 47% of African savannas. On average, South America switched from fuel limited to moisture limited at 500 mm yr<sup>-1</sup>, Africa at 800 mm yr<sup>-1</sup> and Australia at 1000 mm yr<sup>-1 </sup>of mean annual rainfall.</p> <p><strong>Main conclusions:</strong> In 42% of tropical savannas (accounting for 41% of current area burned) increased drought and higher temperatures will not increase fire, but there are savannas, particularly in South America, that are likely to become more flammable with increasing temperatures. These findings highlight that we cannot transfer knowledge of fire responses to global change across ecosystems/regions – local solutions to local fire management issues are required, and different tropical savanna regions may show contrasting responses to the same drivers of global change.</p>
JOINT WEBINAR: SUSTAFUELS, Three European Solutions Working on Algal & Renewable Fuels
<p>On May 21, 2024, an informative webinar titled “SUSTAFUELS, Three European Solutions Working on Algal & Renewable Fuels” was held from 12:00 to 13:00 CET. This online event was a collaborative effort among three key projects—ALFAFUELS, COCPIT, and FUELGAE—aimed at advancing renewable fuel technologies. Attendees were introduced to the main concepts, ambitions, and methodologies behind these innovative European initiatives. The event was structured in six parts, including presentations on non-biological algal renewable fuels, detailed discussions on each project, and a Q&A session.</p> <p>The webinar was moderated by Pablo Morales Moya from Sustainable Innovations (SIE), and featured a presentation from Javier Sánchez López of CINEA, who discussed the agency’s role in supporting climate, infrastructure, and environmental initiatives. Following the introductory segments, the spotlight shifted to the project coordinators. Charis Xiros from RISE Research Institutes of Sweden presented the ALFAFUELS project, Sary Awad from IMT Atlantique showcased the COCPIT project, and Silvia Morales de la Rosa from CSIC presented the FUELGAE project. Each coordinator provided insights into their project’s objectives, impacts, and collaborative efforts.</p> <p>Participants had the opportunity to learn about groundbreaking renewable fuel solutions and their potential for carbon capture. The event underscored the importance of European collaboration in tackling environmental challenges through innovative research and development. Recordings of the session will be used for dissemination purposes, ensuring that the knowledge shared continues to benefit a wider audience interested in sustainable fuel technologies.</p>
Research Data/Code for "Scale-bridging within a complex model hierarchy for investigation of a metal-fueled circular energy economy by use of Bayesian model calibration with model error quantification"
<p>This repository contains research data and code for supplementing the manuscript <br>"Scale-bridging within a complex model hierarchy for investigation of a metal-fueled circular energy economy by use of Bayesian model calibration with model error quantification" <br>by L. Gossel, E. Corbean, S. Dübal, P. Brand, M. Fricke, H. Nicolai, C. Hasse, S. Hartl, S. Ulbrich, and D. Bothe. </p> <p>There is a corresponding preprint available on Arxiv: https://doi.org/10.48550/arXiv.2404.13092</p> <p><br>Users are referred to the manuscript for background information. This repository shall enable reproduction of the reported results and does not stand alone. </p> <p>Please read important information in the README in the top-level directory. </p> <p>Funded by the Hessian Ministry of Higher Education, Research, Science and the Arts - cluster project Clean Circles. </p>
Dataset related to the Journal Article 'A deep learning method for the prediction of ship fuel consumption in real operational conditions'
<p>This dataset contains the data used to plot the graphs and create tables corresponding to the figure/table number in the published version of the paper.<br>Paper DOI:https://doi.org/10.1016/j.engappai.2023.107425</p> <p> </p>
The monthly operating costs (vehicle, fuel, and maintenance) of each compared vehicle, Tesla 3 (283 HP), and Infiniti Q50 (300 HP).
<p>We compared two vehicles with similar horsepower, Tesla 3 (283 HP), and Infiniti Q50 (300 HP). The monthly operating costs of each compared vehicle were:</p> <ul> <li> <p>for the model, Tesla 3 electric vehicle was US$426.10/month, including purchase and depreciation US$333/month, fuel (electricity) US$27.8/month, maintenance US$65.3/month.</p> </li> <li> <p>for the model, Infiniti Q50, the internal combustion engine car was US$583.7/month, including purchase and depreciation US$321/month, fuel US$166/month, maintenance US$96.7/month.</p> </li> </ul>
Spain fuel prices
<p>This dataset provides insight into:</p> <ul> <li>All fuel types available at each petrol station</li> <li>Covers all petrol stations of Spain</li> <li>Informs fuel price evolution along wk46, 2022</li> </ul>
Data for "Detection of large-scale cloud microphysical changes within a major shipping corridor after implementation of the IMO 2020 fuel sulfur regulations"
<p>Processed data used for the manuscript "Detection of large-scale cloud microphysical changes within a major shipping corridor after implementation of the IMO 2020 fuel sulfur regulations".</p> <p>Includes input data for kriging algorithm as "SSF1deg_shipkrige_Terra.nc" and output data files as "Data_Terra_[VAR]_[YEAR]_C_M[MONTH].nc" for [VAR] Acld (overcast albedo) or cer (cloud droplet effective radius), [YEAR] the starting year of a three-year period starting with 2002 and ending at 2020 or "clim" for the 2002-2019 climatology, and [MONTH] 1to12 (annual mean) or 9to11 (austral spring).</p> <p>For the output data, "Obs" is the original data, "Est" is the mean counterfactual field obtained via kriging, "lowEst" and "highEst" are the 2.5th and 97.5th percentiles of the kriged fields for each grid box, "krSims" stores the results of the 5,000 simulated kriged fields, "Semivariance" is the binned empirical variogram values, "pVal" is the raw field significance (not adjusted for multiple testing), "nOut" is the number of individually significant grid boxes, "tran" is the transform applied (none for cer, logit for Acld), "iniPhi" and "iniSigma2" are the initial values for the fitted variogram, "Phi" and "Sigma2" are the fitted values using weighted least squares, and "parSel" is the list of selected regressors for the mean function that minimize the Bayesian information criterion.</p>
Result data related to "Tröndle et al (2023): Public preferences for phasing-out fossil fuels in the German building and transport sectors"
<p>Parameter estimations from the conjoint experiments performed in "Tröndle et al (2023): Public preferences for phasing-out fossil fuels in the German building and transport sectors". Parameter estimations are given for different:</p> <p>* estimands: average marginal component effects (amce) or marginal means,</p> <p>* variables: choice and rating,</p> <p>* sectors: buildings (heat) and transport sector,</p> <p>* subgroups: by-<subgroupname>.</p> <p>Filenames accordingly are: <estimand>-<variable>-<sector>.csv or <estimand>-<variable>-<sector>-by-<subgroup>.csv</p>
South Portugal Live Fuel Moisture Content (LFMC) dataset
<p>This dataset contains a collection of biweekly LFMC field samples collected between April 2022 and July 2023 over the Alentejo region, South Portugal.<br><br>Metadata:</p> <p>Coordinate Reference System: ETRS_1989_Portugal_TM06 - EPSG 3763<br>File Format: ESRI shapefile<br>Column Fields:</p> <ul> <li>FID - Internal ID</li> <li>Ponto - control point for backup purposes.</li> <li>LFMC - Live fuel moisture content (LFMC) in percentage.</li> <li>DATE - Sample date. AREA - in-situ field name.</li> <li>POINT_X - Longitude.</li> <li>POINT_Y - Latitude.</li> <li>POINT_Z - Altitude.</li> </ul> <p>Fundings:</p> <p>Filippe Santos was supported by the Portuguese Foundation for Science and Technology, I.P (Grant 2022.11960.BD).<br>This research was funded by national funds through FCT-Foundation for Science and Technology, I.P. under the PyroC.pt project (Refs. PCIF/MPG/0175/2019), ICT project (Refs. UIDB/04683/2020 and UIDP/04683/2020).</p>
Young forests and fire: Using lidar-imagery fusion to explore fuels and burn severity in a subalpine forest reburn, Grand Teton National Park, Wyoming.
Anticipating fire behavior as climate change and fire activity accelerate is an increasingly pressing management challenge in fire-prone landscapes. In subalpine forests adapted to infrequent, stand-replacing fire, self-limitation of burn severity in short-interval fire is incompletely understood. Spatially explicit fuels data can support assessments of landscape-scale fire risk and fuels feedbacks on burn severity. For a about 1,450 km2 largely forested landscape in the US Northern Rocky Mountains, we used airborne lidar and imagery to predict and map canopy and surface fuels. In a fire that burned mature ( greater than 125-year-old) and also reburned young (~30-year-old) subalpine forest, we then asked: (1) How do pre-fire fuels and burn severity compare between young and mature forests that burned under similar fire weather conditions? (2) How well do pre-fire fuels and forest structure predict burn severity under extreme versus moderate fire weather? Lidar-imagery fusion predicted fuel characteristics with high accuracy across forest and shrubland vegetation (R2 from 0.65-0.95). Young post-fire forests had abundant, densely packed canopy fuels, and both young and mature forests had similar canopy fuel loads and coarse wood biomass. Under similar weather conditions, young and mature forests burned at similar severity. Overall, fuels were weak predictors of burn severity and, surprisingly, better predicted severity under extreme (R2LMM(m) = 0.27) rather than moderate (R2LMM(m) = 0.15) fire weather. Our findings are relevant for subalpine landscapes increasingly dominated by young lodgepole pine (Pinus contorta var. latifolia) forests vulnerable to short-interval fire and provide a benchmark to assess how fuels influence burn severity in future fires. Fire managers should continually reassess fuels and update expectations about fire behavior as landscapes change. Although recovering post-fire forests can limit fire spread and severity for a period of time, our resu
Less fuel for the next fire? Short-interval fire delays forest recovery and interacting drivers amplify effects, Greater Yellowstone Ecosystem, Montana and Wyoming, USA
As 21st-century climate and disturbance dynamics depart from historical baselines, ecosystem resilience is uncertain. Multiple drivers are changing simultaneously, and interactions among drivers could amplify ecosystem vulnerability to change. We explored how interacting drivers affected post-fire recovery of subalpine forests, which Subalpine forests in Greater Yellowstone (Northern Rocky Mountains, USA) were historically resilient to infrequent (100-300 year), severe fire., in Greater Yellowstone (Northern Rocky Mountains, USA). We sampled paired short- (< 30 year) and long- (> 125 year) interval post-fire plots most recently last burned between 1988 and 2018 to address two questions: (1) How do short-interval fire, climate, topography, and distance to unburned live forest edge and other factors (topography, distance to live edge) interact to affect post-fire forest recoveryregeneration? (2) How do forest biomass and fuels vary following short- versus long-interval severe fires? Mean post-fire live stem density was an order of magnitude lower following short- versus long-interval fires (3,240 versus 28,741 stems ha-1, respectively). Differences between paired plots increased with greater climate water deficit normal (ρ = 0.67) and were amplified at longer distances to live forest edge. Surprisingly, warmer-drier climate was associated with higher seedling densities even after short-interval fire, likely relating to regional variation in serotiny of lodgepole pine (Pinus contorta var. latifolia). Unlike conifers, density of aspen (Populus tremuloides), a deciduous resprouter, increased with short- versus long-interval fire (mean 384 versus 62 stems ha-1, respectively). Live biomass and canopy fuels remained low nearly 30 years after short-interval fire, in contrast to rapid recovery after long-interval fire, suggesting that future burn severity may be reduced for several decades following reburns. Short-interval plots also had half as much dead woody biomass compar
Measurement of Fuel load (organic biomass) Quantities of Organic Soils, Understory Plants and Trees at Sites within the Bonanza Creek LTER Regional Site Network in Interior Alaska, 2019
This dataset contains fuel load measurements for a subset of the BNZ LTER's RSN sites (n = 28). The data for each site separted by 16 fuel types (or plant functional types) including organic soil layers (fibric, mesic), vascular understory (evergreen shrub, short deciduous shrub, graminoid, forb, tall deciduous shrub, tree seedling/sapling, dead-downed wood), nonvascular understory (feather moss, Sphagnum moss, colonizer moss, lichen) and trees (evergreen tree, deciduous tree).
Teaser Promoting solar fuels at COP25!
<p>Promotional video: We are happy to announce that SUNRISE will have a booth on December 5, 6 and 7 at COP25 in Madrid! The stand - number 2 - will be placed at the Green Zone of IFEMA, within the Science & Innovation area. Come & visit us!</p>
Hélène Lepaumier - From Fossil to Renewable Fuels
<p>What is the link between neutral climate and renewable fuels? Are all clean fuels equally beneficial? Find out more about solar fuels & don't miss our interview with Hélène Lepaumier, research engineer at ENGIE Laborelec and a SUNRISE consortium member</p>
Effects of plant hydraulic traits on the flammability of live fine canopy fuels in 62 Australian plant species
<ol> <li><span>Plant species vary in how they regulate moisture and this has implications for their flammability during wildfires. We explored how fuel moisture is shaped by variation within six hydraulic traits: saturated moisture content, cell wall rigidity, cell solute potential, symplastic water fraction and tissue capacitance.</span></li> <li><span>Using pressure-volume curves, we measured these hydraulic traits distal shoots (<i>i.e.</i> twigs + leaves) in 62 plant species across four wooded communities in south-eastern Australia. For a subset of 30 of those species, we also measured hydraulic traits of twigs using moisture-release curves. Moisture content of fine fuels was then estimated for circumstances typical of fire weather. These projections were made assuming that under the hot, dry, windy conditions typical of large wildfires, leaves and fine twigs would function at internal water pressures close to wilting point (<i>i.e. </i>turgor loss point, TLP). The effect of different moisture contents at TLP on ignition time was then modelled using a fully mechanistic, finite element model of biomass ignition based on standard principles of physical chemistry.</span></li> <li><span>We also measured predawn water potential, an indication of plant access to soil water that is influenced by root architecture. These data were used to model how root traits influence fuel moisture and ignition time.</span></li> </ol>
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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.