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9 results for “fuel consumption”
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>
Fuel Consumption Patterns linked to Evolution of India's PM2.5 Pollution Between 1998 and 2020
<p>These datasets are part of Supplementary information for the journal article<br> "<a href="https://doi.org/10.1039/D2EA00027J">Evolution of India’s PM2.5 Pollution Between 1998 and 2020 Using Global Reanalysis Fields Coupled with Satellite Observations and Fuel Consumption Patterns</a>"</p> <p>Pollution data summaries are listed <a href="https://doi.org/10.5281/zenodo.7115052">here</a>.<br> <a href="https://doi.org/10.5281/zenodo.7115052">https://doi.org/10.5281/zenodo.7115052</a></p> <p>Fuel consumption and activity data over the years is collected from indiastats.<br> Excel files include absolute activity data.<br> Figures are showing the ratio to year 2000 for each of the categories<br> Individual figures are included in the power point for reference and use.<br> Individual figures are also included in the respective excel files</p> <p>Files listed below</p> <ol> <li>coal.consumption.total.xlsx (total coal)</li> <li>electricity.consumption.xlsx (industry, domestic, railways, agriculture, commercial, and total)</li> <li>fuel.consumption.xlsx (LPG, petrol, diesel, kerosene, ATF, others, and petcoke)</li> <li>pass.freight.movement.xlsx (freight as tonne/km and passenger as pass/km)</li> <li>vehicle.registrations.xlsx (2-wheelers+4-wheelers, buses, freight, and all vehicles)</li> </ol>
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>
Impact of Ambient Temperature on Light-duty Gasoline Vehicle Fuel Consumption under Real-World Driving Conditions
<p>This dataset includes the vehicle operating (speed, acceleration), fuel consumption rate, and weather condition (temperature, pressure, humidity, heat index) data of Beijing, China.</p> <p>See our published paper for more information: </p> <div> <div>Fan, P., Song, G., Lu, H., Yin, H., Zhai, Z., Wu, Y., Yu, L., 2024. Impact of ambient temperature on light-duty gasoline vehicle fuel consumption under real-world driving conditions. International Journal of Sustainable Transportation 1–16. <a href="https://doi.org/10.1080/15568318.2024.2385635">https://doi.org/10.1080/15568318.2024.2385635</a></div> </div>
Model data for "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>500 m fire carbon emissions and burned area as part of 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 Wees<sup>1</sup>, Guido R. van der Werf<sup>1</sup>, James T. Randerson<sup>2</sup>, Brendan M. Rogers<sup>3</sup>, Yang Chen<sup>2</sup>, Sander Veraverbeke<sup>1</sup>, Louis Giglio<sup>4</sup>, and Douglas C. Morton<sup>5</sup></p> <p><sup>1</sup>Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br><sup>2</sup>Department of Earth System Science, University of California, Irvine, CA 92697, USA<br><sup>3</sup>Woodwell Climate Research Center, Falmouth, MA 02540, USA<br><sup>4</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br><sup>5</sup>Biospheric 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><strong>UPDATE OF DATASET TO 2023:</strong></p> <p>This dataset has now been extended to 2023. Since the first release of this dataset, multiple updates to the model input data have been made:</p> <p>- Update from MODIS C6 to MODIS C6.1 for all MODIS input data, including MCD12Q1 land cover types, MCD14ML active fires, MCD15A2H fPAR, MOD44B VCF, MOD44W land-water mask, and MCD64A1 burned area.<br>- Update of Hansen forest loss data from v1.9 to v1.11.<br>- Update of GLEAM evaporative stress data from v3.6b to v3.7b.<br>- Extension of ERA5-land data to 2023.<br>- Addition of land cover type layers to the 500-m resolution data files.</p> <p> </p> <p>Files contain 500-m (per MODIS tile) and 0.25 degree aggregated (global grid) carbon emissions and burned area from biomass burning for 2002-2022, as part of the paper "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)" published in Geoscientific Model Development (https://doi.org/10.5194/gmd-15-8411-2022). 500-m resolution files include land cover type grids. 0.25 degree global grid files also include biome partitioning and accompanying biome fractional cover grids.</p> <p>Zip archives with filenames "500m_YYYY.zip" contain annual files named "Model500m_2002-2023yr_h##v##_YYYY.nc", which are the 500-meter resolution model results per MODIS tile using the MODIS sinusoidal projection. Carbon emission data layers are:</p> <p>- Total biomass burning carbon emissions from aboveground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_AG_TOT)</p> <p>- Total biomass burning carbon emissions from belowground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_BG_TOT)</p> <p>- Fire-related forest loss carbon emissions from aboveground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_AG_FL)</p> <p>- Fire-related forest loss carbon emissions from belowground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_BG_FL)</p> <p>Total emissions are calculated as: C_AG_TOT + C_BG_TOT. Total fire-related forest loss emissions are calculated as: C_AG_FL + C_BG_FL.</p> <p>Burned area data layers are:</p> <p>- Total burned area; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_TOT)</p> <p>- Burned area from fire-related forest loss; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_FL)</p> <p>The Zip archive with filename "025d_2002_2023.zip" contains annual files named "Model500m_2002-2023yr_025d_YYYY.nc", which are the 500-m model results aggregated to a 0.25 degree global lat-lon grid. These files contain the same variables as the 500-m files, but aggregated to 0.25 degree resolution (MOD_CMG025). Furthermore, these files include biome partitioning of emissions and burned area (MOD_CMG025BIOME) and provide accompanying biome fractional cover grids for all 20 biomes (variable 'biomes'). Biomes are listed in detail in Table S1 of the van Wees et al. (2022) paper. The biomes 'water', 'snow/ice' and 'barren' were excluded from Table S1 because of their negligible share, but are included in the files provided here for completeness.</p>
Model code for "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>500 m fire carbon emissions model code as part of 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 Wees<sup>1</sup>, Guido R. van der Werf<sup>1</sup>, James T. Randerson<sup>2</sup>, Brendan M. Rogers<sup>3</sup>, Yang Chen<sup>2</sup>, Sander Veraverbeke<sup>1</sup>, Louis Giglio<sup>4</sup>, and Douglas C. Morton<sup>5</sup></p> <p><sup>1</sup>Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br> <sup>2</sup>Department of Earth System Science, University of California, Irvine, CA 92697, USA<br> <sup>3</sup>Woodwell Climate Research Center, Falmouth, MA 02540, USA<br> <sup>4</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br> <sup>5</sup>Biospheric 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>Developed in Python version 2.7.16. Please note, this code is meant to give a general overview of the model structure and not to fully reproduce the model results with the push of one button. The full model code is much more complex to account for various simulation scenarios and relies on numerous large input datasets that all require extensive preprocessing. By omitting these complexities, we tried to make this script as understandable as possible. In case your goal is to reproduce the model in detail, please contact the first author to discuss the possibilities.</p>
CONSUMPTION MANAGEMENT OF FUEL AND ENERGY RESOURCES - THE BASIS OF ENERGY EFFICIENCY
Open the record for dataset details and reuse information.
Effect of Walking on Brain Fuel Consumption in Mild Alzheimer's Disease
ClinicalTrials.gov study NCT02708485. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Analysis of Virtual Sensors for Predicting Aircraft Fuel Consumption
Previous research described the use of machine learning algorithms to predict aircraft fuel consumption. This technique, known as Virtual Sensors, models fuel consumption as a function of aircraft Flight Operations Quality Assurance (FOQA) data. FOQA data consist of a large number of measurements that are already recorded by many commercial airlines. The predictive model is used for anomaly detection in the fuel consumption history by noting when measured fuel consumption exceeds an expected value. This exceedance may indicate overconsumption of fuel, the source of which may be identified and corrected by the aircraft operator. This would reduce both fuel emissions and operational costs. This paper gives a brief overview of the modeling approach and describes efforts to validate and analyze the initial results of this project. We examine the typical error in modeling, and compare modeling accuracy against both complex and simplistic regression approaches. We also estimate a ranking of the importance of each FOQA variable used as input, and demonstrate that FOQA variables can reliably be used to identify different modes of fuel consumption, which may be useful in future work. Analysis indicates that fuel consumption is accurately predicted while remaining theoretically sensitive to sub-nominal pilot inputs and maintenance-related issues.
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