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Predictive Capability Assessment of Probabilistic Machine Learning Models for Density Prediction of Conventional and Synthetic Jet Fuels

Hall, Clemens and Rauch, Bastian and Bauder, Uwe and Le Clercq, Patrick and Aigner, Manfred (2021) Predictive Capability Assessment of Probabilistic Machine Learning Models for Density Prediction of Conventional and Synthetic Jet Fuels. Energy and Fuels, 35, pp. 2520-2530. ACS Publications. doi: 10.1021/acs.energyfuels.0c03779. ISSN 0887-0624.

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Official URL: https:\\doi.dx.org/10.1021/acs.energyfuels.0c03779


Item URL in elib:https://elib.dlr.de/143874/
Document Type:Article
Title:Predictive Capability Assessment of Probabilistic Machine Learning Models for Density Prediction of Conventional and Synthetic Jet Fuels
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Hall, ClemensUNSPECIFIEDUNSPECIFIED
Rauch, BastianUNSPECIFIEDhttps://orcid.org/0000-0002-1393-5943
Bauder, UweUNSPECIFIEDhttps://orcid.org/0000-0002-5019-6043
Le Clercq, PatrickUNSPECIFIEDUNSPECIFIED
Aigner, ManfredUNSPECIFIEDUNSPECIFIED
Date:2021
Journal or Publication Title:Energy and Fuels
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:35
DOI:10.1021/acs.energyfuels.0c03779
Page Range:pp. 2520-2530
Publisher:ACS Publications
ISSN:0887-0624
Status:Published
Keywords:probabilistic, machine learning, predictive capability, density, jet fuels
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Clean Propulsion
DLR - Research area:Aeronautics
DLR - Program:L CP - Clean Propulsion
DLR - Research theme (Project):L - Virtual Engine
Location: Stuttgart
Institutes and Institutions:Institute of Combustion Technology > Multiphase flow and Alternative Fuels
Deposited By: Geigle, Dr. Klaus Peter
Deposited On:10 Sep 2021 08:29
Last Modified:24 May 2022 23:47

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