Michelbach, C orcid.org/0000-0001-6448-2705 and Tomlin, A (2023) Predicting the Combustion Behaviour of Tailorable Advanced Biofuel Blends Using Automatically Generated Mechanisms. In: Proceedings of the European Combustion Meeting 2023. 11th European Combustion Meeting, 26-28 Apr 2023, Rouen, France. Combustion Institute , https://ecm2023.sciencesconf.org/ , pp. 450-455.
Abstract
Emerging processes such as biomass alcoholysis have the potential to provide tailorable, advanced biofuels to replace conventional fossil fuels. To model the combustion characteristics of the resultant complex blends, automatic mechanism generation (AMG) techniques are applied to produce detailed ethyl (ethyl levulinate, diethyl ether, ethanol) and butyl (n-butyl levulinate, di-n-butyl ether, n-butanol) kinetic mechanisms. The predictive capabilities of these mechanisms are evaluated, showing a high degree of accuracy when compared to ignition delay time (IDT) and speciation measurements, at thermodynamic conditions of relevance to engine technologies.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author produced version of a conference paper published in Proceedings of the European Combustion Meeting 2023. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/T033088/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 01 Jun 2023 09:39 |
Last Modified: | 25 Jul 2023 15:54 |
Published Version: | https://ecm2023.sciencesconf.org/ |
Status: | Published |
Publisher: | Combustion Institute |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:199617 |