Xie, Y, Li, J, Yang, J et al. (1 more author) (2023) Laminar burning velocity blending laws using particle imaging velocimetry. Applications in Energy and Combustion Science, 13. 100114. ISSN 2666-352X
Abstract
One of the most essential inherent characteristics of a combustible mixture is laminar burning velocity. Because of its relevance, many methods for measuring laminar flame velocity have been devised. One advanced procedure was used to derive laminar burning velocity in a constant volume vessel for blends of iso-octane and n-heptane/air mixtures in this study: particle imaging velocimetry. Based on a precise flow field with vectors, particle imaging velocimetry (PIV) provides highly accurate laminar burning velocities. The results of the PIV method were compared with Schlieren data and simulations based on detailed LLNL gasoline surrogate chemical kinetics. It was found that the measured laminar burning velocities using the PIV method were more consistent. To predict the laminar burning velocity of the binary mixture of primary reference fuel (PRF) investigated in this study, five blending laws were used and all of them demonstrated strong application and high accuracy. The Leeds Q/k law based on the variation curve of the heat release rate/reaction process was found to have a strong predictive capability as well.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Crown Copyright © 2023 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (CC BY-NC-ND 4.0). |
Keywords: | Laminar burning velocity, Particle imaging velocimetry, Blending laws |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Thermofluids, Surfaces & Interfaces (iETSI) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Jan 2023 14:28 |
Last Modified: | 25 Jun 2023 23:13 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.jaecs.2023.100114 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:195263 |