A mixture-fraction-based hybrid binomial Langevin-multiple mapping conditioning model
- Submitting institution
-
Imperial College of Science, Technology and Medicine
- Unit of assessment
- 12 - Engineering
- Output identifier
- 301
- Type
- D - Journal article
- DOI
-
10.1016/j.proci.2018.06.122
- Title of journal
- Proceedings of the Combustion Institute
- Article number
- -
- First page
- 2151
- Volume
- 37
- Issue
- 2
- ISSN
- 0082-0784
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
10.1016/j.proci.2018.06.122
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
1
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This methodology is expected to have a major impact by facilitating the efficient design of low-emission energy conversion devices. Work presented during the International Symposium on Combustion and accepted for publication in the Proceedings of The Combustion Institute. Subsequently honoured with the 2019 Distinguished Paper award in Turbulent Combustion for quality, achievement, and significance to advance a field of combustion science (https://www.combustioninstitute.org/news/2019-distinguished-paper-award-dpa-in-the-turbulent-flames-combustion-colloquium/). The work has attracted co-funding £70k from Toyota as part of the EPSRC-DTP “Transition to a sustainable zero pollution economy” initiative and methodology developments form part of a $380K award from the AFOSR/EOARD.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -