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A Bayesian optimization framework for the control of combustion instability of a bluff-body stabilized combustor.

Authors :
Yang, Jun
Shao, Changxiao
Wang, Lei
Wen, Qizhe
Yang, Niewei
Chen, Zhi X.
Li, Lei
An, Qiang
Jin, Tai
Luo, Kun
Source :
Physics of Fluids. May2024, Vol. 36 Issue 5, p1-11. 11p.
Publication Year :
2024

Abstract

Control of combustion instability for a realistic gas-turbine combustor is challenging. This work aims to establish an efficient numerical framework for optimization to improve the combustion stability of a bluff-body combustor. Large eddy simulations of the spray combustion process are conducted, and the experimental measurements are used to evaluate the numerical accuracy of the baseline case. The air preheating temperature, the Sauter mean diameter of fuel droplets, and the location of liquid fuel injection are regarded as input variables. The root mean square of pressure amplitude is regarded as an optimization objective. The Bayesian optimization framework is proposed that includes the sampling process, surrogate model, acquisition function, and genetic algorithm optimizer processes. It is found that PRMS can be reduced by 64% for the optimized case compared to the baseline case using only 17 sample evaluations. This work is promising as it provides an effective optimization framework for the development of next-generation gas-turbine combustors. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10706631
Volume :
36
Issue :
5
Database :
Academic Search Index
Journal :
Physics of Fluids
Publication Type :
Academic Journal
Accession number :
177609562
Full Text :
https://doi.org/10.1063/5.0207790