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ORIGINAL ARTICLE
Predicting the ignition sequences in a separated stratified swirling spray flame with stochastic flame particle tracking
1
Institute for Aero Engine, Tsinghua University, Beijing, 100084, China
2
National Key Laboratory of Science and Technology on Aero-Engine, School of Energy and Power Engineering, Beihang University, Beijing, 100191, China
3
National Key Laboratory of Science and Technology on Aero-Engine, Research Institute of Aero-Engine, Beihang University, Beijing 100191, China
Submission date: 2022-06-14
Acceptance date: 2022-09-04
Publication date: 2022-10-12
Corresponding author
J. Glob. Power Propuls. Soc. 2022;6:279-289
KEYWORDS
TOPICS
ABSTRACT
Stochastic flame particle tracking in conjunction with non-reacting combustor simulations can offer insights into the ignition processes and facilitate the combustor optimization. In this study, this approach is employed to simulate the ignition sequences in a separated dual-swirl spray flame, in which the newly proposed pairwise mixing-reaction model is used to account for the mass and energy transfer between the flame particle and the surrounding shell layer. Based on the flame particle temperature, the particle state can be classified in to burnt, hot gas, and extinguished. The additional state of hot gas is introduced to allow the flame particles with high temperature to survive from nonflammable region and then potentially to ignite the nearby favourable regions. The simulations of the separated stratified swirl spray flame reveal two different ignition pathways for flame stabilization. The first showed that some flame particles from the spark would directly enter the main recirculation zone resulting from the velocity randomness and then ignite both sides of the combustor simultaneously. The second showed that flame particles from the spark would ignite the traversed regions following the swirl motion inside the combustor. The predicted ignition sequences were compared with the evolution of flame morphology recorded by high-speed imaging from experiments, showing qualitative agreement.
FUNDING
This work is supported by National Natural Science Foundation of China No. 91841302, 52025062, and 91641109, and China Postdoctoral Science Foundation No. 2021M701859.
COMPETING INTERESTS
Qing Xie declares that he has no conflict of interest. Siheng Yang declares that he has no conflict of interest. Hao Cheng declares that he has no conflict of interest. Chi Zhang declares that he has no conflict of interest. Zhuyin Ren declares that
he has no conflict of interest.
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| eISSN: | 2515-3080 |
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