Lean blowout characteristics of spray flame in a multi-swirl staged combustor under different fuel decreasing rates

Zhao, Qianpeng; Yang, Jinhu; Liu, Cunxi; Liu, Fuqiang; Wang, Shaolin; Mu, Yao; Xu, Gang; Zhu, Jihong

doi:10.1016/j.cja.2022.06.008

Cited by 6 publications

(1 citation statement)

References 27 publications

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“…In the context of reducing NOx emission, the swirl flame easily becomes extinct due to very low equivalence ratio levels. Meanwhile, the extinction events and vortex breakdown become more likely to occur once the lean limit is reached, exacerbating the complexity of the interaction between flow and combustion [3][4][5]. The investigation of the flame structure feature characterizing the flame evolution process is of great significance for understanding dynamic behaviors and recognizing the flame state.…”

Section: Introductionmentioning

confidence: 99%

A Neural Network-Based Flame Structure Feature Extraction Method for the Lean Blowout Recognition

Yan,

Cao,

Peng

et al. 2024

Aerospace

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A flame’s structural feature is a crucial parameter required to comprehensively understand the interaction between turbulence and flames. The generation and evolution processes of the structure feature have rarely been investigated in lean blowout (LBO) flame instability states. Hence, to understand the precursor features of the LBO flame, this work employed high-speed OH-PLIF measurements to acquire time-series LBO flame images and developed a novel feature extraction method based on a deep neural network to quantify the LBO features in real time. Meanwhile, we proposed a deep neural network segmentation method based on a tri-map called the Fire-MatteFormer, and conducted a statistical analysis on flame surface features, primarily holes. The statistical analysis results determined the relationship between the life cycle of holes (from generation to disappearance) and their area, perimeter, and total number. The trained Fire-MatteFormer model was found to represent a viable method for determining flame features in the detection of incipient LBO instability conditions. Overall, the model shows significant promise in ascertaining local flame structure features.

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Section: Introductionmentioning

confidence: 99%