Flame features and oscillation characteristics in near-blowout swirl-stabilized flames using high-speed OH-PLIF and mode decomposition methods

Cao, Zhen; Yu, Xin; Peng, Jiangbo; Hu, Bin; Wang, Zhonghao; Yu, Yang; Gao, Long; Han, Minghong; Yuan, Xun; Wu, Guohua

doi:10.1016/j.cja.2022.05.009

Cited by 6 publications

(2 citation statements)

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“…The swirl-stabilized burner structure was similar to the dual-swirl burner model of the German Aerospace Center (DLR), which can be a typical research object of swirl flow structure and dynamics. The schematic of the dual-swirl burner setup has been reported in our recent work [20]. The angle of the primary swirler is 45 • with a swirl number of 0.11.…”

Section: Experiments Detailsmentioning

confidence: 99%

“…In our recent works, flame area and moment features have been used to investigate the near-LBO flame dynamics. The heat-release frequencies and dominant oscillation modes were obtained to demonstrate the oscillation characteristics of the near-LBO flame [20]. In the above-mentioned study, a flame hole structure from a turbulent flame front was found, but whether it can be used as a novel flame feature requires subse-quent research.…”

Section: Introductionmentioning

confidence: 99%

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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: Experiments Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%