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The manner in which an ultra-lean hydrogen flame stabilizes and blows off is crucial for the understanding and design of safe and efficient combustion devices. In this study, we use experiments and numerical simulations for pure H2-air flames stabilized behind a cylindrical bluff body to reveal the underlying physics that make such flames stable and eventually blow-off. Results from CFD simulations are used to investigate the role of stretch and preferential diffusion after a qualitative validation with experiments. It is found that the flame displacement speed of flames stabilized beyond the lean flammability limit of a flat stretchless flame (ϕ=0.3) can be scaled with a relevant tubular flame displacement speed. This result is crucial as no scaling reference is available for such flames. We also confirm our previous hypothesis regarding lean limit blow-off for flames with a neck formation that such flames are quenched due to excessive local stretching. After extinction at the flame neck, flames with closed flame fronts are found to be stabilized inside a recirculation zone.

This paper proposes a YOLO fire detection algorithm based on an attention-enhanced ghost mode, mixed convolutional pyramids, and flame-centre detection (AEGG-FD). Specifically, the enhanced ghost bottleneck is stacked to reduce redundant feature mapping operations in the process for achieving lightweight reconfiguration of the backbone, while attention is added to compensate for accuracy loss. Furthermore, a feature pyramid built using mixed convolution is introduced to accelerate network inference speed. Finally, the local information is extracted by the designed flame-centre detection (FD) module for furnishing auxiliary information in effective firefighting. Experimental results on both the benchmark fire dataset and the video dataset show that the AEGG-FD performs better than the classical YOLO-based models such as YOLOv5, YOLOv7 and YOLOv8. Specifically, both the mean accuracy (mAP0.5, reaching 84.7%) and the inferred speed (FPS) are improved by 6.5 and 8.4 respectively, and both the number of model parameters and model size are compressed to 72.4% and 44.6% those of YOLOv5, respectively. Therefore, AEGG-FD achieves an effective balance between model weight, detection speed, and accuracy in firefighting.