CNN-based blade tip vortex region detection in flow field

Yanyang Luo; Yanhua Shao; Hongyu Chu; Bin Wu; Mingqi Huang; Yunbo Rao

doi:10.1117/12.2557248

3 January 2020 CNN-based blade tip vortex region detection in flow field

Yanyang Luo, Yanhua Shao, Hongyu Chu, Bin Wu, Mingqi Huang, Yunbo Rao

Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 113730P (2020) https://doi.org/10.1117/12.2557248
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China

Abstract

Research on characteristics of helicopter rotor blade tip vortex (BTV) is one of the key elements for helicopter rotor aerodynamic characteristics research. The existing traditional computational fluid dynamics (CFD) based detection methods of vortex core area in the flow field mainly use points or lines in the flow field for calculation. However, the traditional CFD-based model is complex, huge computational cost and without effective vortex core model. So the manual analysis will be necessary in some scenarios to simplify the work of vortex detection, such as vortex region detection for helicopter rotor BTV in domestic. In order to decrease the workload of manual analysis, we draw on the advanced research results in the field of computer vision and machine learning, especially target detection, and firstly propose a vortex region detection method in blade tip vortex based on You Only Look Once (YOLO) network. First of all, the vortex region is marked in flow field images under the guidance of domain experts to construct the vortex data set. Secondly, we propose an improved model based on yolo v3-tiny. Finally, the self-built vortex data set is used to train models. Experiments show that the CNN-based method has better result than traditional methods.

Citation Download Citation

Yanyang Luo, Yanhua Shao, Hongyu Chu, Bin Wu, Mingqi Huang, and Yunbo Rao "CNN-based blade tip vortex region detection in flow field", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113730P (3 January 2020); https://doi.org/10.1117/12.2557248

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