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CNN-based blade tip vortex region detection in flow field
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Paper 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.

Paper Details

Date Published: 3 January 2020
PDF: 6 pages
Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113730P (3 January 2020); doi: 10.1117/12.2557248
Show Author Affiliations
Yanyang Luo, Southwest Univ. of Science and Technology (China)
Yanhua Shao, Southwest Univ. of Science and Technology (China)
Hongyu Chu, Southwest Univ. of Science and Technology (China)
Bin Wu, Southwest Univ. of Science and Technology (China)
Mingqi Huang, China Aerodynamics Research and Development Ctr. (China)
Yunbo Rao, Univ. of Electronic Science and Technology of China (China)


Published in SPIE Proceedings Vol. 11373:
Eleventh International Conference on Graphics and Image Processing (ICGIP 2019)
Zhigeng Pan; Xun Wang, Editor(s)

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