Study on algorithms of flush air data sensing system for hypersonic vehicle

摘要：

　　In this paper,according to the typical hypersonic vehicle,a study of flush air data sensing system using BP neural network algorithm were carried out.The independent research and development of CACFD software for solving the Euler equations,calculation of vehicle head pressure distribution as the neural network training input,the corresponding flow conditions,such as the static pressure,the Ma number,angle of attack and side slip angle as the target samples to train the neural network,set up FADS algorithm based on BP neural network and testing.Studies showed that the FADS algorithm based on neural network technique is robust and has good precision,strong real-time,is a very effective algorithm.Research results had showed that : in the sample number range,the precision of FADS is increasing with the the number of samples increasing ; the average error of FADS algorithm decreases with increase of pressure measuring point of layout combinations; contains large cone angle position measurement points,points than only the small cone angle measuring point combination results average error.To remove vertex pressure point,had little effect on the precision of algorithm,the FADS algorithm generalization performance is very stable with 1%pressure measurement error.

关键词： Hypersonic vehicle Flush air data system Neural networks Computational Fluid Dynamics

作者: Guangqiang Chen Guidong Wang Bingyan Chen Weijiang Zhou Chuqun Ji

作者单位: The Institute of aerodynamics theories and application of China Academy of Aerodynamic of Aerospace(CAAA),Beijing,100074,China

会议类型: 国际会议

会议名称: 2014 Asia-Pacific International Symposium on Aerospace Technology(2014亚太航空航天技术学术会议)

会议地点: 上海

会议语种:英文

页码: 1-5

在线出版日期: 2014-09-24（万方平台首次上网日期，不代表论文的发表时间）