A real-time online unstart prediction approach for supersonic inlet

Abstract

Unstart of supersonic inlet is an abnormal phenomenon, which may cause serious accidents such as flameouts. In this paper, we experimentally record the pressure signals from start to unstart at 25 different measurement points on the wall of a two-dimensional supersonic inlet under eight different experimental conditions. It can be found the existence of precursor phenomena by analyzing the pressure signals and schlieren image of the flow field. In order to detect precursor phenomena, a real-time online unstart prediction approach is proposed by combining change-point detection (CD), wavelet packet (WPT) and convolutional neural network (CNN), named CD-WPT-CNN in this paper, whose effectiveness is verified under two test conditions. In addition, we introduce three traditional real-time online unstart prediction methods and further confirm the superiority of CD-WPT-CNN for practical applications by comparing their performance in test conditions. All in all, the experimental results show that the proposed CD-WPT-CNN performs better in many respects and has a good application prospect.