Establishment of super sonic inlet flow pattern monitoring system: A workflow

Abstract

To reduce the negative effects of the buzz flow state of supersonic air inlets, this paper proposes a process in which an online monitoring system for the supersonic inlet flow patterns is established with pressure sensors. The first step in this workflow is to build an experimental system based on the objective air inlet and a large number of pressure sensors. After that, for each pressure sensor, based on Sliding Window Short-Time Fourier Transform (SW-STFT) pre-processing and Deep residual network (ResNet) structure, a method to train a sub classifier (Classifier trained by only one sensor signal) with each pressure sensor is proposed to achieve efficient extraction of highly perturbed signal information and high classification accuracy of different flow patterns. However, the main limitation of this workflow is that too many pressure sensors can increase the complexity of aero engine control systems and make the installation difficult. Therefore, in the third step, a multi-objective optimization model is built to solve this problem and find the best subset of sensors in the experimental system. Compared with previous studies, the flow modal monitoring system established in this paper has the advantages of real-time monitoring, higher classification accuracy, and better performance robustness. At the same time, this workflow allows the construction of individualized flow monitoring systems for different inlets.