Shock train leading-edge detection in an isolator using genetic algorithm

Inlet unstart is a complex phenomenon which would dramatically degrade the performance of hypersonic vehicles. Shock train leading-edge (STLE) detection is fundamental to inlet unstart prevention control of scramjet engine. First, the numerical solution of unsteady Reynolds-averaged Navier–Stokes equations for the hypersonic inlet is obtained and the results provide the dataset of STLE detection. Based on genetic algorithm, an STLE detection method is proposed in this article to achieve accurate STLE locations with a sensor array of least amount of sensors. The validity of the method is then testified by comparing the calculated STLE locations with actual ones originated by datasets.