A TIPSO algorithm assessment for aerothermodynamic optimization of hypersonic compression systems

Abstract

A two-step improved particle swarm optimization (TIPSO) algorithm was recently proposed and used for the optimization of flexible satellite-control parameters. It was found to be much more stable and much less complex than other evolutionary algorithms. In this article the efficacy of the TIPSO algorithm is investigated for multidisciplinary optimization of aerothermodynamic parameters of performance, cowl deflection angle and shock–boundary layer separation on a cone-derived, wedge-integrated hypersonic (waverider) compression system. This algorithm uses an aggregate objective function. Optimization results from the TIPSO algorithm are compared with those obtained from a hybrid genetic algorithm, particle swarm optimization using an inertial weight approach and a multi-objective genetic algorithm. Since optimality of the forebody configuration is the basic requirement, the optimization variables selected are the isolator exit Mach number, static pressure ratio across the forebody–inlet configuration, cycle temperature ratio and cowl (inlet) deflection angle, which is the maximum deflection angle corresponding to the optimum oblique shock angle generated by the inlet cowl. Simulation results show that for the given case study only the TIPSO algorithm can locate the globally best aerothermodynamic–geometric parameters, compared with all the other optimization methods.