Supersonic compressor cascade shape optimization under multiple inlet Mach operating conditions

Abstract

Transonic compressors are widely used today in propulsion and industrial applications thanks to their higher specific work compared to subsonic. In this work, the aerodynamic optimization of a two-dimensional Computational Fluid Dynamics (CFD) model of the transonic cascade ARL-SL19 is described. The validated computational model is used for a multi-objective optimization of the cascade at three different inlet Mach numbers using a genetic algorithm and an artificial neural network, with the aim of reducing total pressure loss and increasing maximum pressure ratio. Finally, the optimized shapes on the Pareto fronts are investigated, analyzing mechanisms responsible for loss reduction and enhanced compression. Profiles having the lowest losses have flatter camberlines and reduced acceleration of flow on the suction side, while geometries achieving the highest pressure ratio values have a more cambered shape with a concave suction side.