Learning from evolutionary algorithm based design optimization of axisymmetric scramjet inlets

Optimisation is a key element in today's design processes and there is an ever increasing emphasis on development of efficient algorithms to deal with computationally expensive optimisation problems. While surrogate assisted optimisation methods are commonly used for such problems, there are few studies that attempt to understand the optimal solutions to help in directing the optimisation process in the hope of faster convergence. This paper introduces a novel scheme to uncover hidden relationships among the variables in the promising regions of the search space. Such relationships an we subsequently used to separate promising and unpromising designs. The study is conducted using a scramjet inlet design optimisation exercise, where the optimal geometry is sought by simultaneously maximising the compression efficiency and minimising the inlet drag and the maximum adverse pressure gradient. Results clearly indicate that the scheme has the potential to reduce evaluation of unpromising solutions to about 50%. The paper also provides new directions on how such schemes can be adopted on-the-fly within an optimisation framework thereby accelerating its rate of convergence and reducing computational cost.