Multi-Objective Optimization of Transonic Compressor Blade Using Evolutionary Algorithm

Abstract

Multi-objective optimization is performed of the NASA rotor67 transonic compressor blade.The objectives are to maximize the stage pressure ratio, as well as to minimize the compressor weight.The backbones of the optimization approach consist of a genetic algorithm, a gradient-based method, and a response surface model.The genetic algorithm is used to facilitate the multi-objective optimization and to find the global optima of highdimensional problems.The gradient-based method accelerates the optimization convergence rate.The response surface model, constructed to replace the computationally expensive analysis tool, reduces the computational cost.Representative solutions are selected from the Pareto-optimal front to verify against the computational fluid dynamics tool.Compared with the baseline design, some optimal solutions increase the stage pressure ratio by 1.8% and decrease the weight by 5.4%.A detailed study of flow structure near peak efficiency is presented by means of pressure distribution and streamlines inside boundary layers.Results show that the optimized blade favors a lighter weight by a thinner blade shape.The stage pressure rise is attributed to a reduced separation zone and a weakened shock wave.