Optimization of Rotor Blade Stacking Line Using Three Different Surrogate Models

Abstract

This paper describes the shape optimization of rotor blade in a transonic axial compressor rotor. Three surrogate models, Kriging, radial basis neural network and response surface methods, are introduced to find optimum blade shape and to compare the characteristics of object function at each optimal design condition. Blade sweep, lean and skew are considered as design variables and adiabatic efficiency is selected as an objective function. Throughout the shape optimization of the compressor rotor, the predicted adiabatic efficiency has almost same value for three surrogate models. Among the three design variables, a blade sweep is the most sensitive on the object function. It is noted that the blade swept to backward and skewed to the blade pressure side is more effective to increase the adiabatic efficiency in the axial compressor. Flow characteristics of an optimum blade are also compared with the results of reference blade.