Optimization of a Fan-Shaped Hole for Film Cooling Using a Surrogate Model

Abstract

A numerical procedure for shape optimization of a fan-shaped hole is presented to enhance film-cooling effectiveness by combining a three-dimensional Reynolds-averaged Navier-Stokes analysis with the radial neural network method, a well known surrogate modeling technique for optimization. The injection angle of the hole, lateral expansion angle of hole and ratio of length-to-diameter of the hole are chosen as design variables and spatially averaged film-cooling effectiveness is considered as an objective function which is to be maximized. Latin hypercube sampling is used to determine the training points as a mean of the design of experiment. Sequential quadratic programming is used to search for the optimal point from the constructed surrogate. The film-cooling effectiveness has been successfully improved by the optimization with increased values of all design variables as compared to the reference geometry.