Multi-Objective Optimization of a Row of Film Cooling Holes Using an Evolutionary Algorithm and Surrogate Modeling

Abstract

Abstract Multi-objective shape optimization of a row of laidback fan-shaped film cooling holes has been performed using a hybrid multi-objective evolutionary approach in order to achieve an acceptable compromise between two competing objectives: the enhancement of film cooling effectiveness and the reduction of aerodynamic loss. In order to perform comprehensive optimization of a film cooling hole shape, the injection angle of the hole, lateral expansion angle of the diffuser, forward expansion angle of the hole, and pitch-to-hole diameter ratio are chosen as design variables. Forty experimental designs within the design spaces are selected using the Latin hypercube sampling method. The response surface approximation method is used to construct the surrogate using objective function values calculated at the experimental points using Reynolds-averaged Navier-Stokes analysis. The shear stress transport turbulence model is used as a turbulence closure. The optimization results are processed using the Pareto-optimal method. The Pareto-optimal solutions are obtained using a combination of a evolutionary algorithm and a local search method. The optimum designs are grouped using the k-means clustering technique, and the three optimal points selected in the Pareto-optimal solutions are evaluated by numerical analysis. The optimum designs give enhanced objective function values compared to the experimental designs. Acknowledgments This work was supported by the National Research Foundation of Korea (NRF) grant (no. 2009-0083510) funded by the Korean government (MEST) through the Multi-phenomena CFD Engineering Research Center. This research was also supported by INHA University Research Grant.