Optimization of a fan-shaped hole to improve film cooling performance by RBF neural network and genetic algorithm

To improve the cooling performance, shape optimization of a fan-shaped film cooling hole was carried out. Three geometric parameters, including incline angle, lateral expansion angle and hole length, were selected as the design parameters. Numerical model of the film cooling system was established, validated, and used to generate training samples. Radial basis function neural network (RBF-NN) was applied for surrogate model, and the optimal design parameters were determined by a kind of genetic algorithms. At low blowing ratio (M=0.5M=0.5), the area-averaged film cooling effectiveness can reach its maximum value in the design space as incline angle, lateral expansion angle and hole length-to-diameter ratio are 40.5°, 23.97° and 7.43. At M=1.5M=1.5, the optimal values of length-to-diameter ratio, lateral expansion angle and incline angle are 20.1°, 23.92° and 7.65. RBF-NN coupled with genetic algorithm is an effective scheme for the optimization of fan-shaped film cooling holes.