A multiple-objective shape optimization is implemented for two staggered rows of discrete film cooling holes on the suction surface of a turbine vane. The optimization aims to maximize the film cooling performance while minimizing the corresponding aerodynamic penalty. The cooling performance is assessed using the adiabatic film cooling effectiveness, while the aerodynamic penalty is measured with a mass-averaged total pressure loss coefficient. The conical expansion angle, the compound angle, and the length to diameter ratio of the non-expanded portion of the hole are selected as geometric design variables. The effect of varying the geometric variables on the adiabatic film cooling effectiveness and the aerodynamic penalty is analyzed using the optimization method and three-dimensional Reynolds-averaged Navier-Stokes (RANS) simulations. A non-dominated sorting genetic algorithm (NSGA-II) is coupled with an artificial neural network (ANN) to perform the multiple-objective optimization. RANS simulations are employed to construct the ANN network which produces low-fidelity predictions of the objective functions during the optimization. The Pareto front of optimum solutions is generated. Two optimum designs, denoted as the aerodynamic, and thermal optimums are chosen from the Pareto front and evaluated through RANS simulations. The optimum designs present improved performance in comparison to the reference design, which consists of cylindrical holes.

This content is only available via PDF.
You do not currently have access to this content.