Axial Turbine Blade Aerodynamic Optimization Using a Novel Multi-Level Genetic Algorithm

Abstract

In this paper, a new multiploid genetic optimization method handling surrogate models of the CFD solutions is presented and applied for single objective turbine blade aerodynamic optimization problem. A fast, efficient, robust, and automated design method is developed to aerodynamically optimize 3D gas turbine blades. The design objectives are selected as maximizing the adiabatic efficiency and torque so as to reduce the weight, size and cost of the gas turbine engine. A 3-Dimensional steady Reynolds Averaged Navier Stokes solver is coupled with an automated unstructured grid generation tool. The solver is verified using two well known test cases. Blade geometry is modeled by 36 design variables plus the number of blades variable in a row. Fine and coarse grid solutions are respected as high and low fidelity models, respectively. One of the test cases is selected as the baseline and is modified by the design process. It was found that the multiploid genetic algorithm successfully accelerates the optimization at the initial generations for both optimization problems, while preventing converging to local optimums.