Aerodynamic design by jointly applying S2 flow surface calculation and modern optimization methods on multistage axial turbine

Abstract

A three-stage axial turbine was redesigned by jointly applying S2 flow surface direct problem calculation methods and multistage local optimization methods. A genetic algorithm and artificial neural network were jointly adopted during optimization. A three-dimensional viscosity Navier–Stokes equation solver was applied for flow computation. H-O-H-topology grid was adopted as computation grid, that is, an H-topology grid was adopted for inlet and outlet segment, whereas an O-topology grid was adopted for stator zone and rotor zone. Through the optimization design, the total efficiency increases 1.1%, thus indicating that the total performance is improved and the design objective is achieved.

Keywords

turbine, optimization design, S2 flow surface direct problem calculation, genetic algorithm, artificial neural network