Study on aerodynamic optimization design of multistage axial turbine under multiple working conditions

A three-stage axial turbine was redesigned by using an aerodynamic optimization design process of multistage axial turbine in multiple working conditions, which combines quasi-3D design methods and multistage local optimization methods. Genetic algorithm and artificial neural network were employed to 3D local optimization of various cascades. The flow field was computed through a three-dimensional viscosity Navier-Stokes equation. With optimization design, the performance of every cascade was optimized, and the overall efficiency increased by 1% under the reliable total flow mass, indicating that the total performance was improved to satisfy the design requirements.