Research on the parametric design of marine nuclear-powered turbine and the multi-objective intelligent optimization of aerothermodynamics performance under variable working conditions

In order to improve the aerothermodynamics performance of the marine nuclear-powered turbine and control the exhaust humidity of the blade grid, thereby improving the operating efficiency and power of the turbine and ensuring safety. We propose a method for parameterized reconstruction of steam turbine blade profiles based on their geometric parameters using a coordinate equation developed based on the third-order Bezier curve. By combining the blade parameterized reconstruction method with a Kriging approximation model and a multi-objective genetic algorithm (GA), we developed an optimized system for thermodynamic performance in turbines. The optimization objective was the cascade core thermal parameters of steam turbine under multiple operating conditions. The design parameters were the geometric parameters of the parameterized blade profile. Based on the calculation results of wet steam non-equilibrium condensate flow of steam turbine, the optimization method and process of multi-objective thermodynamic performance of steam turbine blades based on Kriging model were proposed. Then, we executed parameterized reconstruction of a Dykas planar cascade and a steam turbine 3D cascade to achieve multi-parameter, multi-condition design optimization of planar and 3D cascades. Using the proposed novel method, the isentropic efficiency and stage power of steam turbine cascades at variable operating conditions were enhanced, thermodynamic parameters (e.g., velocity, temperature, and pressure) were more homogeneously distributed, and overload condition sites showed more significant improvements. Thus, the proposed method achieved multi-condition, multi-constraint thermodynamic performance design optimization of wet steam turbine blades, thereby providing insights for intelligent design optimization and operation of wet steam turbine cascades.