Aerodynamic Optimization of Radial Turbines Based on Surrogate Model of Pre-screened Strategies and DFFD Parameterization

There were some problems such as difficult geometric control, many control variables and low optimization efficiency in aerodynamic optimization of three-dimensional complex blade surfaces of radial turbines. To solve these problems, multi-degree-of-freedom parameterization of radial turbine runner and blade multidimensional geometry were implemented based on DFFD method. Then an differential evolution algorithm assisted by surrogate models of pre-screened strategies(Pre-SADE) was introduced. Finally, a data-driven three-dimensional aerodynamic optimization platform for centripetal turbines was constructed by combining python and batch script of process automation. The platform was used to carry out the joint optimization design of flow channel-static/rotating blades for the radial turbines. The results show that after optimization, the adiabatic efficiency and mass-flow of the design point of the centripetal turbines are increased by 1.66% and 1.7% respectively, which effectively reduces the shock intensity in the guide vane channel and the shock loss on the suction surfaces of the guide vane, and the efficiency characteristics of the design rotational speed are improved in all working conditions. Finally, the method and platform may ensure the aerodynamic optimization efficiency, and effectively reduce the optimization variables and sample real evaluation times, significantly improve the optimization efficiency, and meet the rapid and elaborate optimization design requirements of radial turbines.