Bayesian optimization design of inlet volute for supercritical carbon dioxide radial-flow turbine

Abstract

The radial-flow turbine, a key component of the supercritical CO2 ( S-CO2 ) Brayton cycle, has a significant impact on the cycle efficiency. The inlet volute is an important flow component that introduces working fluid into the centripetal turbine. In-depth research on it will help improve the performance of the turbine and the entire cycle. This article aims to improve the volute flow capacity by optimizing the cross-sectional geometry of the volute, thereby improving the volute performance, both at design and non-design points. The Gaussian process surrogate model based parameter sensitivity analysis is first conducted, and then the optimization process is implemented by Bayesian optimization (BO) wherein the acquisition function is used to query optimal design. The results show that the optimized volute has better and more uniform flow characteristics at design and non-design points. It has a smoother off-design conditions performance curve. The total pressure loss coefficient at the design point of the optimized volute is reduced by 33.26%, and the flow deformation is reduced by 54.55%.