Performance optimization of a neon turboexpander based on the Kriging model and genetic algorithm

Abstract

The rapid development of high-temperature superconducting (HTS) technology has increased demand for stable and reliable cooling sources, with the neon refrigerator emerging as a favorable solution for HTS applications, among which neon turboexpander is the core component. This paper proposes an optimal design method tailored for the neon turboexpander, which combines a one-dimensional mean-line design, three-dimensional CFD analysis, adaptive Kriging surrogate model, and the genetic algorithm. The meridional contour of the turboexpander impeller is geometrically parameterized, and the three most critical structural parameters are selected through Sobol sensitivity analysis, and then the response surface analysis is carried out to analyze the coupling relationship between the structural parameters. After optimization, the total-to-static efficiency and output power of the neon turboexpander increased by 3.98% and 6.12%, respectively. Notably, the flow separation phenomenon within the optimized impeller is significantly improved, resulting in reduced flow loss. Furthermore, the optimized impeller demonstrates robust performance in a wide range of variable operating conditions. Therefore, the optimization design method has been proven effective, and the optimal turboexpander impeller structure can be obtained quickly and accurately.