Multi-Objective Parameter Optimization of Pulse Tube Refrigerator Based on Kriging Metamodel and Non-Dominated Ranking Genetic Algorithms

Structure parameters have an important influence on the refrigeration performance of pulse tube refrigerators. In this paper, a method combining Kriging metamodel and Non-Dominated Sorting Genetic Algorithm II (NSGA II) is proposed to optimize structure regenerators tubes obtain better cooling capacity. Firstly, original refrigerator CFD model established improve iterative solution efficiency. On basis, NSGA was applied optimization iteration process optimal worst Pareto front solutions for performance, heat mass transfer characteristics which were further analyzed comparatively reveal mechanism structural parameters. The results show that presents prediction error about 2.5%. A 31.24% drop in minimum temperature 31.7% increase capacity at 120 K are achieved after optimization, pressure loss regenerator vortex caused by parameter changes main factors influencing whole current study provides scientific efficient design miniature cryogenic