Optimal Design of Orifice Pulse Tube Refrigerator Based on Response Surface and Genetic Algorithm

Abstract

The modeling and optimization of a pulse tube refrigerator is a complicated task, due to its complexity of geometry and nature. The present work aims to optimize the orifice type pulse tube refrigerator (OPTR) using the response surface methodology (RSM). The influence of operating condition like frequency, charging pressure, orifice opening, and geometrical dimensions of pulse tube and regenerator on cold end temperature and input compressor power in the OPTR is investigated. For a fixed reservoir volume and regenerator size and porosity, the optimized value of the above parameters suggested by the response surface methodology has been solved using available one-dimensional code. It is reported that the cold head temperature varies due to variation in dimension of the pulse tube and regenerator in between 44 and 160 K, and compressor work varies from 265 to 1288 W. Using the results from the simulation, RSM is conducted to analyze the effect of the independent variables on the responses. To check the accuracy of the model, the analysis of variance method has been conducted. A quadratic model for cold end temperature and compressor input power has been developed. Based on the proposed mathematical RSM models, a novel multi-objective optimization study, using the non-dominated sorting genetic algorithm, has been performed to optimize the responses by generating the pareto frontiers. To avoid subjectiveness and imprecision, maximum deviation theory is used to rank the pareto frontiers based on composite scores.