Optimization of an inertance pulse tube refrigerator using the particle swarm optimization algorithm

Abstract

In this study, a particle swarm optimization method is employed to find the optimal operating parameters and geometrical parameters, which maximize the coefficient of performance (COP) of an inertance pulse tube refrigerator (IPTR). The considered decision variables of the IPTR are the charging pressure, which varies from 15 to 25 bar, operating frequency varying from 20 to 60 Hz, geometrical parameters, such as diameter varying from 15.0 to 25.00 mm, and length varying from 40.0 to 70 mm of the regenerator; diameter varying from 12.0 to 20.00 mm and length varying from 40.0 to 80 mm of the pulse tube; and diameter varying from 2.0 to 6.00 mm and length varying from 1.0 to 3.0 m of the inertance tube. A 1D numerical model, based on the finite volume discretization of governing equations has been selected to build the initial design matrix and solve the governing continuity, momentum, and energy equations. Analysis of variance is performed using the result obtained from the numerical simulation to visualize the variations of COP as a combination of various input parameters. It is observed that after optimizing the input parameters, the COP of the IPTR increases by 15.14%. K E Y W O R D S ANOVA, COP, IPTR, PSO, RSM PANDA AND ROUT | 3509precisely the flow physics at a low operating frequency. Ashwini et al 36 used a thermal nonequilibrium model to visualize variations of the temperature of the solid. Rout et al 37 investigated the effect of porosity of the regenerator on the cooling performance. A few more researchers used the FLUENT code to visualize the effect of thermodynamic processes, swirling flows, and so forth on the refrigeration performance. [35][36][37][38][39] Mulcahey et al 40 reported the convection instabilities generated inside the pulse tube, under the influence of gravity using FLUENT. John et al 41 proposed an approximate design method for the design of a single-stage IPTR using design parameters such as refrigeration capacity, refrigeration temperature, frequency, mean pressure, and pressure ratio using the NIST code REGEN 3.3 (for regenerator), PHASOR, and INERTANCE (a MATHCAD code developed by Radebaugh to design the inertance tube). Many users also used the 1D commercial code SAGE developed by Gedeon associates for the design and optimization of the pulse tube refrigerator. 42,43 No doubt these codes are capable enough for designing pulse tube refrigerators to achieve the targeted output, but modern technology requires high cooling capacity cryocoolers at a fixed refrigeration temperature for further performance improvement of machines used for space and defense applications. This can only be possible by optimizing the performance as a function of input parameters.Multiobjective optimization was applied to improve the performance of a PTR by Jafari et al. 44 In contrast, Rout et al 45 used NSGA-II in combination with "response surface methodology" (RSM) to visualize the simultaneous effect of more than one parameter upon the cooling performance. But for the sake of simplicity, they have consid...

Optimization and sensitivity analysis of high frequency pulse tube cryocooler for aerospace application

“…Response surface methodology (RSM) is an approach that can be applied for optimizing mechanical systems (Nariman et al, 2021) through several combinations of factor levels to build a regression model. A 1D analysis of the cryocooler (Panda and Rout et al, 2019) where an optimized COP for a fixed cold end temperature was reported. Even though many researchers have adopted RSM analysis, an optimization method and sensitivity analysis with CFD data for obtaining the lowest cold end temperature is lacking.…”