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

被引:1
|
作者
Panda, Debashis [1 ]
Rout, Sachindra Kumar [2 ]
机构
[1] Natl Inst Technol, Mech Engn Dept, Cryogen Engn Lab, Rourkela, Odisha, India
[2] CV Raman Coll Engn, Mech Engn Dept, Refrigerat Lab, Bhubaneswar 752054, Odisha, India
来源
HEAT TRANSFER-ASIAN RESEARCH | 2019年 / 48卷 / 08期
关键词
ANOVA; COP; IPTR; PSO; RSM; NUMERICAL-ANALYSIS; MULTIOBJECTIVE OPTIMIZATION; MODEL; PERFORMANCE; CRYOCOOLER; FLOW;
D O I
10.1002/htj.21552
中图分类号
O414.1 [热力学];
学科分类号
摘要
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%.
引用
收藏
页码:3508 / 3537
页数:30
相关论文
共 50 条
  • [31] Multiuser detection using the particle swarm optimization algorithm
    Liu, C
    Xiao, Y
    INTERNATIONAL SYMPOSIUM ON COMMUNICATIONS AND INFORMATION TECHNOLOGIES 2005, VOLS 1 AND 2, PROCEEDINGS, 2005, : 350 - 353
  • [32] Binary wavefront optimization using particle swarm algorithm
    Fang, Longjie
    Zuo, Haoyi
    Yang, Zuogang
    Zhang, Xicheng
    Du, Jinglei
    Pang, Lin
    LASER PHYSICS, 2018, 28 (07)
  • [33] A hybrid Particle Swarm Optimization algorithm for function optimization
    Sevkli, Zulal
    Sevilgen, F. Erdogan
    APPLICATIONS OF EVOLUTIONARY COMPUTING, PROCEEDINGS, 2008, 4974 : 585 - +
  • [34] The particle swarm optimization algorithm in size and shape optimization
    P.C. Fourie
    A.A. Groenwold
    Structural and Multidisciplinary Optimization, 2002, 23 : 259 - 267
  • [35] PARTICLE SWARM OPTIMIZATION ALGORITHM FOR THE PREPACK OPTIMIZATION PROBLEM
    Agharezaei, Sajjad
    Falamarzi, Mehdi
    ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, 2019, 53 (02): : 289 - 307
  • [36] Particle Swarm Optimization Algorithm for Solving Optimization Problems
    Ozsaglam, M. Yasin
    Cunkas, Mehmet
    JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI, 2008, 11 (04): : 299 - 305
  • [37] A Novel Particle Swarm Optimization Algorithm for Global Optimization
    Wang, Chun-Feng
    Liu, Kui
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2016, 2016
  • [38] A Hybrid Whale Optimization and Particle Swarm Optimization Algorithm
    Yuan, Zijing
    Li, Jiayi
    Yang, Haichuan
    Zhang, Baohang
    PROCEEDINGS OF THE 2021 IEEE INTERNATIONAL CONFERENCE ON PROGRESS IN INFORMATICS AND COMPUTING (PIC), 2021, : 260 - 264
  • [39] An Adaptive Particle Swarm Optimization Algorithm for Unconstrained Optimization
    Qian, Feng
    Mahmoudi, Mohammad Reza
    Parvin, Hamid
    Pho, Kim-Hung
    Tuan, Bui Anh
    COMPLEXITY, 2020, 2020
  • [40] The particle swarm optimization algorithm in size and shape optimization
    Fourie, PC
    Groenwold, AA
    STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2002, 23 (04) : 259 - 267