Multi-objective optimization of hydro-viscous flexible drive for dynamic characteristics using genetic algorithm

被引:5
|
作者
Cui, Jianzhong [1 ,2 ]
Li, Hu [3 ]
Zhang, Dong [3 ]
Xu, Yawen [3 ]
Xie, Fangwei [4 ]
机构
[1] Yancheng Inst Technol, Res Ctr Mould Intelligent Mfg Technol, Yancheng, Peoples R China
[2] Southeast Univ, Sch Mech Engn, Nanjing, Peoples R China
[3] Yancheng Inst Technol, Sch Mech Engn, Yancheng, Peoples R China
[4] China Univ Min & Technol, Sch Mech Engn, Xuzhou, Jiangsu, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Genetic algorithm; Multi-objective optimization; Dynamic characteristics; Hydro-viscous drive; Flexible transmission efficiency; ROUGH; SURFACES; CONTACT; CLUTCH; MODEL; FLOW;
D O I
10.1108/ILT-12-2020-0472
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Purpose The purpose of this study is to investigate the flexible dynamic characteristics about hydro-viscous drive providing meaningful insights into the credible speed-regulating behavior during the soft-start. Design/methodology/approach A comprehensive dynamic transmission model is proposed to investigate the effects of key parameters on the dynamic characteristics. To achieve a trade-off between the transmission efficiency and time proportion of hydrodynamic and mixed lubrication, a multi-objective optimization of friction pair system by genetic algorithm is presented to obtain the optimal combination of design parameters. Findings Decreasing the engagement pressure or the ratio of inner and outer radius, increasing the lubricating oil viscosity or the outer radius will result in the increase of time proportion of hydrodynamic and mixed lubrication, as well as the transmission efficiency and its maximum value. After optimization, main dynamic parameters including the oil film thickness, angular velocity of the driven disk, viscous torque and total torque show remarkable flexible transmission characteristics. Originality/value Both the dynamic transmission model and multi-objective optimization model are established to analyze the effects of main design parameters on the dynamic characteristics of hydro-viscous flexible drive.
引用
收藏
页码:1003 / 1010
页数:8
相关论文
共 50 条
  • [41] THE SOLUTION OF MULTI-OBJECTIVE FUZZY OPTIMIZATION PROBLEMS USING GENETIC ALGORITHM
    Kelesoglu, Omer
    SIGMA JOURNAL OF ENGINEERING AND NATURAL SCIENCES-SIGMA MUHENDISLIK VE FEN BILIMLERI DERGISI, 2006, 24 (02): : 102 - 108
  • [42] Robot trajectory planning using multi-objective genetic algorithm optimization
    Pires, EJS
    Machado, JAT
    Oliveira, PBD
    GENETIC AND EVOLUTIONARY COMPUTATION - GECCO 2004, PT 1, PROCEEDINGS, 2004, 3102 : 615 - 626
  • [43] Multi-objective optimization of membrane separation modules using genetic algorithm
    Yuen, CC
    Aatmeeyata
    Gupta, SK
    Ray, AK
    JOURNAL OF MEMBRANE SCIENCE, 2000, 176 (02) : 177 - 196
  • [44] A multi-objective optimization for memory BIST sharing using a genetic algorithm
    Zaourar, Lilia
    Kieffer, Yann
    Wenzel, Arnaud
    2011 IEEE 17TH INTERNATIONAL ON-LINE TESTING SYMPOSIUM (IOLTS), 2011,
  • [45] Multi-objective optimization of radar absorbing coating using the genetic algorithm
    Beijing Institute of Aeronautical Materials, Beijing 100095, China
    Hangkong Cailiao Xuebao/Journal of Aeronautical Materials, 2007, 27 (03): : 82 - 86
  • [46] Multi-objective Flexible Scheduling Optimization Scheme base on Improved DNA Genetic Algorithm
    Nie Shuzhi
    Zhong Yanhua
    JOURNAL OF COMPUTERS, 2012, 7 (08) : 1982 - 1989
  • [47] The new model of parallel genetic algorithm in multi-objective optimization problems - Divided range multi-objective genetic algorithm
    Hiroyasu, T
    Miki, M
    Watanabe, S
    PROCEEDINGS OF THE 2000 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2, 2000, : 333 - 340
  • [48] A Species-Based Multi-Objective Genetic Algorithm for Multi-Objective Optimization Problems
    Sun Fuquan
    Wang Hongfeng
    Lu Fuqiang
    2014 11TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA), 2014, : 5063 - 5066
  • [49] Scheduling of a flexible job-shop using a multi-objective genetic algorithm
    Agrawal, Rajeev
    Pattanaik, L. N.
    Kumar, S.
    JOURNAL OF ADVANCES IN MANAGEMENT RESEARCH, 2012, 9 (02) : 178 - 188
  • [50] Solving flexible multi-objective JSP problem using a improved genetic algorithm
    Lan M.
    Xu T.
    Peng L.
    Journal of Software, 2010, 5 (10) : 1107 - 1113