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兵工学报 ›› 2017, Vol. 38 ›› Issue (9): 1839-1844.doi: 10.3969/j.issn.1000-1093.2017.09.022

• 研究简报 • 上一篇    下一篇

基于计算流体力学的车辆发动机散热器芯部外形优化

索文超1, 许翔2, 耿飞1   

  1. (1.陆军航空兵学院 预选士官训练基地, 北京 101123; 2.军事交通学院 军用车辆系, 天津 300161)
  • 收稿日期:2017-02-03 修回日期:2017-02-03 上线日期:2017-11-03
  • 作者简介:索文超(1979—),男,工程师,博士。E-mail:suowenchao@163.com

Optimization of Radiator Core Shape of Vehicle Engine Based on CFD

SUO Wen-chao1, XU Xiang2, GENG Fei1   

  1. (1.Primary Non-Commissioned Officer Training Base, Army Aviation Institute of PLA, Beijing 101123,China;2.Department of Military Vehicle, Military Transportation University, Tianjin 300161, China)
  • Received:2017-02-03 Revised:2017-02-03 Online:2017-11-03

摘要: 为降低散热器空气侧流动阻力和芯部体积以达到节约冷却风扇功率和车辆动力舱空间的目的,建立了散热器空气侧计算流体力学(CFD)模型和基于CFD分析的散热器芯部外形优化模型。在满足散热需求和动力舱安装空间要求的前提下,以空气侧流动阻力和芯部体积为目标函数,基于遗传算法对一板翅式散热器芯部外形进行了优化,对优化后散热器的散热性能进行了校核。结果表明,所建优化模型是可行的。采用正交试验设计法确定了各变量对优化目标影响的主次顺序:在空气流量一定时,散热器芯部高度对散热器空气侧流动阻力和芯部体积的影响最为显著。

关键词: 动力机械工程, 散热器, 计算流体力学, 遗传算法, 外形优化

Abstract: A radiator air side CFD model and an optimization model of radiator core shape based on CFD are established for reducing the airflow pressure drop and the volume of vehicle engine radiator core to save the cooling fan power and the vehicle engine compartment space. Under the precondition of satisfying the requirement of heat dissipating capacity, the shape of a plate-fin radiator core is optimized based on genetic algorithm by taking the air side pressure drop and the volume of its core as the target functions. The heat dissipation performance of the optimized radiator was verified. The optimized results show that the proposed optimization model is feasible. The primary and secondary sequences of the variables affecting the optimization targets are found by using an orthogonal experimental design method. And the height of radiator core has the most significant influence on the air side pressure drop and the volume of radiator core when the air flow rate is constant. Key

Key words: powermachineryengineering, radiator, computationalfluiddynamics, geneticalgorithm, shapeoptimization

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