基于多种群协同进化免疫多目标优化算法的百叶窗优化研究
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摘要
为降低空气流通阻力、增加空气流量以及获得最大的防护能力,对装甲车辆进气和排气百叶窗结构优化进行研究。建立装甲车辆进气和排气百叶窗数值计算模型,根据冷却系统工作特点搭建冷却风道半实物仿真模型,对比分析优化前的仿真结果和台架测试结果,并给出了优化目标函数表达式。为解决优化效率低、计算量大的问题,确定设计变量并建立设计变量与目标函数之间关系的椭圆基神经网络替代模型。通过分析目标函数随设计变量的变化规律,采用基于多种群协同进化免疫的多目标优化算法对模型进行优化,确定了最终解。利用自组织神经网络对样本点进行数据挖掘,找到了内在规律。研究结果表明,优化后的进气和排气百叶窗在提高防护能力的同时,提高了散热性能。
The structure optimization of intake and exhaust louvers of armored vehicle is studied to reduce air flow resistance and increase air flow. A numerical calculation model is established for the intake and exhaust louvers of armored vehicle. According to the operating characteristics of cooling system,a hardware-in-the-loop test-rig of air-cooling duct is set up. The simulated and test results of the duct before and after optimization are compared and analyzed,and the expression of optimization objective function is given. The design variables are determined. In order to solve the problem of low efficiency and large computation,an elliptic basis neural network agent model is established for the relationship between design variables and objective functions. The hybrid multi-objective optimization based on estimation of distribution algorithm( EDA) and artificial immune systems is used to find a final solution by analyzing the variation of objective function with the design variables. The inherent law of sample points is found by using self-organizing neural network for data mining. The research results show that the optimized inlet and exhaust louvers improve the performance of heat dissipation while improving the protection capability.
The structure optimization of intake and exhaust louvers of armored vehicle is studied to reduce air flow resistance and increase air flow. A numerical calculation model is established for the intake and exhaust louvers of armored vehicle. According to the operating characteristics of cooling system,a hardware-in-the-loop test-rig of air-cooling duct is set up. The simulated and test results of the duct before and after optimization are compared and analyzed,and the expression of optimization objective function is given. The design variables are determined. In order to solve the problem of low efficiency and large computation,an elliptic basis neural network agent model is established for the relationship between design variables and objective functions. The hybrid multi-objective optimization based on estimation of distribution algorithm( EDA) and artificial immune systems is used to find a final solution by analyzing the variation of objective function with the design variables. The inherent law of sample points is found by using self-organizing neural network for data mining. The research results show that the optimized inlet and exhaust louvers improve the performance of heat dissipation while improving the protection capability.
引文
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[12]段孟华.某柴油发动机缸内流场和燃烧的数值模拟分析[D].重庆:重庆理工大学,2016.DUAN M H.Numerical simulation of flow field and combustion in a diesel engine cylinder[D].Chongqing:Chongqing University of Technology,2016.(in Chinese)
[13]刘静乐.基于EDA和人工免疫系统的混合多目标优化算法[D].西安:西安电子科技大学,2012.LIU J L.Hybrid multi-objective optimizations based on EDA and AIS[D].Xi'an:Xidian University,2012.(in Chinese)
[14]秦仙蓉,潘杰,徐俭,等.塔式起重机结构有限元模型修正的响应面方法[J].振动与冲击,2018,37(6):244-250.QIN X R,PAN J,XU J,et al.Response surface method for the structural finite element model updating of tower cranes[J].Journal of Vibration and Shock,2018,37(6):244-250.(in Chinese)
[2]殷明,毕小平,周国印,等.基于CFD与近似模型的坦克百叶窗优化方法[J].装甲兵工程学院学报,2011,25(5):25-28.YIN M,BI X P,ZHOU G Y,et al.Optimization method for shutter of tank based on CFD and approximate model[J].Journal of Academy of Armored Force Engineering,2011,25(5):25-28.(in Chinese)
[3]李云龙,赵长禄,张付军,等.装甲车辆进排气格栅的气动特性试验研究与防护性能分析[J].兵工学报,2012,33(2):129-133.LI Y L,ZHAO C L,ZHANG F J,et al.Aerodynamic characteristics experimental study and protective performance analysis for intake and vent grilles of armored vehicles[J].Acta Armamentarii,2012,33(2):129-133.(in Chinese)
[4]毛航,王珂,王永庆,等.基于响应面分析法的百叶窗翅片结构优化设计[J].化工设备与管道,2015,52(1):23-27.MAO H,WANG K,WANG Y Q,et al.Optimum design of louvered fin structure based on response surface methodology[J].Process Equipment&Piping,2015,52(1):23-27.(in Chinese)
[5]张银亮,蔡惠坤,沈超.工程机械百叶窗翅片式散热器的多目标优化设计[J].中国工程机械学报,2016,14(6):508-514.ZHANG Y L,CAI H K,SHEN C.Multi-objective optimization for engineering machinery radiator with louvered fins[J].Chinese Journal of Construction Machinery,2016,14(6):508-514.(in Chinese)
[6]喻虎.基于流动与传热分析的装甲车辆动力舱冷却风道优化研究[D].北京:装甲兵工程学院,2013.YU H.Optimization of cooling air duct for armored vehicle power cabin based on flow and heat transfer analysis[D].Beijing:A-cademy of Armored Forces Engineering,2013.(in Chinese)
[7]陆荣,袁建平,李彦军,等.基于神经网络模型和CFD的轴流泵自动优化[J].排灌机械工程学报,2017,35(6):481-487.LU R,YUAN J P,LI Y J,et al.Automatic optimization of axial flow pump based on radial basis functions neural network and CFD[J].Journal of Drainage and Irrigation Machinery Engineering,2017,35(6):481-487.(in Chinese)
[8]王钦龙,王红岩,芮强.基于多目标遗传算法的高速履带车辆动力学模型参数修正研究[J].兵工学报,2016,37(6):969-978.WANG Q L,WANG H Y,RUI Q.Research on parameter updating of high mobility tracked vehicle dynamic model based on multi-objective genetic algorithm[J].Acta Armamentarii,2016,37(6):969-978.(in Chinese)
[9]骆清国,尹洪涛,刘红彬,等.基于遗传算法的风扇蜗壳结构优化研究[J].中国工程机械学报,2016,14(4):305-309.LUO Q G,YIN H T,LIU H B,et al.Genetic-algorithm-based optimization on fan volute structure[J].Chinese Journal of Construction Machinery,2016,14(4):305-309.(in Chinese)
[10]李剑锋,李国平,路波,等.旋涡流非接触气爪结构优化设计[J].中国机械工程,2018,29(5):519-525.LI J F,LI G F,LU B,et al.Optimal design of vortex flow noncontact pneumatic gripper structures[J].China Mechanical Engineering,2018,29(5):519-525.(in Chinese)
[11]陈小雄.基于遗传算法的汽车外形优化[D].重庆:重庆理工大学,2016.CHEN X X.Automobile shape optimization based on genetic algorithm[D].Chongqing:Chongqing University of Technology,2016.(in Chinese)
[12]段孟华.某柴油发动机缸内流场和燃烧的数值模拟分析[D].重庆:重庆理工大学,2016.DUAN M H.Numerical simulation of flow field and combustion in a diesel engine cylinder[D].Chongqing:Chongqing University of Technology,2016.(in Chinese)
[13]刘静乐.基于EDA和人工免疫系统的混合多目标优化算法[D].西安:西安电子科技大学,2012.LIU J L.Hybrid multi-objective optimizations based on EDA and AIS[D].Xi'an:Xidian University,2012.(in Chinese)
[14]秦仙蓉,潘杰,徐俭,等.塔式起重机结构有限元模型修正的响应面方法[J].振动与冲击,2018,37(6):244-250.QIN X R,PAN J,XU J,et al.Response surface method for the structural finite element model updating of tower cranes[J].Journal of Vibration and Shock,2018,37(6):244-250.(in Chinese)