Research on Optimization of Louvered Fin with Hybrid Multi-objective Optimizations Based on EDA and AIS

doi:10.3969/j.issn.1000-1093.2019.04.003

Abstract

Abstract: 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. Key

Key words: armoredvehicle, enginecompartment, louveredfin, multi-objectiveoptimization, testdesign

CLC Number:

TJ810.2

LUO Qingguo, ZHAO Yao, GUI Yong, LIU Hongbin , SHUAI Gang. Research on Optimization of Louvered Fin with Hybrid Multi-objective Optimizations Based on EDA and AIS[J]. Acta Armamentarii, 2019, 40(4): 689-696.

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第40卷
第4期2019 年4月兵工学报ACTA
ARMAMENTARIIVol.40No.4Apr.2019

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