摘要
目的通过自动寻优制定冲击环境后峰锯齿脉冲试验条件,避免冲击“过试验”。方法根据冲击响应谱的基本原理和谱型特征,提出利用对数距离量化评价2条谱型的吻合度。对冲击环境和后峰锯齿脉冲分别进行冲击响应谱分析,将2条冲击响应谱之间的对数距离最小作为优化目标,将后峰锯齿脉冲的波形参数作为优化参数,基于自适应差分进化算法,提出冲击环境后峰锯齿脉冲试验条件自动寻优方法。结果将所提方法分别应用于模拟冲击环境和实测冲击环境,均获得了合理的后峰锯齿脉冲试验条件,验证了所提方法的正确性。结论提出的基于自适应差分进化算法的冲击环境后峰锯齿脉冲试验条件自动寻优方法正确可行。
The work aims to propose an automatic optimization method for terminal peak sawtooth shock pulse test conditions of shock environment to avoid"over-test".Firstly,the logarithmic distance was proposed to quantitatively evaluate the goodness of fit between two shock response spectra according to both the basic principle and spectrum characteristics of shock response spectra.Secondly,shock response spectrum analysis was carried out on both the shock environment and the terminal peak sawtooth shock pulse.According to the analysis results obtained,via setting the logarithmic distance between the two spectra as the optimization goal and the wave parameters of terminal peak sawtooth shock pulse as the optimization parameters,the automatic optimization method for terminal peak sawtooth shock pulse test conditions of shock environment was established based on adaptive differential evolution algorithm.The proposed method was applied in a simulated shock environment and a measured shock environment respectively.The reasonable terminal peak sawtooth shock pulse test conditions obtained verified the correctness of the method proposed.As a consequence,the proposed automatic optimization method for terminal peak sawtooth pulse test conditions in shock environments based on adaptive differential evolution algorithm is correct and feasible.
作者
陈江攀
刘艳
王增凯
刘艺
孙立敏
CHEN Jiang-pan;LIU Yan;WANG Zeng-kai;LIU Yi;SUN Li-min(Beijing Institute of Electronic System Engineering,Beijing 100854,China)
出处
《装备环境工程》
CAS
2023年第12期135-141,共7页
Equipment Environmental Engineering
基金
中国航天科工集团第二研究院质量与技术基础自筹资金项目(E23A013)。
关键词
冲击环境
后峰锯齿脉冲
冲击响应谱
自适应差分进化算法
自动寻优
shock environment
terminal peak sawtooth shock pulse
shock response spectrum
adaptive differential evolution algorithm
automatic optimization