摘要
为了确定油管式汽车动态称重仪的仿真材料参数,根据油管的主要作用采用VPG技术中的简化方法,将钢丝/橡胶复合层视为弹性材料,该弹性材料的待定参数为弹性模量E和泊松比μ。依据反演的基本原理设计了获取观测数据的试验方案,将有限元法与人工神经网络两种计算方法相结合,对参数E和μ进行了反演。最后将反演结果代入有限元模型中进行计算,其结果与实测数据吻合较好。
Considering load-transfer as the main function of oil hose, and adopting the simplified method used in virtual proving ground （VPG） technique for tire, the steel-wire/rubber compound layer is treated as isotropic elastic rather than composite material with the elastic modulus E and Poisson＇s ratio μ of the elastic material as the parameters to be determined. According to the theory of inversion, a scheme for＇ acquiring observed data is designed. By combining finite element analysis with artificial neural network algorithm, the inversion of parameters E and μ is performed. The results of finite element analysis with the values of E and μ obtained from parameter inversion are compared with that of experiments, showing good agreement.
出处
《汽车工程》
EI
CSCD
北大核心
2006年第5期487-490,共4页
Automotive Engineering
基金
江苏省科技厅高新技术研究项目(BG2003019)资助
关键词
动态称重
钢丝编织胶管
BP神经网络
参数反演
Weigh-in-motion, Wire braided hose, BP neural network, Parameter inversion
