基于全局敏感性的自适应模型更新混合模拟方法

An adaptive model updating method for hybrid simulation based on global sensitivity analysis

基于全局敏感性(global sensitivity, GS)的模型更新混合模拟方法可以有效提升混合模拟精度并拥有较高的计算效率,然而该方法易陷入局部最优值。为解决此问题,在GS方法的基础上引入了动量和自适应步长的概念,提出了基于全局敏感性的自适应(global sensitivity adaptive, GS-A)模型更新混合模拟方法。以钢框架模型为例,对8个本构参数和模型几何参数进行参数识别,利用均匀设计试验方法,建立11个模型更新案例,分别进行了不更新、GS方法和GS-A方法的数值模拟分析。33组数值模拟案例参数识别过程、位移时程的对比分析表明:GS-A方法相对于GS方法,解决了参数组合陷入局部最优的问题,具有较高参数识别的精度和稳定性,提升了模型更新效果。

The model updating for hybrid simulation method based on global sensitivity(GS)analysis can effectively improve the accuracy of hybrid simulation with high computing efficiency.However,this always makes it fall into the local optimal value.To address this issue,momentum and adaptive step size was introduced,and an adaptive(GS-A)model updating hybrid simulation method was proposed based on global sensitivity analysis.The steel frame model was used as the analysis object,and the eight parameters including both the constitutive and model geometric ones were identified.The uniform design test was used to establish 11 model update cases and numerical simulations were performed for the hybrid simulation with no updating,the GS method,and the GS-A method,respectively.The parameter identification process,displacement time course and hysteresis curves of the 33 sets of numerical simulation cases were analyzed and compared.Results show that the GS-A method,compared with the GS method,solves the problem of the parameters falling into the local optimal value,performs higher accuracy and stability of parameter identification,and improves the model updating effect.