基于敏度分析的考虑动态再结晶的黏塑性本构模型的全局可识别分析

提出了一套能够考虑约束影响的大型复杂模型的全局参数可识别分析方法. 该方法基于全局敏度矩阵、全局Fiseher信息矩阵及全局归一化Fisher信息矩阵,可识别性指标包括敏度指数、共线性指数及特征值指标. 通过统计方法将局部指标集合成全局指标;约束通过罚函数方法引入,罚系数通过基于全局竞争排序的方法确定;应用提出的方法对考虑动态再结晶的黏塑性本构模型进行全局可识别分析. 分析结果表明,由于参数之间存在较强相关性,模型中的部分参数很难识别.

Global Identifiability Analysis of Macro-Micro Coupled Viscoplastic Constitutive Model Considering Dynamic Recrystallization Based on Sensitivity

A systematic global identifiability analysis method of large and complex models was proposed. The method was based on the global sensitivity, Fisher matrix and the normalized Fisher information matrix and could take the effect of constraint into account. The identifiability measure includes sensitivity index, collinearity index and determinant index. Firstly the statistical approach was applied to integrate the local matrix into the corresponding global matrix. The constraint was introduced into the objective function through the penalty function method and the penalty coefficient was determined by the global competitive ranking method. Secondly, the global identifiability analysis on the macro-micro coupled viscoplastic constitutive model considering dynamic recrystallization was taken. The result shows that the identifiability is poor due to high correlation coefficient between some parameters.