Optimal Parameter Identification for Model Correlation Using Model Reduction Methods

Abstract

Classically, to achieve correlation between a dynamic test and a Finite Element Model (FEM), an experienced engineer chooses a small subset of input parameters and uses a model updating technique or engineering judgment to update the parameters until the error between the FEM and the test article is acceptable. To reduce the intricacy and difficulty of model correlation, model reduction methods such as the Discrete Empirical Interpolation Method (DEIM), and dime are implemented to reduce the scale of the problem by reducing the number of FEM parameters to its most critical ones. These model reduction methods serve to identify the critical parameters required to develop an accurate model with reduced engineering effort and computational resources. The insight gained using these methods is critical to develop an optimal, reduced parameter set that provides high correlation with minimal iterative costs. This can be seen as a particular approach to sensitivity analysis in the model updating community. The parameter set rankings derived from each method are evaluated by correlating each parameter set on five simulated test geometries. The methodology presented highlights the most valuable parameters for correlation, enabling a straightforward and computationally efficient model correlation approach.