Improved model correlation through optimal parameter ranking using model reduction algorithms: Augmenting engineering judgment

摘要

As the complexity and scales of dynamic models increase, novel and efficient model correlation methodologies are vital to the development of accurate models. Classically, to correlate a Finite Element Model (FEM) such that it matches a dynamic test, an experienced engineer chooses a small subset of input parameters that are surmised to be crucial, sensitive and/or possibly erroneous. The operator will then use engineering judgment, or a model updating technique to update the selected subset of parameters until the error between the FEM and the test article is reduced to within a set bound. To reduce the intricacy and difficulty of model correlation, a methodology is proposed to provide a quantitative parameter importance ranking using a model reduction algorithm applied to a parameter sensitivity analysis. Four model reduction algorithms are studied in this effort, the Discrete Empirical Interpolation Method (SVD-DEIM), Q-DEIM, Projection Coefficient and finally Weighted Projection Coefficient. These model reduction methods identify and rank critical parameters, enabling the selection of a minimum set of critical correlation parameters. This reduced set of parameters results in reduced computational resources and engineering effort required to generate a correlated model. The insight gained using these methods is essential in developing an optimal, reduced parameter set that provides high correlation capability with minimal iterative costs. To evaluate the proposed parameter selection methodology, a representative set of academic and industry experts provided their engineering judgment for comparison with the methodology presented. A comprehensive investigation of the robustness of this methodology is performed on a simple cantilever beam for demonstration. The scale of the model has expressly been chosen to allow for all potential ranking variations to be evaluated so that these ranking methods can be understood relative to the true optimal ranking. The ranking rob