Calibration, Validation and Uncertainty Quantification of Nominally Identical Car Subframes

Abstract

In this paper a finite element model, with over half a million degrees-of-freedom, of a car front subframe has been calibrated and validated against experimental MIMO data of several nominally identical components. The spread between the individual components has been investigated and is reported. Sensor positioning was performed with an extended effective independence method, using system gramians to reject sensors with redundant information. The Fisher information matrix was used in the identification of the most significant model calibration parameters. Validation of the calibrated model was performed to evaluated the difference between the nominal and calibrated model, and bootstrapping used to investigate the validity of the calibrated parameters. The parameter identification, calibration, validation and bootstrapping have been performed using the open-source MATLAB tool FEMcali.

Redundant Information Rejection in Sensor Localisation Using System Gramians

“…The mesh is also very dense and every node is placed in the candidate set to point out the difference between the two methods. For a more complex structure see [16], where this method was applied to an industrial sized structure with a more realistic candidate set with good results. For all three examples the first 10 flexible modes were selected and the algorithms were asked to find 20 triaxial sensor locations given one initial sensor location at the top right corner and one input at the same location perpendicular to the plate surface.…”

Model Updating of Multiple Nominally Identical Car Components

“…Successful results have been obtained in previous studies using this method on various structures, e.g. see [1,2,13,16,21,33]. In the calibration procedure, a parametrised surrogate model of the FE model around the nominal parameter setting is first created.…”