Model updating of a nonlinear experimental vehicle using substructuring and unscented Kalman filtering
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Chatzi, Eleni N.
- Conference Paper
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This study establishes a computational framework for nonlinear finite element (FE) model updating of large-scale structures, through the exploration of two case studies pertaining to a spring-mass-damper chain model and to a laboratory vehicle with nonlinear suspensions. The proposed approach combines a substructuring model reduction approach with a near realtime system identification scheme, namely the Unscented Kalman Filter (UKF). The former aims at isolating and locally updating individual structural subsystems of a large-scale structure, while the latter, in contrast to other alternatives (e.g. the Extended Kalman Filter), offers a number of advantages in treating nonlinear systems, such as a derivative free calculation and a capacity for handling higher order nonlinearities. To this end, after formulating a detailed large-scale FE model for the vehicle frame substructure, a lumped model is adopted for the description of the nonlinear suspensions. Accordingly, a joint state and parameter estimation (JS&PE) problem is formulated on the basis of the lumped model. The proposed framework uses acceleration measurements from a limited number of sensors attached on the structure and a UKF observer for fusing these with a nonlinear FE substructure model, resulting into a JS&PE problem. The results indicate the validity of the proposed framework and motivate further implementation in large-scale structural systems with nonlinear components Show more
Book titleProceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering
Pages / Article No.
PublisherNational Technical University of Athens (NTUA)
Organisational unit03890 - Chatzi, Eleni / Chatzi, Eleni
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