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D. Giagopoulos¹, D.-C. Papadioti², C. Papadimitriou² and S. Natsiavas³

¹Department of Mechanical Engineering, University of Western Macedonia, Kozani, Greece
²Department of Mechanical Engineering, University of Thessaly, Volos, Greece
³Department of Mechanical Engineering, Aristotle University, Thessaloniki, Thessaloniki, Greece

Keywords: system identification, Bayesian inference, nonlinear dynamics, substructuring.

Summary

Nonlinear modelling and parametric identification of an experimental vehicle model, are employed in this paper. The composite structure of the vehicle model is split into a frame substructure and to four support substructures. The frame substructure possesses linear properties determined through application of a finite element analysis and designed to exhibit a relatively large modal density. A method for modal identification and structural model updating are employed in order to develop a high fidelity finite element model of the vehicle substructure. On the other hand, the four support substructures including the wheels and suspensions components, possesses strongly nonlinear characteristics, accounting mainly for absorber damping nonlinearities. Then, a Bayesian uncertainty quantification and propagation framework is adopted in order to estimate the optimal values of the four support substructures model parameters. Uncertainty models of the nonlinear wheel and suspension components are identified using the experimentally obtained response spectra for each of the components tested separately. These uncertainties, integrated with uncertainties in the body of the experimental vehicle, are propagated to estimate the uncertainties of output quantities of interest for the combined wheel-suspension-frame system. The computational challenges are outlined and the effectiveness of the Bayesian UQ&P framework on the specific example structure is demonstrated. Finally the experimental results were compared to those from the numerical model for verification of the numerical procedure and for the improvement of the numerical modelling of the vehicle substructuring components.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 103 PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON SOFT COMPUTING TECHNOLOGY IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING Edited by: Y. Tsompanakis Paper 24 Bayesian Uncertainty Quantification and Propagation in Nonlinear Structural Dynamics D. Giagopoulos¹, D.-C. Papadioti², C. Papadimitriou² and S. Natsiavas³ ¹Department of Mechanical Engineering, University of Western Macedonia, Kozani, Greece ²Department of Mechanical Engineering, University of Thessaly, Volos, Greece ³Department of Mechanical Engineering, Aristotle University, Thessaloniki, Thessaloniki, Greece doi:10.4203/ccp.103.24 purchase the full-text of this paper Full Bibliographic Reference for this paper D. Giagopoulos, D.-C. Papadioti, C. Papadimitriou, S. Natsiavas, "Bayesian Uncertainty Quantification and Propagation in Nonlinear Structural Dynamics", in Y. Tsompanakis, (Editor), "Proceedings of the Third International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 24, 2013. doi:10.4203/ccp.103.24 Keywords: system identification, Bayesian inference, nonlinear dynamics, substructuring. Summary Nonlinear modelling and parametric identification of an experimental vehicle model, are employed in this paper. The composite structure of the vehicle model is split into a frame substructure and to four support substructures. The frame substructure possesses linear properties determined through application of a finite element analysis and designed to exhibit a relatively large modal density. A method for modal identification and structural model updating are employed in order to develop a high fidelity finite element model of the vehicle substructure. On the other hand, the four support substructures including the wheels and suspensions components, possesses strongly nonlinear characteristics, accounting mainly for absorber damping nonlinearities. Then, a Bayesian uncertainty quantification and propagation framework is adopted in order to estimate the optimal values of the four support substructures model parameters. Uncertainty models of the nonlinear wheel and suspension components are identified using the experimentally obtained response spectra for each of the components tested separately. These uncertainties, integrated with uncertainties in the body of the experimental vehicle, are propagated to estimate the uncertainties of output quantities of interest for the combined wheel-suspension-frame system. The computational challenges are outlined and the effectiveness of the Bayesian UQ&P framework on the specific example structure is demonstrated. Finally the experimental results were compared to those from the numerical model for verification of the numerical procedure and for the improvement of the numerical modelling of the vehicle substructuring components. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £42 +P&P)
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