Vibration analysis of structure with uncertainty using two- level Gaussian processes and Bayesian inference

13th International Conference on Motion and Vibration Control; 12th International Conference on Recent Advances in Structural Dynamics

Zhou, Kai^1 ; Liang, Gang^2 ; Tang, J.^1

Department of Mechanical Engineering, University of Connecticut, Storrs

CT

06269, United States^1

Laboratory Management Division, Shanghai Maritime University, Shanghai

201306, China^2

关键词: Bayesian inference;    Component mode synthesis;    Gaussian Processes;    High dimensionality;    Low fidelities;    Modal characteristics;    Probabilistic order;    Reduced model;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/744/1/012202/pdf
DOI  :  10.1088/1742-6596/744/1/012202

来源: IOP

【 摘 要 】

Vibration analysis of structure with uncertainty is computationally costly, especially when the finite element model involved has high dimensionality. In this research a combination of two-level Gaussian processes and Bayesian inference is employed to facilitate the development of an efficient and accurate probabilistic order-reduced model. We first employ the two-level Gaussian processes emulator to integrate together small amount of high- fidelity data from full-scale finite element analysis and large amount of low-fidelity data from order-reduced component mode synthesis (CMS) model to improve the response variation prediction. We then utilize the improved response variation prediction on modal characteristics to update the CMS model in the probabilistic sense. The effectiveness of this method is demonstrated through a case study.