A bi-level approximation tool for the computation of FRFs in stochastic dynamic systems. (March 2016)

• Record Type: Journal Article
• Authors: Chatterjee, Tanmoy; Chakraborty, Souvik; Chowdhury, Rajib
• Is Part Of: Mechanical systems and signal processing. Volume 70/71(2016)
• Page Start: 484
• Page End: 505
• Publication Date: 2016-03
• Subjects: Hybrid PCFE -- Frequency response function -- Stochastic dynamics -- Gaussian process -- Homotopy algorithm; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2015.09.001
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: Frequency response functions (FRFs) are considered to be a significant aspect in the evaluation of structural response subjected to dynamic loading. A new approach, referred to as the hybrid polynomial correlated function expansion (H-PCFE) has been developed for predicting the natural frequencies and the FRF of stochastic dynamical systems. H-PCFE has been developed by incorporating the advantages of two available techniques namely, PCFE and Gaussian process (GP) modeling. These two methods are coupled in such a way that PCFE handles the global behavior of the model using a set of component functions and GP interpolates local variations as a function of the sample points, performing as a two level approximation. Implementation of the proposed approach for stochastic dynamic problems has been demonstrated with four problems. The main focus of this study lies in the prediction of FRFs. The efficiency and accuracy of H-PCFE to compute FRFs of stochastic dynamic systems is assessed by a comparison with direct Monte Carlo simulation (MCS). Excellent results in terms of accuracy and computational effort obtained makes the proposed methodology potential for application in large scale structural applications. Highlights: A novel tool for computing FRFs of stochastic system is presented. It utilizes bi-level approximations, firstly on global and secondly on local scale. It is accurate as well as efficient. It has huge potential for large scale applications in various fields.