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**Harvard**

Yaghoubi, V. och Abrahamsson, T. (2017) *Sparse polynomial chaos expansions of frequency response functions using stochastic frequency transformation*.

** BibTeX **

@article{

Yaghoubi2017,

author={Yaghoubi, Vahid and Abrahamsson, Thomas},

title={Sparse polynomial chaos expansions of frequency response functions using stochastic frequency transformation},

journal={Probabilistic Engineering Mechanics},

issn={0266-8920},

abstract={Frequency response functions (FRFs) are important for assessing the behavior of stochastic linear dynamic systems. For large systems, their evaluations are time-consuming even for a single simulation. In such cases, uncertainty quantification by crude Monte-Carlo simulation is not feasible. In this paper, we propose the use of sparse adaptive polynomial chaos expansions (PCE) as a surrogate of the full model. To overcome known limitations of PCE when applied to FRF simulation, we propose a frequency transformation strategy that maximizes the similarity between FRFs prior to the calculation of the PCE surrogate. This strategy results in lower-order PCEs for each frequency. Principal component analysis is then employed to reduce the number of random outputs. The proposed approach is applied to two case studies: a simple 2-DOF system and a 6-DOF system with 16 random inputs. The accuracy assessment of the results indicates that the proposed approach can predict single FRFs accurately. Besides, it is shown that the first two moments of the FRFs obtained by the PCE converge to the reference results faster than with the Monte-Carlo (MC) methods.},

year={2017},

keywords={ Polynomial chaos expansions, Frequency response functions, Stochastic frequency-transformation, Uncertainty quantification, Principal component analysis},

}

** RefWorks **

RT Journal Article

SR Electronic

ID 249447

A1 Yaghoubi, Vahid

A1 Abrahamsson, Thomas

T1 Sparse polynomial chaos expansions of frequency response functions using stochastic frequency transformation

YR 2017

JF Probabilistic Engineering Mechanics

SN 0266-8920

AB Frequency response functions (FRFs) are important for assessing the behavior of stochastic linear dynamic systems. For large systems, their evaluations are time-consuming even for a single simulation. In such cases, uncertainty quantification by crude Monte-Carlo simulation is not feasible. In this paper, we propose the use of sparse adaptive polynomial chaos expansions (PCE) as a surrogate of the full model. To overcome known limitations of PCE when applied to FRF simulation, we propose a frequency transformation strategy that maximizes the similarity between FRFs prior to the calculation of the PCE surrogate. This strategy results in lower-order PCEs for each frequency. Principal component analysis is then employed to reduce the number of random outputs. The proposed approach is applied to two case studies: a simple 2-DOF system and a 6-DOF system with 16 random inputs. The accuracy assessment of the results indicates that the proposed approach can predict single FRFs accurately. Besides, it is shown that the first two moments of the FRFs obtained by the PCE converge to the reference results faster than with the Monte-Carlo (MC) methods.

LA eng

LK http://dx.doi.org/10.1016/j.probengmech.2017.04.003

OL 30