TY - GEN
T1 - Uncertainty quantification in modal characteristics of viscoelastic damping structures by using integration approach of adaptive dimension reduction method and stochastic collocation method
AU - Wang, T.
AU - Xu, C.
AU - Guo, N.
N1 - Publisher Copyright:
© 2020 Proceedings of ISMA 2020 - International Conference on Noise and Vibration Engineering and USD 2020 - International Conference on Uncertainty in Structural Dynamics. All rights reserved.
PY - 2020
Y1 - 2020
N2 - The adaptive dimension reduction method is combined with a stochastic collocation method to quantitatively estimate the variability of modal characteristics in viscoelastic damping structures. For the deterministic solver, the layer-wise finite element is employed to model the viscoelastic damping structures and the shift-invert iteration method is used to solve nonlinear eigenvalue problem. In the uncertainty quantification phase, the high-dimensional model is first decomposed into several low-dimensional component models by the adaptive dimension reduction method. Then, in each component model, the stochastic collocation method (SC) is used for constructing the approximate model with minimal model evaluations. The integration approach is illustrated on two examples. It is shown that the integration approach has efficiency and accuracy advantages over Monte Carlo Simulation and SC method.
AB - The adaptive dimension reduction method is combined with a stochastic collocation method to quantitatively estimate the variability of modal characteristics in viscoelastic damping structures. For the deterministic solver, the layer-wise finite element is employed to model the viscoelastic damping structures and the shift-invert iteration method is used to solve nonlinear eigenvalue problem. In the uncertainty quantification phase, the high-dimensional model is first decomposed into several low-dimensional component models by the adaptive dimension reduction method. Then, in each component model, the stochastic collocation method (SC) is used for constructing the approximate model with minimal model evaluations. The integration approach is illustrated on two examples. It is shown that the integration approach has efficiency and accuracy advantages over Monte Carlo Simulation and SC method.
UR - http://www.scopus.com/inward/record.url?scp=85105819892&partnerID=8YFLogxK
M3 - 会议稿件
AN - SCOPUS:85105819892
T3 - Proceedings of ISMA 2020 - International Conference on Noise and Vibration Engineering and USD 2020 - International Conference on Uncertainty in Structural Dynamics
SP - 3655
EP - 3666
BT - Proceedings of ISMA 2020 - International Conference on Noise and Vibration Engineering and USD 2020 - International Conference on Uncertainty in Structural Dynamics
A2 - Desmet, W.
A2 - Pluymers, B.
A2 - Moens, D.
A2 - Vandemaele, S.
PB - KU Leuven - Departement Werktuigkunde
T2 - 2020 International Conference on Noise and Vibration Engineering, ISMA 2020 and 2020 International Conference on Uncertainty in Structural Dynamics, USD 2020
Y2 - 7 September 2020 through 9 September 2020
ER -