Stochastic model order reduction in uncertainty quantification of composite structures

2015 ◽  
Vol 128 ◽  
pp. 21-34 ◽  
Author(s):  
P. Sasikumar ◽  
R. Suresh ◽  
Sayan Gupta
Keyword(s):  
Stochastic Model ◽  
Uncertainty Quantification ◽  
Composite Structures ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Model Order

scholarly journals A New MEMS Stochastic Model Order Reduction Method: Research and Application

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Bian Xiangjuan ◽  
Youping Gong ◽  
Chen Guojin ◽  
Lv Yunpeng
Keyword(s):  
Finite Element ◽  
Stochastic Model ◽  
Model Order Reduction ◽  
Large Scale ◽  
Reduction Method ◽  
Order Reduction ◽  
Fast Time ◽  
Random Input ◽  
Model Order ◽  
Order Reduction Method

Modeling and simulation of MEMS devices is a very complex tasks which involve the electrical, mechanical, fluidic, and thermal domains, and there are still some uncertainties that need to be accounted for during the robust design of MEMS actuators caused by uncertain material and/or geometric parameters. According to these problems, we put forward stochastic model order reduction method under random input conditions to facilitate fast time and frequency domain analyses; the method makes use of polynomial chaos expansions in terms of the random input variables for the matrices of a finite element model of the system and then uses its transformation matrix to reduce the model; the method is independent of the MOR algorithm, so it is seamlessly compatible with MOR method used in popular finite element solvers. The simulation results verify the method is effective in large scale MEMS design process.

MEMS stochastic model order reduction method based on polynomial chaos expansion

2015 ◽  
Vol 22 (5) ◽  
pp. 993-1003 ◽  
Author(s):  
Youping Gong ◽  
Xiangjuan Bian ◽  
Chen Guojin ◽  
Lv Yunpeng ◽  
Zhangming Peng
Keyword(s):  
Stochastic Model ◽  
Model Order Reduction ◽  
Polynomial Chaos ◽  
Reduction Method ◽  
Order Reduction ◽  
Polynomial Chaos Expansion ◽  
Chaos Expansion ◽  
Model Order ◽  
Order Reduction Method

Deterministic and Stochastic Model Order Reduction for Vibration Analyses of Structures With Uncertainties

2017 ◽  
Vol 139 (2) ◽  
Author(s):  
Ji Yang ◽  
Béatrice Faverjon ◽  
Herwig Peters ◽  
Steffen Marburg ◽  
Nicole Kessissoglou
Keyword(s):  
Finite Element ◽  
Stochastic Model ◽  
Dynamic Characteristics ◽  
Model Order Reduction ◽  
Krylov Subspace ◽  
Order Reduction ◽  
Element Model ◽  
Uncertain Parameters ◽  
Model Order ◽  
Frequency Responses

To reduce the computational effort using polynomial chaos expansion to predict the dynamic characteristics of structures with several uncertain parameters, hybrid techniques combining stochastic finite element analysis with either deterministic or stochastic model order reduction (MOR) are developed. For the deterministic MOR, the Arnoldi-based Krylov subspace technique is implemented to reduce the system matrices of the finite element model. For the stochastic MOR, a stochastic reduced basis method is implemented in which the structural modal and frequency responses are approximated by a small number of basis vectors using stochastic Krylov subspace. To demonstrate the computational efficiency of each reduced stochastic finite element model, variability in the natural frequencies and frequency responses of a simply supported flexible plate randomized by uncertain geometrical and material parameters is examined. Results are compared with both Monte Carlo (MC) simulations and nonreduced stochastic models. Using the reduced models, the effects of the individual uncertain parameters as well as the combined uncertainties on the dynamic characteristics of the plate are examined.

Sign in / Sign up

Export Citation Format

Share Document