Abstract

This work proposes a domain adaptive stochastic collocation approach for uncertainty quantification, suitable for effective handling of discontinuities or sharp variations in the random domain. The basic idea of the proposed methodology is to adaptively decompose the random domain into subdomains. Within each subdomain, a sparse grid interpolant is constructed using the classical Smolyak construction [S. Smolyak, Quadrature and interpolation formulas for tensor products of certain classes of functions, Soviet Math. Dokl. 4 (1963) 240-243], to approximate the stochastic solution locally. The adaptive strategy is governed by the hierarchical surpluses, which are computed as part of the interpolation procedure. These hierarchical surpluses then serve as an error indicator for each subdomain, and lead to subdivision whenever it becomes greater than a threshold value. The hierarchical surpluses also provide information about the more important dimensions, and accordingly the random elements can be split along those dimensions. The proposed adaptive approach is employed to quantify the effect of uncertainty in input parameters on the performance of micro-electromechanical systems (MEMS). Specifically, we study the effect of uncertain material properties and geometrical parameters on the pull-in behavior and actuation properties of a MEMS switch. Using the adaptive approach, we resolve the pull-in instability in MEMS switches. The results from the proposed approach are verified using Monte Carlo simulations and it is demonstrated that it computes the required statistics effectively.

Original languageEnglish (US)
Pages (from-to)7662-7688
Number of pages27
JournalJournal of Computational Physics
Volume228
Issue number20
DOIs
StatePublished - Nov 1 2009

Fingerprint

collocation
microelectromechanical systems
MEMS
interpolation
Interpolation
switches
Switches
subdivisions
actuation
quadratures
Tensors
Materials properties
discontinuity
grids
Statistics
statistics
tensors
methodology
thresholds
products

Keywords

  • Adaptive sampling
  • Geometrical uncertainty
  • Multiphysics
  • Reliability
  • Sparse grids
  • Stochastic collocation
  • Stochastic Galerkin method
  • Uncertainty propagation

ASJC Scopus subject areas

  • Computer Science Applications
  • Physics and Astronomy (miscellaneous)

Cite this

A domain adaptive stochastic collocation approach for analysis of MEMS under uncertainties. / Agarwal, Nitin; Aluru, N. R.

In: Journal of Computational Physics, Vol. 228, No. 20, 01.11.2009, p. 7662-7688.

Research output: Contribution to journalArticle

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