Abstract
We present a method for linear stability analysis of systems with parametric uncertainty formulated in the stochastic Galerkin framework. Specifically, we assume that for a model partial differential equation, the parameter is given in the form of generalized polynomial chaos expansion. The stability analysis leads to the solution of a stochastic eigenvalue problem, and we wish to characterize the rightmost eigenvalue. We focus, in particular, on problems with nonsymmetric matrix operators, for which the eigenvalue of interest may be a complex conjugate pair, and we develop methods for their efficient solution. These methods are based on inexact, line-search Newton iteration, which entails use of preconditioned GMRES. The method is applied to linear stability analysis of the Navier{Stokes equations with stochastic viscosity, its accuracy is compared to that of Monte Carlo and stochastic collocation, and the efficiency is illustrated by numerical experiments.
Original language | English (US) |
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Pages (from-to) | 1101-1129 |
Number of pages | 29 |
Journal | SIAM-ASA Journal on Uncertainty Quantification |
Volume | 10 |
Issue number | 3 |
DOIs | |
State | Published - 2022 |
Keywords
- Navier-Stokes equation
- eigenvalue analysis
- linear stability
- preconditioning
- spectral stochastic finite element method
- stochastic Galerkin method
- uncertainty quantification
ASJC Scopus subject areas
- Statistics and Probability
- Modeling and Simulation
- Statistics, Probability and Uncertainty
- Discrete Mathematics and Combinatorics
- Applied Mathematics