4.6 Article

A SPACE-TIME PETROV-GALERKIN CERTIFIED REDUCED BASIS METHOD: APPLICATION TO THE BOUSSINESQ EQUATIONS

期刊

SIAM JOURNAL ON SCIENTIFIC COMPUTING
卷 36, 期 1, 页码 A232-A266

出版社

SIAM PUBLICATIONS
DOI: 10.1137/120903300

关键词

space-time Petrov-Galerkin; parametrized parabolic equations; discontinuous Galerkin; certified reduced basis; Brezzi-Rappaz-Raviart theory; Boussinesq equations

资金

  1. OSD/AFOSR/MURI [FA9550-09-1-0613]
  2. ONR [N00014-11-1-0713]

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We present a space-time certified reduced basis method for long-time integration of parametrized parabolic equations with quadratic nonlinearity which admit an affine decomposition in parameter but with no restriction on coercivity of the linearized operator. We first consider a finite element discretization based on discontinuous Galerkin time integration and introduce associated Petrov-Galerkin space-time trial-and test-space norms that yield optimal and asymptotically mesh independent stability constants. We then employ an hp Petrov-Galerkin (or minimum residual) space-time reduced basis approximation. We provide the Brezzi-Rappaz-Raviart a posteriori error bounds which admit efficient offline-online computational procedures for the three key ingredients: the dual norm of the residual, an inf-sup lower bound, and the Sobolev embedding constant. The latter are based, respectively, on a more round-off resistant residual norm evaluation procedure, a variant of the successive constraint method, and a time-marching implementation of a fixed-point iteration of the embedding constant for the discontinuous Galerkin norm. Finally, we apply the method to a natural convection problem governed by the Boussinesq equations. The result indicates that the space-time formulation enables rapid and certified characterization of moderate-Grashof-number flows exhibiting steady periodic responses. However, the space-time reduced basis convergence is slow, and the Brezzi-Rappaz-Raviart threshold condition is rather restrictive, such that offline effort will be acceptable only for very few parameters.

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