Title of article
Two-level discretizations of nonlinear closure models for proper orthogonal decomposition
Author/Authors
Wang، نويسنده , , Z. and Akhtar، نويسنده , , I. and Borggaard، نويسنده , , J. and Iliescu، نويسنده , , T.، نويسنده ,
Issue Information
روزنامه با شماره پیاپی سال 2011
Pages
21
From page
126
To page
146
Abstract
Proper orthogonal decomposition has been successfully used in the reduced-order modeling of complex systems. Its original promise of computationally efficient, yet accurate approximation of coherent structures in high Reynolds number turbulent flows, however, still remains to be fulfilled. To balance the low computational cost required by reduced-order modeling and the complexity of the targeted flows, appropriate closure modeling strategies need to be employed. Since modern closure models for turbulent flows are generally nonlinear, their efficient numerical discretization within a proper orthogonal decomposition framework is challenging. This paper proposes a two-level method for an efficient and accurate numerical discretization of general nonlinear closure models for proper orthogonal decomposition reduced-order models. The two-level method computes the nonlinear terms of the reduced-order model on a coarse mesh. Compared with a brute force computational approach in which the nonlinear terms are evaluated on the fine mesh at each time step, the two-level method attains the same level of accuracy while dramatically reducing the computational cost. We numerically illustrate these improvements in the two-level method by using it in three settings: the one-dimensional Burgers equation with a small diffusion parameter ν = 10−3, the two-dimensional flow past a cylinder at Reynolds number Re = 200, and the three-dimensional flow past a cylinder at Reynolds number Re = 1000.
Keywords
Two-level discretization , Proper orthogonal decomposition , Nonlinear closure models
Journal title
Journal of Computational Physics
Serial Year
2011
Journal title
Journal of Computational Physics
Record number
1483020
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