Improving the predictable accuracy of fluid Galerkin reduced-order models using two POD bases

Published

Journal Article

© 2020, Springer Nature B.V. A fundamental limitation of fluid flow reduced-order models (ROMs) which utilize the proper orthogonal decomposition is that there is little capability to determine one’s confidence in the fidelity of the ROM a priori. One reason why fluid ROMs are plagued by this issue is that nonlinear fluid flows are fundamentally multi-scale, often chaotic dynamical systems and a single linear spatial basis, however carefully selected, is incapable of ensuring that these characteristics are captured. In this paper, the velocity and the velocity gradient were decomposed using differently optimized linear bases. This enabled an optimization for several dynamically significant flow characteristics within the modal bases. This was accomplished while still ensuring the resulting model is accurate and without iterative methods for constructing the modal bases.

Full Text

Duke Authors

Cited Authors

  • Lee, MW; Dowell, EH

Published Date

  • July 1, 2020

Published In

Volume / Issue

  • 101 / 2

Start / End Page

  • 1457 - 1471

Electronic International Standard Serial Number (EISSN)

  • 1573-269X

International Standard Serial Number (ISSN)

  • 0924-090X

Digital Object Identifier (DOI)

  • 10.1007/s11071-020-05833-x

Citation Source

  • Scopus