Physically constrained data-driven correction for reduced-order modeling of fluid flows

We have recently proposed a data-driven correction reduced-order model (DDC-ROM) framework for the numerical simulation of fluid flows, which can be formally written as follows. The new DDC-ROM was constructed by using reduced-order model spatial filtering and data-driven reduced-order model closure modeling (for the Correction term) and was successfully tested in the numerical simulation of a two-dimensional channel flow past a circular cylinder at Reynolds numbers Re = 100,Re = 500, and Re = 1000. In this paper, we propose a physically constrained DDC-ROM (CDDC-ROM), which aims at improving the physical accuracy of the DDC-ROM. The new physical constraints require that the CDDC-ROM operators satisfy the same type of physical laws (ie, the Correction term's linear component should be dissipative, and the Correction term's nonlinear component should conserve energy) as those satisfied by the fluid flow equations. To implement these physical constraints, in the data-driven modeling step, we replace the unconstrained least squares problem with a constrained least squares problem. We perform a numerical investigation of the new CDDC-ROM and standard DDC-ROM for a two-dimensional channel flow past a circular cylinder at Reynolds numbers Re = 100,Re = 500, and Re = 1000. To this end, we consider a reproductive regime as well as a predictive (ie, cross-validation) regime in which we use as little as 50% of the original training data. The numerical investigation clearly shows that the new CDDC-ROM is significantly more accurate than the DDC-ROM in both regimes.