Model order reduction technique for large scale flow computations

Current progress in numerical methods and available computational power combined with industrial needs promote the development of more and more complex models. However, such models are, due to their complexity, expensive from the point of view of the data storage and the time necessary for their evaluation. The model order reduction (MOR) seeks to reduce the computational complexity of large scale models. We present an application of MOR to the problems originating in the finite volume (FV) discretization of incompressible Navier-Stokes equations. Our approach to MOR is based on the proper orthogonal decomposition (POD)\nwith Galerkin projection. Moreover, the problems arising from the nonlinearities present in the original model are adressed within the framework of the discrete empirical interpolation method (DEIM). We provide a link between the POD-DEIM based MOR and OpenFOAM, which is an open-source CFD toolbox capable of solving even industrial scale problems. The availability of a link between OpenFOAM and POD-DEIM based MOR enables a direct order reduction for large scale systems originating in the industrial practice