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CFD motivated applications of model order reduction
Isoz, Martin
The ongoing advances in numerical mathematics and available computing power combined with the industrial needs promote a 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 approach to MOR based on the proper orthogonal decomposition (POD) with Galerkin projection. The problems arising from the nonlinearities present in the original model are adressed within the framework of the discrete empirical interpolation method (DEIM). The main contribution of this work consists in providing a link between the POD-DEIM based MOR and OpenFOAM. OpenFOAM is an open-source CFD toolbox capable of solving even industrial scale processes. Hence, 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.

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