Parametric model order reduction for large-scale and complex thermal systems

Daming Lou, Siep Weiland

In this paper, a parametric model order reduction (pMOR) technique is proposed to find a simplified system representation of a large-scale and complex thermal system. The main principle behind this technique is that any change of the physical parameters in the high-fidelity model can be updated directly in the simplified model. For deriving the parametric reduced model, a Krylov subspace method is employed which yields the relevant subspaces of the projected state. With the help of the projection operator, first moments of the low-rank model are set identical to the correspondent moments of the original model. Additionally, a prior upper bound of the error induced by the approximation is derived.

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