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

TL;DR

This paper introduces a parametric model order reduction method for large-scale thermal systems, enabling efficient updates of simplified models with parameter changes while maintaining accuracy.

Contribution

It presents a Krylov subspace-based pMOR technique that directly incorporates parameter variations into reduced models for complex thermal systems.

Findings

Efficient parametric updates of reduced models.

Error bounds for approximation accuracy.

Application to large-scale thermal systems.

Abstract

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.