A Krylov enhanced proper orthogonal decomposition method for frequency domain model reduction. Issue 2 (18th April 2017) - Binion, David; Chen, Xiaolin
Abstract : Purpose: This paper aims to describe a method for efficient frequency domain model order reduction. The method attempts to combine the desirable attributes of Krylov reduction and proper orthogonal decomposition (POD) and is entitled Krylov enhanced POD (KPOD). Design/methodology/approach: The KPOD method couples Krylov's moment-matching property with POD's data generalization ability to construct reduced models capable of maintaining accuracy over wide frequency ranges. The method is based on generating a sequence of state- and frequency-dependent Krylov subspaces and then applying POD to extract a single basis that generalizes the sequence of Krylov bases. Findings: The frequency response of a pre-stressed microelectromechanical system resonator is used as an example to demonstrate KPOD's ability in frequency domain model reduction, with KPOD exhibiting a 44 per cent efficiency improvement over POD. Originality/value: The results indicate that KPOD greatly outperforms POD in accuracy and efficiency, making the proposed method a potential asset in the design of frequency-selective applications.