A Second Order Arnoldi Method with Stopping Criterion and Reduced Order Selection for Reducing Second Order Systems
Received: 10 April 2022 | Revised: 21 April 2022 | Accepted: 26 April 2022 | Online: 4 May 2022
Corresponding author: A. Tamri
Abstract
This paper introduces a new algorithm for reducing large dimensional second-order dynamic systems through the Second Order Arnold Reduction (SOAR) procedure, with a stopping criterion to select an acceptable good order for the reduced model based on a new coefficient called the Numerical-Rank Performance Coefficient (NRPC), for efficient early termination and automatic optimal order selection of the reduced model. The key idea of this method is to calculate the NRPC coefficient for each iteration of the SOAR algorithm and measure the dynamic evolution information of the original system, which is added to each vector of the Krylov subspace generated by the SOAR algorithm. When the dynamical tolerance condition is verified, the iterative procedure of the algorithm stops. Three benchmark models were used as numerical examples to check the effectiveness and simplicity of the proposed algorithm.
Keywords:
Model order reduction,, Second-order systems, Second-order Krylov sub-spaces, second-order Arnoldi procedure (SOAR), Structure preserving, Stability, Projection, State SpaceDownloads
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