A Structured Quasi-Arnoldi procedure for model order reduction of second-order systems

A Structured Quasi-Arnoldi procedure for model order reduction of second-order systems

Article history: Received 3 June 2010 Accepted 5 July 2011 Available online 20 August 2011 Submitted by V. Mehrmann Dedicated to Danny Sorensen on the occasion of his 65th birthday

Dimension Reduction of Large-Scale Second-Order Dynamical Systems via a Second-Order Arnoldi Method

A structure-preserving dimension reduction algorithm for large-scale second-order dynamical systems is presented. It is a projection method based on a second-order Krylov subspace. A second-order Arnoldi (SOAR) method is used to generate an orthonormal basis of the projection subspace. The reduced system not only preserves the second-order structure but also has the same order of approximation ...

6 Model Reduction of Second - Order Systems

Model reduction of second order systems

where the matrix M ∈ RN×N is assumed to be invertible. Models of mechanical systems are often of this type since (1.1) then represents the equation of motion of the system. For such a system M = MT , C = CT and K = KT are respectively the mass, damping and stiffness matrices, f(t) ∈ RN×1 is the vector of external forces, and x(t) ∈ RN×1 is the vector of internal generalized coordinates (see [4]...

Error Estimation for Arnoldi-based Model Order Reduction of MEMS

In this paper we present two different, heuristic error estimates for the Pade-type approximation of transfer functions via an Arnoldi algorithm. We first suggest a convergence criterion between two successive reduced models of the order and . We further propose to use the solution of the Lyapunov equations for reduced-order systems as a stop-criterion during iterative model order reduction.