Reduced order surrogate modeling technique for linear dynamic systems
Artikel i vetenskaplig tidskrift, 2018

The availability of reduced order models can greatly decrease the computational costs needed for modeling, identification and design of real-world structural systems. However, since these systems are usually employed with some uncertain parameters, the approximant must provide a good accuracy for a range of stochastic parameters variations. The derivation of such reduced order models are addressed in this paper. The proposed method consists of a polynomial chaos expansion (PCE)-based state-space model together with a PCE-based modal dominancy analysis to reduce the model order. To solve the issue of spatial aliasing during mode tracking step, a new correlation metric is utilized. The performance of the presented method is validated through four illustrative benchmarks: a simple mass-spring system with four Degrees Of Freedom (DOF), a 2-DOF system exhibiting a mode veering phenomenon, a 6-DOF system with large parameter space and a cantilever Timoshenko beam resembling large-scale systems.

Polynomial chaos expansion

Parametric model reduction

Mode veering

Modal observability correlation

Surrogate model

Modal dominancy analysis


Vahid Yaghoubi Nasrabadi

Isfahan University of Technology

Sadegh Rahrovani

Chalmers, Mekanik och maritima vetenskaper, Dynamik

Hassan Nahvi

Isfahan University of Technology

Stefano Marelli

Eidgenössische Technische Hochschule Zürich (ETH)

Mechanical Systems and Signal Processing

0888-3270 (ISSN) 1096-1216 (eISSN)

Vol. 111 172-193







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