Reduced order surrogate modeling technique for linear dynamic systems

Journal article, 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.