Title

Persistent Model Order Reduction for Complex Dynamical Systems Using Smooth Orthogonal Decomposition

Authors

Shahab Ilbeigi, University of Rhode Island
David Chelidze, University of Rhode IslandFollow

Document Type

Article

Date of Original Version

2017

Abstract

Full-scale complex dynamic models are not effective for parametric studies due to the inherent constraints on available computational power and storage resources. A persistent reduced order model (ROM) that is robust, stable, and provides high-fidelity simulations for a relatively wide range of parameters and operating conditions can provide a solution to this problem. The fidelity of a new framework for persistent model order reduction of large and complex dynamical systems is investigated. The framework is validated using several numerical examples including a large linear system and two complex nonlinear systems with material and geometrical nonlinearities. While the framework is used for identifying the robust subspaces obtained from both proper and smooth orthogonal decompositions (POD and SOD, respectively), the results show that SOD outperforms POD in terms of stability, accuracy, and robustness.

Citation/Publisher Attribution

Ilbeigi, S., & Chelidze, D. (2017). Persistent model order reduction for complex dynamical systems using smooth orthogonal decomposition. Mechanical Systems and Signal Processing, 96, 125-138. doi: 10.1016/j.ymssp.2017.04.005

Available at: https://doi.org/10.1016/j.ymssp.2017.04.005