Smooth robust subspace based model reduction

Document Type

Conference Proceeding

Date of Original Version



Long-time numerical simulations of large-scale mechanistic models of complex systems (e.g., molecular dynamics, computational fluid dynamics, structural finite element, or multi-body dynamics models) are still problematic, either due to numerical instabilities or the excessive necessary computational resources. Therefore, reduced models that can be simulated for long-time and provide truthful approximations to the actual longtime dynamics, are needed. A new framework-based on new concepts of dynamical consistency and subspace robustness- for identifying subspaces suitable for reduced-order model development is presented. Model reductions based on proper and smooth orthogonal decompositions (POD and SOD, respectively) are considered and tested using a nonlinear four-degreeof- freedom model. It is shown that the new framework identifies subspaces that provide accurate model reductions for a range of forcing parameters, and that only four and higher dimensional models could be dynamically consistent. In addition, for reduced-order models based on randomly driven data, a fourdimensional SOD-based model outperformed a five-dimensional POD-based model. Finally, randomly driven data-based models generally outperformed harmonically driven data-based models when tested for a wide range of forcing amplitudes. Copyright © 2013 by ASME.

Publication Title, e.g., Journal

Proceedings of the ASME Design Engineering Technical Conference


7 B