- Conference Paper
In many areas of engineering, nonlinear numerical analysis is playing an increasingly important role in supporting the design and monitoring of structures. Whilst increasing computer resources have made such formerly prohibitive analyses possible, certain use cases such as uncertainty quantification and real time high-precision simulation remain computationally challenging. This motivates the development of reduced order modelling methods, which can reduce the computational toll of simulations relying on mechanistic principles. The majority of existing reduced order modelling techniques involve projection onto linear bases. Such methods are well established for linear systems but when considering nonlinear systems their application becomes more difficult. Targeted schemes for nonlinear systems are available, which involve the use of multiple linear reduction bases or the enrichment of traditional bases. These methods are however generally limited to weakly nonlinear systems. In this work, nonlinear normal modes (NNMs) are demonstrated as a possible invertible reduction basis for nonlinear systems. The extraction of NNMs from output only data using machine learning methods is demonstrated and a novel NNM-based reduced order modelling scheme introduced. The method is demonstrated on a simulated example of a nonlinear 20 degree-of-freedom (DOF) system. Show more
Book titleEURODYN 2020. Proceedings of the XI International Conference on Structural Dynamics. Volume II
Journal / seriesEASD Procedia
Pages / Article No.
PublisherEuropean Association for Structural Dynamics (EASD)
SubjectNNMs; ROMs; Autoencoders; Dynamics
Organisational unit03890 - Chatzi, Eleni / Chatzi, Eleni
764547 - Dynamic virtualisation: modelling performance of engineering structures (EC)
NotesDue to the Coronavirus (COVID-19) the conference was conducted virtually.
MoreShow all metadata