scholarly journals Nonintrusive Nonlinear Reduced Order Models for Structures in Large Deformations: Validations to Atypical Structures and Basis Construction Aspects

Vibration ◽  
2022 ◽  
Vol 5 (1) ◽  
pp. 20-58
Author(s):  
Xiaoquan Wang ◽  
Ricardo A. Perez ◽  
Bret Wainwright ◽  
Yuting Wang ◽  
Marc P. Mignolet

The focus of this investigation is on reduced order models (ROMs) of the nonlinear geometric response of structures that are built nonintrusively, i.e., from standard outputs of commercial finite element codes. Several structures with atypical loading, boundary conditions, or geometry are considered to not only support the broad applicability of these ROMs but also to exemplify the different steps involved in determining an appropriate basis for the response. This basis is formed here as a combination of linear vibration modes and dual modes, and some of the steps involved follow prior work; others are novel aspects, all of which are covered in significant detail to minimize the expertise needed to develop these ROMs. The comparisons of the static and dynamic responses of these structures predicted by the ROMs and by the underlying finite element models demonstrate the high accuracy that can be achieved with the ROMs, even in the presence of significant nonlinearity.

PAMM ◽  
2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Morteza Karamooz Mahdiabadi ◽  
Francesco De Crescenzo ◽  
Christian H. Meyer ◽  
Daniel J. Rixen

Author(s):  
Christian Gogu ◽  
Anirban Chaudhuri ◽  
Christian Bes

Many sampling-based approaches are currently available for calculating the reliability of a design. The most efficient methods can achieve reductions in the computational cost by one to several orders of magnitude compared to the basic Monte Carlo method. This paper is specifically targeted at sampling-based approaches for reliability analysis, in which the samples represent calls to expensive finite element models. The aim of this paper is to illustrate how these methods can further benefit from reduced order modeling to achieve drastic additional computational cost reductions, in cases where the reliability analysis is carried out on finite element models. Standard Monte Carlo, importance sampling, separable Monte Carlo and a combined importance separable Monte Carlo approach are presented and coupled with reduced order modeling. An adaptive construction of the reduced basis models is proposed. The various approaches are compared on a thermal reliability design problem, where the coupling with the adaptively constructed reduced order models is shown to further increase the computational efficiency by up to a factor of six.


Author(s):  
Benjamin Fröhlich ◽  
Peter Eberhard

In structural mechanics, shape optimization is one way to improve mechanical properties of the structure. Typical objectives are to minimize displacements or to minimize stresses. In this context, it is desirable to conduct optimizations with parameterized, reduced order models. However, commercial finite element codes do not provide parameterized system matrices for a geometrical parameterization making an application of PMOR challenging. Therefore, a geometrically parameterized solid finite element is derived which can be formulated with respect to global design parameters. This leads to an affine representation of the system matrices. This allows an efficient application of interpolatory projection methods for parameterized systems. The approach is demonstrated with a numerical example where the proposed approach shows a significant speedup.


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