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Title: Evaluation of Geometrically Nonlinear Reduced Order Models with Nonlinear Normal Modes

Several reduced-order modeling strategies have been developed to create low-order models of geometrically nonlinear structures from detailed finite element models, allowing one to compute the dynamic response of the structure at a dramatically reduced cost. But, the parameters of these reduced-order models are estimated by applying a series of static loads to the finite element model, and the quality of the reduced-order model can be highly sensitive to the amplitudes of the static load cases used and to the type/number of modes used in the basis. Our paper proposes to combine reduced-order modeling and numerical continuation to estimate the nonlinear normal modes of geometrically nonlinear finite element models. Not only does this make it possible to compute the nonlinear normal modes far more quickly than existing approaches, but the nonlinear normal modes are also shown to be an excellent metric by which the quality of the reduced-order model can be assessed. Hence, the second contribution of this work is to demonstrate how nonlinear normal modes can be used as a metric by which nonlinear reduced-order models can be compared. Moreover, various reduced-order models with hardening nonlinearities are compared for two different structures to demonstrate these concepts: a clamped–clamped beam model,more » and a more complicated finite element model of an exhaust panel cover.« less
Authors:
 [1] ;  [2] ;  [3] ;  [1]
  1. Univ. of Wisconsin, Madison, WI (United States)
  2. Mercury Marine, Fond du Lac, WI (United States)
  3. U.S. Air Force Research Lab., Wright-Patterson Air Force Base, OH (United States)
Publication Date:
OSTI Identifier:
1237462
Report Number(s):
SAND--2015-2368J
Journal ID: ISSN 0001-1452; 579511
Grant/Contract Number:
AC04-94AL85000
Type:
Accepted Manuscript
Journal Name:
AIAA Journal
Additional Journal Information:
Journal Volume: 53; Journal Issue: 11; Journal ID: ISSN 0001-1452
Publisher:
AIAA
Research Org:
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org:
Air Force Office of Scientific Research, OH (United States)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING