Modal testing for model validation of structures with discrete nonlinearities
Author(s) -
D. J. Ewins,
B. Weekes,
Arnaldo delli Carri
Publication year - 2015
Publication title -
philosophical transactions of the royal society a mathematical physical and engineering sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.074
H-Index - 169
eISSN - 1471-2962
pISSN - 1364-503X
DOI - 10.1098/rsta.2014.0410
Subject(s) - computer science , modal , nonlinear system , representation (politics) , construct (python library) , modal analysis , extension (predicate logic) , linear model , linearity , task (project management) , industrial engineering , systems engineering , structural engineering , machine learning , engineering , finite element method , chemistry , physics , quantum mechanics , politics , political science , polymer chemistry , law , programming language , electrical engineering
Model validation using data from modal tests is now widely practiced in many industries for advanced structural dynamic design analysis, especially where structural integrity is a primary requirement. These industries tend to demand highly efficient designs for their critical structures which, as a result, are increasingly operating in regimes where traditional linearity assumptions are no longer adequate. In particular, many modern structures are found to contain localized areas, often around joints or boundaries, where the actual mechanical behaviour is far from linear. Such structures need to have appropriate representation of these nonlinear features incorporated into the otherwise largely linear models that are used for design and operation. This paper proposes an approach to this task which is an extension of existing linear techniques, especially in the testing phase, involving only just as much nonlinear analysis as is necessary to construct a model which is good enough, or 'valid': i.e. capable of predicting the nonlinear response behaviour of the structure under all in-service operating and test conditions with a prescribed accuracy. A short-list of methods described in the recent literature categorized using our framework is given, which identifies those areas in which further development is most urgently required.
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