Uncertain dynamic analysis for rigid-flexible mechanisms with random geometry and material properties

Wu, J; Luo, Z; Zhang, N; Zhang, Y; Walker, PD

Uncertain dynamic analysis for rigid-flexible mechanisms with random geometry and material properties

Wu, J

Luo, Z

Zhang, N

Zhang, Y

Walker, PD

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Publication Type:: Journal Article
Citation:: Mechanical Systems and Signal Processing, 2017, 85 pp. 487 - 511
Issue Date:: 2017-02-15

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Field	Value	Language
dc.contributor.author	Wu, J https://orcid.org/0000-0002-4925-4242	en_US
dc.contributor.author	Luo, Z https://orcid.org/0000-0002-6953-3986	en_US
dc.contributor.author	Zhang, N https://orcid.org/0000-0001-6408-0044	en_US
dc.contributor.author	Zhang, Y https://orcid.org/0000-0003-0059-5369	en_US
dc.contributor.author	Walker, PD https://orcid.org/0000-0003-3988-3966	en_US
dc.date.available	2020-05-25T19:17:01Z
dc.date.issued	2017-02-15	en_US
dc.identifier.citation	Mechanical Systems and Signal Processing, 2017, 85 pp. 487 - 511	en_US
dc.identifier.issn	0888-3270	en_US
dc.identifier.uri	http://hdl.handle.net/10453/54629
dc.description.abstract	© 2016 Elsevier Ltd This paper proposes an uncertain modelling and computational method to analyze dynamic responses of rigid-flexible multibody systems (or mechanisms) with random geometry and material properties. Firstly, the deterministic model for the rigid-flexible multibody system is built with the absolute node coordinate formula (ANCF), in which the flexible parts are modeled by using ANCF elements, while the rigid parts are described by ANCF reference nodes (ANCF-RNs). Secondly, uncertainty for the geometry of rigid parts is expressed as uniform random variables, while the uncertainty for the material properties of flexible parts is modeled as a continuous random field, which is further discretized to Gaussian random variables using a series expansion method. Finally, a non-intrusive numerical method is developed to solve the dynamic equations of systems involving both types of random variables, which systematically integrates the deterministic generalized-α solver with Latin Hypercube sampling (LHS) and Polynomial Chaos (PC) expansion. The benchmark slider-crank mechanism is used as a numerical example to demonstrate the characteristics of the proposed method.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP150102751
dc.relation.ispartof	Mechanical Systems and Signal Processing	en_US
dc.relation.isbasedon	10.1016/j.ymssp.2016.08.040	en_US
dc.subject.classification	Acoustics	en_US
dc.title	Uncertain dynamic analysis for rigid-flexible mechanisms with random geometry and material properties	en_US
dc.type	Journal Article
utslib.citation.volume	85	en_US
utslib.for	091307 Numerical Modelling and Mechanical Characterisation	en_US
utslib.for	0905 Civil Engineering	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0915 Interdisciplinary Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	85	en_US

Abstract:

© 2016 Elsevier Ltd This paper proposes an uncertain modelling and computational method to analyze dynamic responses of rigid-flexible multibody systems (or mechanisms) with random geometry and material properties. Firstly, the deterministic model for the rigid-flexible multibody system is built with the absolute node coordinate formula (ANCF), in which the flexible parts are modeled by using ANCF elements, while the rigid parts are described by ANCF reference nodes (ANCF-RNs). Secondly, uncertainty for the geometry of rigid parts is expressed as uniform random variables, while the uncertainty for the material properties of flexible parts is modeled as a continuous random field, which is further discretized to Gaussian random variables using a series expansion method. Finally, a non-intrusive numerical method is developed to solve the dynamic equations of systems involving both types of random variables, which systematically integrates the deterministic generalized-α solver with Latin Hypercube sampling (LHS) and Polynomial Chaos (PC) expansion. The benchmark slider-crank mechanism is used as a numerical example to demonstrate the characteristics of the proposed method.

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http://hdl.handle.net/10453/54629