Uncertain dynamic analysis for rigid-flexible mechanisms with random geometry and material properties. (15th February 2017)

• Record Type: Journal Article
• Authors: Wu, Jinglai; Luo, Zhen; Zhang, Nong; Zhang, Yunqing; Walker, Paul D.
• Is Part Of: Mechanical systems and signal processing. Volume 85(2017)
• Page Start: 487
• Page End: 511
• Publication Date: 2017-02-15
• Subjects: Rigid-flexible multibody systems -- Absolute node coordinate formula (ANCF) -- Random uncertainty -- Polynomial Chaos (PC) expansion; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2016.08.040
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: 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. Highlights: Random uncertainty analysis in the rigid-flexible multibody systems. Combination of random variables and random field for describing uncertainty. Systematically integration of PCE, LHS, and the ANCF methods.