Experimental identification of distributed nonlinearities in the modal domain. (13th October 2019)

• Record Type: Journal Article
• Authors: Anastasio, D.; Marchesiello, S.; Kerschen, G.; Noël, J.P.
• Is Part Of: Journal of sound and vibration. Volume 458(2019)
• Page Start: 426
• Page End: 444
• Publication Date: 2019-10-13
• Subjects: Nonlinear system identification -- Distributed nonlinearity -- Subspace identification -- Nonlinear beam -- Large deformations; Sound -- Periodicals; Vibration -- Periodicals; Son -- Périodiques; Vibration -- Périodiques; Sound; Vibration; Periodicals; Electronic journals; 620.205
• DOI: 10.1016/j.jsv.2019.07.005
• Languages: English
• ISSNs: 0022-460X

Abstract

Abstract: This paper deals with the nonlinear system identification of structures exhibiting distributed nonlinearities, which has become of great interest recently, due to the continuous interest to improve the performance of structures. This brings the need for designing lighter and more flexible structural elements, which are usually characterized by moderate and large deformation, resulting in a distributed nonlinear behavior. In this framework, system identification remains a particularly challenging problem, especially when experimental measurements are considered. This work proposes a method to perform such a task, based on a convenient model order reduction of the considered structure, followed by a nonlinear system identification algorithm. The methodology is validated on a very thin beam undergoing large-amplitude oscillations, firstly using numerical data and then considering an experimental test bench. On the experimental side, the nonlinearity is first characterized using just the measured data, in order to acquire information that would help the identification process. Eventually, nonlinear system identification is performed in the reduced-order domain. An ad-hoc version of the nonlinear subspace identification (NSI) algorithm is used, but the presented methodology can also be applied with other nonlinear identification tools. Results confirm the goodness of the identification strategy in obtaining a reliable model which takes into account the distributed nonlinear behavior.