A novel time-domain dynamic load identification numerical algorithm for continuous systems. (November 2021)

• Record Type: Journal Article
• Authors: Jiang, Jinhui; Ding, Ming; Li, Jun
• Is Part Of: Mechanical systems and signal processing. Volume 160(2021)
• Publication Date: 2021-11
• Subjects: Dynamic load identification -- The Newmark-β method -- Modal space -- Modal truncation -- Tikhonov regularization method; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.107881
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Newmark-β method is applied to the problem of dynamic loads identification for continuous systems for the first time. Well performance in identifying sinusoidal, random and impact loads in simulation cases. Good robustness in noisy environment. Experimental studies of dynamic load identification show good accuracy of the proposed algorithm. Abstract: Structural design and optimization are inseparable from the load acting on the structure. However, a direct measurement of the input loads is very difficult, especially dynamic loads on the large coupled continuous systems. Hence, having an accurate algorithm for the dynamic load identification for continuous systems is critical for solving many vibration problems. In this paper, we propose a novel time-domain algorithm based on the Newmark-β method, which is first attempt to solve the problems of dynamic load identification for continuous systems. By means of modal coordinate transformation and modal truncation method, the algorithm transforms the continuous system with infinite degrees of freedom into a discrete system with multi-degrees of freedom. The conventional implicit Newmark-β method is transformed into an explicit form for the solution of the Y = H F equation, and then the load F can be obtained by inverse solution. The Tikhonov regularization is adopted to overcome the ill-posedness in the inverse problem. Simulated results demonstrate the better accuracy of the proposed algorithm against Green's kernel function method (GKFM) in the identification of sinusoidal, impact and random loads. To further verify the performance of the proposed algorithm in practical application, experimental studies of dynamic load identification are conducted on a simply supported beam system, and the results show that the algorithm is effective and robust against noises as well.