A novel reliable parametric model for predicting the nonlinear hysteresis phenomenon of composite magnetorheological fluid

• Published in: Smart materials and structures Vol. 34; no. 3; pp. 35060 - 35078
• Main Authors: Zhang, Guang, Luo, Jiahao, Sun, Min, Yu, Yang, Chen, Junyu, Wang, Jiong, Ouyang, Qing, Qiu, Ye, Chen, Guinan, Liu, Qianwei, Chen, Bo, Shen, Teng, Zhang, Zheng
• Format: Journal Article
• Language: English
• Published: IOP Publishing 01.03.2025
• Subjects: genetic algorithm; magnetorheological fluid; nonlinear hysteresis phenomenon; parameter identification; parametric model
• ISSN: 0964-1726; 1361-665X
• DOI: 10.1088/1361-665X/adbf57

Magnetorheological fluid (MRF), as a novel intelligent composite material, possesses unique controllable properties in the presence of a magnetic field, thereby opening up new possibilities for its engineering applications. This study proposes a novel parametric model to predict the nonlinear hysteresis behavior of MRF using micron-scale carbonyl iron particles. Experiments with large-amplitude shear tests (10% strain amplitude, 0.1 Hz and 1 Hz frequencies) were conducted at five current levels (0 A, 0.5 A, 1 A, 1.5 A, and 2 A) to identify model parameters via a genetic optimization algorithm. The proposed model, with fewer parameters and no differential operators, outperforms existing models (e.g. Bouc–Wen and hyperbolic tangent models) in capturing MRF’s nonlinear behavior. This research provides a robust theoretical framework for predicting the nonlinear hysteresis in automotive dampers and semi-active suspension control.