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

Abstract Magnetorheological fluid, 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. In this study, a novel parametric model is proposed to accurately describe the nonlinear hysteresis behavior of a magnetorheological fluid composed of micron-scale carbonyl iron particles. Experimental investigations involving large-amplitude shear tests, employing strain amplitudes of 10% and frequencies of 0.1 Hz and 1 Hz, were conducted at five different current levels (0A, 0.5A, 1A, 1.5A, and 2A) to determine the model parameters. The genetic optimization algorithm was employed to identify the optimal solution for the model parameters. Subsequently, the model parameters were generalized with respect to the applied current, and the relationship between these parameters and current variations was explored. Research findings demonstrate the superiority of the proposed model over existing models such as the Bouc-Wen model and the hyperbolic tangent model in accurately capturing the nonlinear hysteresis behavior of magnetorheological fluid. This study holds significant potential for predicting the nonlinear hysteresis behavior of automotive dampers and provides a solid theoretical foundation for semi-active suspension control.