Temperature dependent modelling of magnetorheological (MR) dampers using support vector regression

Abstract

In this study, a magnetorheological (MR) damper is experimentally characterized and investigations on the temperature developed during the operation of the damper, its effect on the damper hysteresis are carried out. The increase in temperature at higher input current consequently reduces the damper peak force and energy dissipation, thus altering its hysteretic behaviour. This hysteresis, with dependency on temperature, is modelled using a Gaussian kernel based support vector regression (SVR) model. Three methods, namely particle swarm optimization (PSO)-SVR, gravitational search algorithm (GSA)-SVR and a newly proposed PSOGSA-SVR are studied for finding the optimal hyper parameters for effective modelling of the damper. From the experimental training and testing datasets, four different models of the damper depending on the input frequency are obtained using all three methods and evaluated with five performance indices. The results indicate that the proposed PSOGSA-SVR is an effective non-parametric modelling tool for predicting the hysteresis of the MR damper with temperature effect.

Temperature-dependent dynamic properties of magnetorheological gel composite: experiment and modeling

“…For a viscoelastic polymer matrix, changing temperature would affect the entanglements and sliding of the soft and hard segments that disperse among particles, and thus influence the formation and destruction of particle chains [23]. Therefore, it is necessary to investigate the influence of temperature on dynamic properties since engineering applications require a MRG to possess satisfactory performance under different temperatures [24]. Unfortunately, despite the significance of the temperature-dependent rheological behavior of MRGs, there is little relevant literature, to the best of our knowledge, and only a few publications discuss this in part [19,20].…”

“…Priya et al carried out displacement-controlled experiments to investigate the nonlinear hysteretic behavior of a MRF damper under different currents. It was found that high current would lead to an increase in the temperature of the damper and a reduction in its damping force and energy dissipation [24]. Moreover, the Arrhenius equation was adopted by Rabbani et al to explore a model that could precisely reflect the relationship between the maximum shear yield stress of a MRF and temperature and magnetic field [25].…”

A new AI-surrogate model for dynamics analysis of a magnetorheological damper in the semi-active seat suspension

“…Struct. 29 (2020) 037001 (15pp) https://doi.org/10.1088/1361-665X/ab6ba5 6 Author to whom any correspondence should be addressed.…”

“…However, it is always a challenging task to establish an accurate MRD dynamic model due to strong nonlinear hysteresis [5]. Recent researches show the capability of artificial intelligence (AI)-based techniques in modeling the MRD dynamics by performing machine learning and data mining [6,7]. In [8], a new algorithm named establishing neuro-fuzzy system was proposed to identify the dynamic characteristics of smart dampers, and the effectiveness of the proposed algorithm was verified.…”

Mathematical modeling and experimental evaluation of a prototype double-tube Magnetorheological damper

“…Some authors such as Peng et al [42] reported a modeling and parametric study on MR dampers. Furthermore some authors considered the effect of temperature on performance and mathematical modeling of MR dampers [43].…”