A mathematical modelling and experimental study of annular-radial type magnetorheological damper

Abstract

An experimental study was undertaken to evaluate the mathematical modelling of the magnetorheological (MR) damper featuring annular radial gap on its valve. The experiment was conducted using a fatigue dynamic test machine under particular excitation frequency and amplitude to get force-velocity and force-displament characteristics. Meanwhile, the mathematical modelling was done using quasi-steady modelling approach. Simulation using adaptive neuro fuzzy inference (ANFIS) Algorithm (Gaussian and Generalized Bell) were also carried out to portray the damping force-displacement modelling that is used to compare with the experimental results. The experimental characteristics show that amplitudes excitation and current input affect the result damping force value. The comparison of the experimental and mathematical results presented in this paper shows a significant difference in damping force value and that the quasi-steady modelling could not significantly approach the damping force-velocity results. Moreover, the semi-active damper is compared to the passive damper. The results show that a semi-active damper performs better than a passive damper because it only requires a little power. Based on the damping force-displacement modelling, it can be seen that Gaussian has a higher accuracy rather than Generalized Bell. Discussion on the energy dissipation and equivalent damping coefficient were also accomodated in this paper. Having completed in mathematical modelling and simulation, the damper would be ready for further work in-vehicle application that is development of control system.

The Influence of Current Magnitudes and Profiles on the Sedimentation of Magnetorheological Fluids: An Experimental Work

“…This slow rate of oscillation allows for detailed observation and analysis of the settling particles' profile without inducing rapid changes or disturbances. It should be noted that the DC excitation was not considered in this study since this study refers to the testing parameter on the application of MR fluids such as MR dampers, which are carried out under sinusoidal loading at a certain displacement, amplitude, and frequency [29,30]. Therefore, the experiment of this study was conducted with ten different testing parameters considering the various current input intensities, wave excitation, and diameter of the measuring tube (13 mm and 15 mm), which can be seen in Tables 3 and 4.…”

Theoretical switch model of novel asymmetric magnetorheological damper for shock and vibration application

“…It has been proved that the innerto-outer flow channel ratio is greater than 0.65, and the error in using the plate approximation to convert the annular flow channel to a plate model for analysis is less than 1% [21,22]. In addition, combined with different flow modes (shear mode [23,24], flow mode [25], extrusion mode [26,27], and mixed mode [28,29]) and various channel shapes (multi-coil [30][31][32], spiral channel [33,34], curved channel [35,36], multi-parallel channel [37,38], tapered hole-shaped channel [18], bypass channel [39,40]) the flat-plate approximation still can characterize mechanical models well. However, given the conical channel shape of the CFC-MRD structure, the conventional parallel plate model cannot fully capture its flow characteristics.…”

Modelling and Dynamic Analysis of Adaptive Neuro-Fuzzy Inference System-Based Intelligent Control Suspension System for Passenger Rail Vehicles Using Magnetorheological Damper for Improving Ride Index

“…Y.Y [14] developed a neural network model featuring 6 input neurons, 1 output neuron, and 12 neurons within a hidden layer, effectively emulating the dynamic behavior of MR dampers. Empirical studies and mathematical modeling on MR dampers were conducted by Maharani et al [15] and Han et al [16], contributing valuable insights to the field. Altogether, these efforts underscore the continuous strides being made to enhance railway suspension systems through innovative intelligent control strategies and advanced modeling techniques.…”