Abdul Yasser Abd Fatah

A review of design and modeling of magnetorheological valve

Following recent rapid development of researches in utilizing Magnetorheological (MR) fluid, a smart material that can be magnetically controlled to change its apparent viscosity instantaneously, a lot of applications have been established to exploit the benefits and advantages of using the MR fluid. One of the most important applications for MR fluid in devices is the MR valve, where it uses the popular flow or valve mode among the available working modes for MR fluid. As such, MR valve is widely applied in a lot of hydraulic actuation and vibration reduction devices, among them are dampers, actuators and shock absorbers. This paper presents a review on MR valve, discusses on several design configurations and the mathematical modeling for the MR valve. Therefore, this review paper classifies the MR valve based on the coil configuration and geometrical arrangement of the valve, and focusing on four different mathematical models for MR valve: Bingham plastic, Herschel–Bulkley, bi-viscous and Herschel–Bulkley with pre-yield viscosity (HBPV) models for calculating yield stress and pressure drop in the MR valve. Design challenges and opportunities for application of MR fluid and MR valve are also highlighted in this review. Hopefully, this review paper can provide basic knowledge on design and modeling of MR valve, complementing other reviews on MR fluid, its applications and technologies.

A phenomenological dynamic model of a magnetorheological damper using a neuro-fuzzy system

A magnetorheological (MR) damper is a promising appliance for semi-active suspension systems, due to its capability of damping undesired movement using an adequate control strategy. This research has been carried out a phenomenological dynamic model of two MR dampers using an adaptive-network-based fuzzy inference system (ANFIS) approach. Two kinds of Lord Corporation MR damper (a long stroke damper and a short stroke damper) were used in experiments, and then modeled using the experimental results. In addition, an investigation of the influence of the membership function selection on predicting the behavior of the MR damper and obtaining a mathematical model was conducted to realize the relationship between input current, displacement, and velocity as the inputs and force as output. The results demonstrate that the proposed models for both short stroke and long stroke MR dampers have successfully predicted the behavior of the MR damper with adequate accuracy, and an equation is presented to precisely describe the behavior of each MR damper.

Design of magnetorheological valve using serpentine flux path method

Magnetorheological (MR) valve is widely applied in a lot of applications that utilizes the flow mode, which is one of the working modes available for MR fluid devices. This paper introduces the serpentine flux path method in an MR valve, whereby using this method it can help to increase the effective region of the valve. The magnetic flux is guided into the annular gap of the valve, as the magnetic flux can be weaved by alternating the magnetic and non-magnetic materials. The method is simulated using Finite Element Method Magnetics to see the effects of weaving steps and thicknesses of non-magnetic materials to the magnetic flux distribution and pressure drop change in the valve. The results show that MR effective region can be increased with the serpentine flux path method, and the additional steps and thicknesses of non-magnetic materials help to further increase the pressure drops within the valve.

A GA-Weighted Adaptive Neuro-Fuzzy Model to Predict the Behaviour of Magnetorheological Damper

Magnetorheological damper is a controllable device in semi-active suspension system to absorb unwanted movement. The accuracy of magnetorheological damper model will affect performance of the control system. In this paper, a combination of genetic algorithm (GA) and adaptive-network-based fuzzy inference system (ANFIS) approaches is utilized to model the magnetorheological damper using experimental results. GA algorithm is implemented to modify the weights of the trained ANFIS model. The proposed method is compared with ANFIS and artificial neural network (ANN) methods to evaluate the prediction performance. The result illustrates that the proposed GA-weighted adaptive neuro-fuzzy model has successfully predicted the magnetorheological damper behaviour and outperformed other compared methods.