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The magnetorheological fluids are classified as smart materials with controllable rheological properties. The fast growing application of magnetorheological fluids in recent years has increased the demand for simulation and modeling of these fluids. From the invention of magnetorheological fluids up to now, many experimental and also theoretical investigations have been carried out to study these types of smart materials; also many attempts have been made to formulate and simulate their behavior. The aim of this investigation is to present a review on the different models and simulation methods that were applied in the studying of magnetorheological fluids. In this study, the different simulation methods of magnetorheological fluid have been categorized into two general approaches: continuum and discrete phase approaches. The different rheological and structural models of magnetorheological fluids in continuum approach have been summarized in this study. The computational framework of discrete approach and the basic models for magnetorheological fluid in this approach are also discussed.

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Nowadays, among the microscopic traffic flow modeling approaches, the car-following models are increasingly used by transportation experts to utilize appropriate intelligent transportation systems. Unlike previous works, where the reaction delay is considered to be fixed, in this paper, a modified neural network approach is proposed to simulate and predict the carfollowing behavior based on the instantaneous reaction delay of the driver-vehicle unit as the human effects. This reaction delay is calculated based on a proposed idea, and the model is developed based on this feature as an input. In this modeling, the inputs and outputs are chosen with respect to the reaction delay to train the neural network model. Using the field data, the performance of the model is calculated and compared with the responses of some existing neural network car-following models. Considering the difference between the responses of the actual plant and the predicted model as the error, comparison shows that the error in the proposed model is significantly smaller than that that in the other models.

In this Note, a nonlinear dynamic model of a PWM-driven pneumatic fast switching valve is presented. The electro-magnetic, mechanical and fluid subsystems of the valve are investigated, including their interactions. Unknown parameters are identified using direct search optimization and model validation is performed by comparing the simulated and measured current curves. In order to use this model in PWM control applications, a simplification strategy is also proposed and a static model is obtained between the duty cycle input and the moving average of the spool position. The simplified static model is validated again by experiments. To cite this article: M.