Constitutive models of magnetorheological fluids having temperature-dependent prediction parameter

Abstract

This work presents constitutive models of magnetorheological (MR) fluids, which can predict the shear and dynamic yield stress depending on temperature. Two existing models, the Herschel–Bulkley rheological and power law model, which are frequently used in MR fluid research, are adopted and modified to take the temperature into account. A new constitutive model of MR fluids is developed using the extreme learning machine (ELM) method. In this development, among many machine learning approaches, a simple and efficient learning algorithm for a single hidden layer feed-forward neural network (SLFN) is adopted and applied to the rheological model of MR fluids. The temperature, shear rate, and magnetic field are treated as inputs, and the shear stress is taken as an output. After formulating the models associated with experimental coefficients, the two most important properties of MR fluids; the shear and yield stress are predicted and compared with the measured values. The prediction accuracy for the field-dependent rheological properties of MR fluids in several different temperatures is evaluated and compared. It is shown that the ELM model developed in this work provides the best accuracy, followed by two other modified constitutive equations.