Magnetorheological Fluid Yield Stress Prediction Using Particle Swarm Optimization at Low and High Shear Rate Region

Abstract

Yield stress is an important parameter to measure the performance of a magnetorheological (MR) fluid. The parameter can be obtained by fitting a flow curve consisting of a shear rate-shear stress dataset to a Bingham plastic equation. However, the dataset selection is usually determined by trial and error by selecting the data either at a low or high shear rate region due to there is no standardized selection method. Therefore, this paper attempts to develop a platform to predict the yield stress automatically using particle swarm optimization (PSO). The PSO objective function is inspired by the Biplastic Bingham model. The results have shown that the prediction has shown a good agreement when fitting to the experimental data. Furthermore, the obtained yield stress values at high and low shear rate regions also were discussed from the point of view of the difference and possible effect if the wrong variables are chosen. The evaluations have shown that the gap between the yield stress at low and high regions can be relatively high, which is about more than 10 kPa. The wrong selection of the yield stress at an MR device possibly bring inaccuracy performance prediction/design, especially at high magnetic field value.