A new platform for the prediction of field-dependent yield stress and plastic viscosity of magnetorheological fluids using particle swarm optimization

The yield stress and plastic viscosity of magnetorheological (MR) fluids are identified by fitting rheological models based on a selected dataset on a certain range of shear rates. However, the datasets are often arbitrarily determined as there is no standardized procedure available. To overcome this problem, a platform that capable to minimize the fitting error while considering the classification of the shear rate regions is needed. Therefore, this work proposed a new platform for the systematic prediction of field-dependent rheological characteristics using particle swarm optimization (PSO). PSO is a meta-heuristic algorithm for solving optimization problems based on a guided search of the defined problem space, which is governed by the objective function. An intersection point of low and high shear rate regions critical shear rate is formulated as part of the objective function to standardize the characterization within the defined regions. The objective function is inspired by the modified Bingham biplastic and Papanastasiou models to predict five magnetic field dependent-rheological parameters. In the development stage, the shear stress model was first established using a previously developed extreme learning machine method. Then, the codes of the PSO, objective functions and search space identification were developed and implemented. To validate the effectiveness of the proposed procedure, the platform performance was analysed at different algorithmic parameters and compared with the existing optimization methods. The simulation results indicated that the proposed platform performed better than the existing ones with R² of 0.943 and was able to systematically and accurately predict the rheological parameters.