The chain formation process of ferromagnetic particles under an applied magnetic field is simulated. Three main forces -magnetic force, viscous force, and repelling force, are considered. A model to simulate the motion of particles is proposed based on the analysis of the dynamics of the particles, and a corresponding numerical approach is developed. The formation of particle chains in magnetorheological (MR) fluids under an applied magnetic field is simulated, and the result agrees well with the experimental observation. The developed method is significant for the analysis of the overall behavior of MR fluids and their microscopic mechanisms as well as the effects of the influencing factors, which may be helpful for the design of new MR fluids.

The main drawbacks of a back propagation algorithm of wavelet neural network (WNN) commonly used in fault diagnosis of power transformers are that the optimal procedure is easily stacked into the local minima and cases that strictly demand initial value. A fault diagnostic method is presented based on a real-encoded hybrid genetic algorithm evolving a WNN, which can be used to optimise the structure and the parameters of WNN instead of humans in the same training process. Through the process, compromise is satisfactorily made among network complexity, convergence and generalisation ability. A number of examples show that the method proposed has good classifying capability for single-and multiple-fault samples of power transformers as well as high fault diagnostic accuracy.

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