Effect of geometrical factors on magnetic induction distribution of toroidal cores using numerical methods

Abstract

Toroidal cores wound from grain-oriented 3 % SiFe 0.27-mm thick (M4) electrical steel are widely used in many applications. Apart from the processing factors, the geometry of the toroid has an effect on its magnetic properties. In magnetic path length of a toroid from its inside to its outside circumference, the magnetic field varies at different layers. Artificial neural network have been successfully used for the prediction of magnetic performance in electromagnetic devices. Experimental data obtained from the previous measurements have been used as a training data to a feed forward multilayer perceptron neural network for the prediction of magnetic field and flux density distribution. The input parameters were outer and inner diameters and strip thickness while the output parameter was the magnetic field and flux density. When the network was tested by untrained sample data, the average correlation of the models was found to be 99 % and the overall prediction error was in the range of 2.31 and 0.02 in Ampere per meter and Tesla. An analytical equation as depending on theoretical data and prediction results has been determined by using MATLAB(a"double dagger) Curve Fitting Toolbox (TM) for magnetic field and flux density distribution in the toroid.