ANN for Optimization on Large-Scale Structural Acoustics Models

Abstract: Optimization of transformer design is a challenging task which defines the dimensionsof all the transformer parts, based on a given specification in order to achieve better operating performance. The mechanical force distributions, as a result of the transformer operation, make the structure vibrate at twice the network frequency,and ultimately lead to noise emission from the outer surface of the tank. In this paper,an artificial intelligence technique is proposed for transformer noise data prediction asan optimized alternative to the finite-element method with multi-physics capabilities. The technique uses a feedforward artificial neural network and the back propagation of error learning rule for predicting the noisy data, along with a finite-element modelfor computing a training data set. The method considers two well-known backpropagation algorithms, Levenberg–Marquardt and Bayesian Regularization, and while both of them appear to be extremely efficient when it comes to execution time, Bayesian Regularization presents considerably higher accuracy. The level of accuracyas well as the fast execution time makes the application of artificial neural networksfor finite-element model optimization a viable and efficient approach for industrial use.