OPTIMAL NETWORK ARCHITECTURE FOR NUSSELT NUMBER AND FRICTION FACTOR

Abstract

This present research uses artifical neural networks (ANNs) to determine Nusselt numbers and friction factors for nine different baffle plate inserted tubes. MATLAB toolbox was used to search better network configuration prediction by using commonly used multilayer feed-forward neural networks (MLFNN) with back propagation (BP) learning algorithm with five different training functions with adaptation learning function of mean square error and TANSIG transfer function. In this research, eighteen data samples were used in a series of runs for each nine samples of baffle-inserted tube. Up to 70% of the whole experimental data was used to train the models, 15 % was used to test the outputs and the remaining data points which were not used for training were used to evaluate the validity of the ANNs. The results show that the TRAINBR training function was the best model for predicting the target experimental outputs.