Reliability of Artificial Neural Network Approach for Predicting the Performance of Transient Forced Convective Heat Transfer Applications

Artificial Neural Networks (ANNs) have been successfully used in many engineering applications to simulate nonlinear complex systems without requiring any input and output knowledge such as dynamic control, system identification and performance prediction of thermal systems in heat transfer applications. This present research uses artificial neural networks (ANNs) to analyze and estimate the influence of transfer functions and training algorithms on experimentally determined Nusselt numbers, friction factors, entropy generation numbers and irreversibility distribution ratios for nine different baffle plate inserted tubes. Nine baffle-inserted tubes have several baffles with various geometric parameters used in the experiments with a baffle area blockage ratio of two, with different pitch-to-diameter ratios, different baffle orientation angles and different baffle spacings. The experimental set-up of this study consists of three parts flow entrance section, flow development section, test section and flow exit section. The actual experimental data sets were used from previous author’s studies and applied as an input data set of ANNs. MATLAB toolbox was used to search for better network configuration prediction by using commonly used multilayer feed-forward neural networks (MLFNN) with backpropagation (BP) learning algorithm with thirteen 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 of nine samples of baffle-inserted tubes. Reynold number, tube length to baffle spacing ratio, baffle orientation angle and pitch-to-diameter ratio were considered as input variables of ANNs and the time-averaged values of Nusselt number, friction factor, entropy generation number and irreversibility distribution ratio were determined as the target data. 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. Almost perfect accuracy between the neural network predictions and experimental data was achieved with a mean relative error (MRE) of 0,000105816% and correlation coefficient (R) that was 0,999160176 for all datasets, which suggests the reliability of the ANNs as a strong tool for predicting the performance of transient forced convective heat transfer applications.