Artificial Neural Network Approach for Transient Forced Convective Heat Transfer Optimization

This present research uses artifical 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 actual experimental data sets were used from previous author’s studies and applied as a input data set of ANNs. 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 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 nine samples of baffle-inserted tube. Reynold number, tube lenght 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. The total 70% of the experimental data was used to train, 15% was used to test and the rest of data was used to check the validity of the ANNs. The TRAINBR training function was found as the best model for predicting the target experimental outputs. Almost perfect accuracy between the neural network predictions and experimental data was achieved with 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.