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In thermal engineering implementations, heat exchangers need to have improved thermal capabilities and be smaller to save energy. Surface adjustments on tube heat exchanger walls may improve heat transfer using new manufacturing technologies. Since quantifying enhanced tube features is quite difficult due to the intricacy of fluid flow and heat transfer processes, numerical methods are preferred to create efficient heat exchangers. Recently, machine learning algorithms have been able to analyze flow and heat transfer in improved tubes. Machine learning methods may increase heat exchanger efficiency estimates using data. In this study, the boiling pressure drop of different refrigerants in smooth and micro-fin tubes is predicted using an artificial neural network-based machine learning approach. Two different numerical models are built based on the operating conditions, geometric specifications, and dimensionless numbers employed in the two-phase flows. A dataset including 812 data points representing the flow of R12, R125, R134a, R22, R32, R32/R134a, R407c, and R410a through smooth and micro-fin pipes is used to evaluate feed-forward and backward propagation multi-layer perceptron networks. The findings demonstrate that the neural networks have an average error margin of 10 percent when predicting the pressure drop of the refrigerant flow in both smooth and micro-fin tubes. The calculated R-values for the artificial neural network’s supplementary performance factors are found above 0.99 for all models. According to the results, margins of deviations of 0.3 percent and 0.05 percent are obtained for the tested tubes in Model 1, while deviations of 0.79 percent and 0.32 percent are found for them in Model 2.

Because of the prompt improvements in Micro-Electro-Mechanical Systems, thermal management necessities have altered paying attention to the compactness and high energy consumption of actual electronic devices in industry. In this study, 625 data sets obtained numerically according to the change of five different geometric parameters and Reynolds numbers for delta winglet type vortex generator pairs placed in a microchannel were utilized. Four dissimilar artificial neural network models were established to predict the heat transfer characteristics in a microchannel with innovatively oriented vortex generators in the literature. Friction factor, Nusselt number, and performance evaluation criteria were considered to explore the heat transfer characteristics. Different neuron numbers were determined in the hidden layer of each of the models in which the Levethenberg–Marquardt training algorithm was benefited as the training algorithm. The predicted values were checked against the target data and empirical correlations. The coefficient of determination values calculated for each machine learning model were found to be above 0.99. According to obtained results, the designed artificial neural networks can provide high prediction performance for each data set and have higher prediction accuracy compared to empirical correlations. All data predicted by machine learning models were collected within the range of ±3% deviation bands, whereas the majority of the estimated data by empirical correlations dispersed within ±20% ones. For that reason, a full evaluation of the estimation performance of artificial neural networks versus empirical correlations data is enabled to fill a gap in the literature as one of the uncommon works.