Performance optimization of heat-exchanger with delta-wing tape inserts using machine learning

Abstract

• Heat transfer performance of delta-wing tape inserts is predicted using machine learning. • Data include fluids, wing-width and pitch ratios, attack angle, Re , and tube length. • Multiple linear regression is adopted to validate the efficiency of proposed model. • Accurate prediction of heat transfer performance with the lowest variance is earned. • 5–10–10–10–1 configuration is found as optimum configuration with the lowest MAE. Nusselt number ( Nu ), friction factor ( f ), and thermal performance factor ( η ) are effective thermal parameters in determining the robustness of thermal management systems. However, a precise prediction of these parameters is a challenge due to complicated fluid and thermal behaviors of thermal systems, such as heat exchangers. In this study, we propose and develop a machine learning-based approach for predicting the heat transfer performance of a heat exchanger that employs delta-wing tape inserts. An aggregated database, containing 300 data points, is obtained from seven sources that include two working fluids. The wing-width ratio ( w/W ), pitch ratio ( p/W ), attack angle ( α ), Reynolds number ( Re ), and tube length ( L ) are in the ranges of 0.31 to 0.83, 0.95 to 1.65, 30° to 70°, 4,000 to 22,000, and 1,200 to 2,500 mm, respectively. Nu , f , and η are predicted using an artificial neural network (ANN) model based on a universal aggregated database divided into training and test datasets. Optimization is performed, and an ANN model architecture is selected, consisting of input parameters ( w/W , Re , α , p/W , and L ) and hidden layers (10,10,10) that predict the test data with a mean absolute error (MAE) of 0.002393 for f , 1.821792 for Nu , and 0.00507 for η . The robustness of the developed ANN model is analyzed by precluding the databases from the training datasets together and is utilized to predict these excluded datasets. Furthermore, multiple linear regression analysis is adopted to measure and validate the efficiency of the proposed model. From the study results, it is evident that the proposed ANN method can provide valuable guidance to accurately predict the heat transfer characteristics with the lowest variance. Here, the 5–10–10–10-1 configuration is verified as the optimal configuration for the proposed model as it has the lowest MAE of 0.002393.