Optimization of Twist Winglets–Cross-Section Twist Tape in Heat Exchangers Using Machine Learning and Non-Dominated Sorting Genetic Algorithm II Technique

Abstract

This research delves into the impact of Twist Winglets–Cross-Section Twist Tape (TWs-CSTT) structures within heat exchangers on thermal performance. Utilizing Computational Fluid Dynamics (CFD) and machine learning methodologies, optimal geometrical parameters for the TWs-CSTT configuration were examined. The outcomes demonstrate that fluid undergoing a rotational motion within tubes featuring this structure leads to more effective secondary flows, intensified mixing, and improved thermal boundary layer disturbance. Moreover, by integrating machine learning with multi-objective optimization techniques, the performance of heat exchangers can be accurately predicted and optimized, facilitating enhanced heat exchanger design. Through the application of the multi-objective optimization algorithm Non-dominated Sorting Genetic Algorithm II (NSGA-II), the ideal configurations for TWs-CSTT were ascertained: L1 is the cross-sectional length of the Twisted Wings, L2 is the radius of the Central Straight Twisted, and P is the pitch. P = 50.699 mm, L1 = 4.3282 mm, L2 = 4.9736 mm for the Gaussian Process Regression (GPR) model; P = 50.864 mm, L1 = 4.4961 mm, L2 = 4.9992 mm for the LR model; and P = 50.699 mm, L1 = 4.3282 mm, L2 = 4.9736 mm for the Support Vector Regression (SVR) model, aiming to maximize heat exchange efficiency while minimizing friction losses. This study proposes a novel methodological approach to heat exchanger design, leveraging CFD and machine learning technologies to enhance energy efficiency and performance.