Heat transfer enhancement by a pair of asymmetric flexible vortex generators and thermal performance prediction using machine learning algorithms

Heat transfer should be considered in the design process of various industrial fields such as electric-electronics, plants and refrigeration and air conditioning, etc. Many researchers have introduced vortex generators to promote mixing of fluids, thereby improving heat transfer performance. Convective heat transfer can be enhanced by using vortex generators where, however, the disadvantage of increased mechanical loss due to increased pressure drop is not avoidable. In order to compensate for the penalty, the present study utilizes the characteristics of self-sustained flapping motions of the flexible vortex generator to improve heat transfer without much increasing the pressure drop. The two flexible vortex generators (FVGs) fixed on the upper and lower channel walls, respectively, are introduced in the present study and they take the form of asymmetric configurations by adjusting the inclination angles. The heat transfer performance is observed to depend on the parametric conditions of FVGs such as the inclination angle, bending rigidity, and the gap distance between them. The present system including the asymmetric FVGs shows the improvement of thermal performance by 122% as compared to the baseline flow when the bending rigidity is 0.06, the initial inclination angle is 75°, and the gap distance is 2.4 times the length of FVGs. The attracting Lagrangian coherent structures (LCS) are visualized by calculating the finite-time Lyapunov exponent (FTLE) field to analyze the effects of the vortex structures near the heated channel walls on the fluid mixing. The net energy loss and average heat transfer are predicted by the three machine learning algorithms, i.e., artificial neural network (ANN), support vector regression (SVR), and random forest (RF), where the inclination angle, bending rigidity, and gap distance of FVGs are selected as input data. The SVR and ANN algorithms show the best performance in predicting the mean energy loss and the heat transfer, respectively, with the R² value of above 0.99 and 0.95.