Artificial intelligence prediction of natural convection of heat in an oscillating cavity filled by CuO nanofluid

Background: In the last five years, a new generation of advanced numerical methods was developed to reduce computational costs and improve the prediction process. The combination of Artificial Intelligence and traditional computational methods is the best sample of this generation. Employing this method can expand the horizon of numerical modeling. This method is also suitable for complex processes such as natural convection and oscillating heat transfer. This case study tries to peruse the effects of variable boundary conditions such as magnetic field, angle, and nanofluids volume fraction on the heat transfer. Also, the impact of oscillation has been studied. Methods: To accomplish these aims, numerical modeling of natural convection based on Boussinesq approximation was used. Techniques from machine learning were employed to develop a predictive tool that utilizes the data generated by computational analysis of the problem. The Ra and Ha were considered in the ranges of 10(3) <10(6) and 0<40, respectively. Oscillation frequencies between 20 and 50 Hz and amplitudes of 0.5, 1, and 2 mm were studied. Findings: The result showed that Nu increased in initial steps and then decreased at a constant Ha by increasing the rotation angle. It appeared that the amplitude of oscillation has a pronounced effect on the heat transfer rate. The results show that the Ha could affect streamlines of fluid. By increasing Ha=0 to Ha=52, the maximum velocity point in the close enclosure moved 14% closer to the hot side. (C) 2021 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Automated summary

The article introduces an advanced numerical method that combines Artificial Intelligence and traditional computational methods to expand the application scope of numerical modeling, particularly suitable for complex processes such as natural convection and oscillating heat transfer. Through numerical modeling, the study explores the effects of variable boundary conditions on heat transfer, finding that the amplitude of oscillation significantly affects heat transfer rates.