Deep learning with multilayer perceptron for optimizing the heat transfer of mixed convection equipped with MWCNT-water nanofluid

Abstract

In the modern era, Artificial Intelligence (AI) has emerged as a powerful tool that can rapidly generate highly accurate data, offering tremendous potential for optimizing system performance. This study focuses on harnessing the capabilities of an artificial neural network (ANN) to determine optimal parameter values for heat transfer in a mixed convection mechanism. The first step of this research involved conducting CFD simulations to investigate the impact of varying Grashof numbers (102,103,and104), Reynolds numbers (1,10,and100), and MWCNT volume fractions (0, 0.01, 0.02, and 0.03) on the Nusselt number within an elliptical enclosure containing a centrally located rotational cylinder. A total of 48 simulations were performed, generating a comprehensive dataset for training the ANN-Multilayer Perceptron (MLP). In the second step, the trained ANN was utilized to generate an additional 700 data points with remarkable accuracy. This enabled efficient exploration of the parameter space, providing valuable insights into the system behavior and facilitating optimization efforts. The findings of this study revealed a 0.03 vol fraction of MWCNT into the water in Grashof numbers of 102,103,and104, the average Nusselt number increased by approximately 46%, 31%, and 12%, respectively. The ANN-based approach successfully identified optimal values for the variables that maximize the Nusselt number.

Keywords

• Deep learning
• Multilayer perceptron
• Mixed convection
• Nanofluid
• Numerical study