Improving energy storage and performance through implementing artificial neural network modeling to forecast heat transfer and entropy generation within a wavy‐wall microchannel under discontinuous‐boundary condition–hybrid nanofluid utilization

Abstract

Numerous industries deal with heat transfer looking for a new technique to boost efficiency. Using nonuniform condition and making a wavy wall and microchannel are the conventional techniques to increase heat transfer which consequently lead to intensification in energy storage potential. In the present study, forced convection heat transfer of nanofluid through the slippage microchannel is investigated by utilizing numerical methods. Moreover, the results of the artificial neural network in determining the parameters involved in the first and second laws of thermodynamics are discussed; so it is necessary to determine the optimal number of neurons in the middle layer. In addition, the current study aims to examine the effects of Reynolds number, slip wall, and locations of thermal boundary condition on heat transfer and irreversibility. However, thermal entropy generation becomes 1.52 times worse. It is also revealed that the slippage wall can enhance the heat transfer up to 15.6%. The best thing about slippage microchannel is creating slip wall raises the thermal entropy generation and declines the viscous entropy generation.