Predicting entropy generation of a hybrid nanofluid in microchannel heat sink with porous fins integrated with high concentration photovoltaic module using artificial neural networks

Abstract

This paper evaluates the characteristic of second law of thermodynamic, including Bejan number and entropy generation for hybrid nanofluid containing graphene-silver nanofluid through a MCHS whit porous fins. Finite-volume technique is utilized to solve the governing equations. To simulate the problem, different porous medium thicknesses, nanoparticle concentrations, and inlet mass flow rates are used while the heat flux remains constant. The minimum values of the frictional and thermal entropy generation are 5 × 10−4 and 6.25 × 10−2, while the maximum values are 3.2 × 10−4 and 9.75 × 10−2. With increasing nanoparticle concentration up to 0.06% wt at constant porous thickness tp=200 µm, frictional entropy generation rises up by 3 × 10−5 and heat transfer rate go up while, thermal entropy generation decreases by 1.5 × 10−2. In addition, by doubling the input mass flow rate and reaching 0.02% at constant nanoparticle concentration (0.06%), thermal entropy generation decreases by 2 × 10−2 while the frictional entropy generation increases by 2.4 × 10−4. The minimum magnitude of Bejan number is 0.994. This show that the irreversibility is derived significantly from thermal entropy generation rate. Finally, an artificial neural network is employed to obtain a model for entropy generation.