Entropy generation of graphene-platinum hybrid nanofluid flow through a wavy cylindrical microchannel solar receiver by using neural networks

Analyzing microchannel heat sinks (MCHS) in terms of the second thermodynamic law is useful, and it is necessary to examine MCHSs in terms of irreversible factors. In this research, the second thermodynamic law analysis is conducted for graphene-platinum/water hybrid nanofluid flow to assess how a new cylindrical microchannel heat sink has wavy-shaped fins performs. A variety of Reynolds numbers, nanoparticle concentrations as well as wave amplitudes are used to simulate the problem, while the heat flux is constant. Fluent software is employed to solve the governing equations employing the control-volume method. The distributions of velocity and temperature are derived, and the entropy generation rate (including the generation of thermal as well as frictional entropy), along with the Bejan number, is obtained. The minimum values corresponding to the pointed parameters are, respectively, obtained as 7.63 x 10(-2), 1.24 x 10(-4), and 7.78 x 10(-2), while the maximum magnitudes are 1.09 x 10(-1), 1.49 x 10(-3), and 1.09 x 10(-1), respectively. Increasing each factor, including wave amplitude, particle fraction, and Reynolds number, causes a decline in the thermal entropy generation rate, while frictional entropy rises significantly. The Bejan number was obtained greater than 0.98 in all cases, which means that irreversibility mainly results from the thermal entropy generation. This could be a desirable finding, noting that increasing input variables reduced the thermal entropy generation rate. Finally, by employing an artificial neural network, a model is obtained for the entropy generation of entropy based on distinct factors of wave amplitude, nanofluid concentration, and Reynolds number.

Automated summary

This study analyzes the performance of graphene-platinum/water hybrid nanofluid flow in a cylindrical microchannel heat sink using the second law of thermodynamics. Increasing factors such as wave amplitude, nanoparticle concentration, and Reynolds number leads to a decrease in thermal entropy generation rate while frictional entropy increases. The Bejan number was found to be greater than 0.98 in all cases, indicating that irreversibility mainly results from thermal entropy generation. Evidently, increasing input variables reduces the thermal entropy generation rate. Additionally, an artificial neural network model was developed to predict entropy generation based on factors like wave amplitude, nanofluid concentration, and Reynolds number.