Recent trends on nanofluid heat transfer machine learning research applied to renewable energy

Abstract

Nanofluids have received increasing attention in research and development in the area of renewable and sustainable energy systems. The addition of a small amount of high thermal conductivity solid nanoparticles could improve the thermophysical properties of a base fluid and lead to heat transfer augmentation. Various enhancement mechanisms and flow conditions result in nonlinear effects on the thermodynamics, heat transfer, fluid flow, and thermo-optical performance of nanofluids. A large amount of research data have been reported in the literature, yet some contradictory results exist. Many affecting factors as well as the nonlinearity and refutations make nanofluid research very complicated and impede its potentially practical applications. Nonetheless, machine learning methods would be essentially useful in nanofluid research concerning the prediction of thermophysical properties, the evaluation of thermo-hydrodynamic performance, and the radiative-optical performance applied to heat exchangers and solar energy systems. The present review aims at revealing the recent trends of machine learning research in nanofluids and scrutinizing the features and applicability of various machine learning methods. The potentials and challenges of machine learning approaches for nanofluid heat transfer research in renewable and sustainable energy systems are discussed. According to the Web of Science database, about 3% of nanofluid research papers published in 2019 involved in machine learning and such a tendency is increasing.