Recent advances on improved optical, thermal, and radiative characteristics of plasmonic nanofluids: Academic insights and perspectives

Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required. 

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Synthesis, stability, thermophysical properties and AI approach for predictive modelling of Fe3O4 coated MWCNT hybrid nanofluids

Nanoscale heat and mass transport of magnetized 3-D chemically radiative hybrid nanofluid with orthogonal/inclined magnetic field along rotating sheet

The thermophysical properties and enhanced heat transfer performance of SiC-MWCNTs hybrid nanofluids for car radiator system

The MXene/water nanofluids with high stability and photo-thermal conversion for direct absorption solar collectors: A comparative study

Systematical investigation on the solar-thermal conversion performance of TiN plasmonic nanofluids for the direct absorption solar collectors

Systematic investigating on the broadband solar absorption and photo-thermal conversion performance of TiN@rGO plasmonic nanofluids

Doped material-based nanofluids, as an extension of single-phase nanofluids, expand the performance of heat transfer fluids. In this study, a new type of diathermic oil (DO)-based alumina-doped zinc oxide (AZO) nanofluids was prepared, and the thermophysical characteristics of nanofluids were analyzed. It could be found that the addition of new doped nanoparticles made up for the deficiency in the performance of DO as heat transfer fluid (HTF). Compared with the pure DO, the thermal conductivity of the AZO nanofluids (0.08 mass%) was increased by 8.125%. And the viscosity of nanofluids was decreased by 70% as the temperature increased. So as to further determine the application of nanofluids in the domestic heating system, the parameters of forced convection heat transfer in the tube were analyzed in this article. The convective heat transfer coefficient of AZO nanofluids increased with the increase in temperature. At 50 ℃, the convective heat transfer coefficient of 0.08 mass% AZO nanofluid reached the maximum of 5.49% compared with pure DO. More importantly, this research investigated the heat transfer benefits of using nanofluids in domestic solar heating systems. These promising results indicated that AZO nanofluids showed excellent properties at higher temperatures. It shows DO-based AZO nanofluids application potential in domestic solar heating systems.

Xiaoke Li

Papers

The thermophysical properties and enhanced heat transfer performance of SiC-MWCNTs hybrid nanofluids for car radiator system

The MXene/water nanofluids with high stability and photo-thermal conversion for direct absorption solar collectors: A comparative study

Systematical investigation on the solar-thermal conversion performance of TiN plasmonic nanofluids for the direct absorption solar collectors

Systematic investigating on the broadband solar absorption and photo-thermal conversion performance of TiN@rGO plasmonic nanofluids

The enhanced heat transfer of diathermic oil-based alumina-doped zinc oxide nanofluids for domestic solar heating systems