A review on nanofluids: Data-driven modeling of thermalphysical properties and the application in automotive radiator. (December 2016)

• Record Type: Journal Article
• Authors: Zhao, Ningbo; Li, Shuying; Yang, Jialong
• Is Part Of: Renewable & sustainable energy reviews. Volume 66(2017)
• Page Start: 596
• Page End: 616
• Publication Date: 2016-12
• Subjects: Nanofluids -- Thermal conductivity -- Viscosity -- Artificial neural networks -- Automotive radiator; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2016.08.029
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: As a potential candidate for the next generation heat transfer media, nanofluids has attracted many researchers and became a very active field in the past decade due to its many good properties. Although a lot of experimental research and theoretical investigations have been carried out to study the thermalphysical properties of different nanofluids, there are still no well-accepted theories for effectively predicting the thermal conductivity and viscosity of all nanofluids with respect to the properties of nanoparticles and base fluid. This paper first summarizes the recent research on data-driven modeling of nanofluids thermalphysical properties based on artificial neural networks (ANN). Then, the potential applications of nanofluids in automotive radiator are analyzed. Some major findings of the review include: (1) given sufficient samples, ANN seems to be an effective approach to predicting the thermalphysical properties of nanofluids; (2) the overall heat transfer performance of automotive radiator can be enhanced by using nanofluids even if there are some discrepancies in the percentage of enhancement and the optimum amount of nanoparticles; and (3) there are many contradictory results in the literatures about the influences of nanoparticle concentration on Nusselt number and pumping power.