A comprehensive review of nanofluids with fractional derivatives: Modeling and application

Abstract: Nanofluids have been widely used as a class of promising working fluids with excellent heat transfer properties. However, the theoretical research on the thermal enhancement mechanism of nanofluids is still in the preliminary stage. Fractional constitutive models provide a new powerful tool to investigate the superior mechanical and thermal properties of nanofluids owing to their advantages in depicting the memory and genetic properties of the system. Fractional nanofluid models have become one of the hot research topics in recent years as better control of flow behavior and heat transfer can be achieved by considering fractional derivatives. The existing studies have indicated that the results obtained by the fractional-order nanofluid model are more consistent with the experimental results than traditional integer-order models. The purpose of this review is to identify the advantages and applications of fractional nanofluid models. First, various definitions of fractional derivatives and correlations of flux utilized in nanofluid modeling are presented. Then, the recent researches on nanofluids with fractional derivatives are sorted and analyzed. The impacts of fractional parameters on flow behaviors and heat transfer enhancement are also highlighted according to the Buongiorno model as well as the Tiwari and Das nanofluid model with fractional operators. Finally, applications of fractional nanofluids in many emerging fields such as solar energy, seawater desalination, cancer therapy, and microfluidic devices are addressed in detail.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: A comprehensive review of nanofluids with fractional derivatives: Modeling and application ; volume:11 ; number:1 ; year:2022 ; pages:3235-3249 ; extent:15 ; Nanotechnology reviews ; 11, Heft 1 (2022), 3235-3249 (gesamt 15)

Contributors, Places and Time

• Creator: Shen, Ming ; Chen, Hui ; Zhang, Mengchen ; Liu, Fawang ; Anh, Vo

Further information

• DOI: 10.1515/ntrev-2022-0496
• URN: urn:nbn:de:101:1-2022121413021186845475
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:27 AM CEST