Statistical methods, and especially machine learning, have been increasingly used in nanofluid modeling. This paper presents some of the interesting and applicable methods for thermal conductivity prediction and compares them with each other according to results and errors that are defined. The thermal conductivity of nanofluids increases with the volume fraction and temperature. Machine learni...

in this article cuo/water nanofluid was synthesized by using polyvinylpyrolidone (pvp) as the dispersant.  thenanofluid stability period and the heat transfer enhancement were determinedby measuring the thermal conductivities. to study the nano-fluid stability, zeta (ζ) potential, and absorbency were measured under different ph values and pvp surfactant concentrations; also thermal conductivity...

Interatomic potentials, which describe interactions between elements of nanosystems, are crucial in theoretical study of their physical properties. We focus on two well known empirical potentials, i.e. Tersoff's and Brenner's potentials, and compare their performance in calculation of thermal transport in carbon nanotubes. In this way, we study the temperature and diameter dependence of thermal...

in this study the thermal conductivity and the volumetric heat capacity of non-newtonian fluids are investigated. these fluids find many applications in chemical, medical and food industries. they mostly have viscoelastic behavior and generally differ from newtonian fluids. two non-newtonian fluids, solutions of xanthan and carboxy methyl cellulose in water with various concentrations have bee...

Bismuth telluride nanowires are of interest for thermoelectric applications because of the predicted enhancement in the thermoelectric figure-of-merit in nanowire structures. In this letter, we carried out temperature-dependent thermal diffusivity characterization of a 40 nm diameter Bi2Te3 nanowires/alumina nanocomposite. Measured thermal diffusivity of the composite decreases from 9.2310−7 m2...

Statistical methods, and especially machine learning, have been increasingly used in nanofluid modeling. This paper presents some of the interesting and applicable methods for thermal conductivity prediction and compares them with each other according to results and errors that are defined. The thermal conductivity of nanofluids increases with the volume fraction and temperature. Machine learni...

Non-metallic crystalline materials conduct heat by the transport of quantized atomic lattice vibrations called phonons. Thermal conductivity depends on how far phonons travel between scattering events-their mean free paths. Due to the breadth of the phonon mean free path spectrum, nanostructuring materials can reduce thermal conductivity from bulk by scattering long mean free path phonons, wher...

The thermal conductivity, thermal diffusivity and heat capacity per unit volume of dunite rocks taken from Chillas near Gilgit, Pakistan have been measured simultaneously using transient plane source technique. The temperature dependence of thermal transport properties is studied in the temperature range 83-303 K. Different relations for the estimation of thermal conductivity are also tested. T...

Liquid desiccants such as glycols are used in dehydration process, among which Triethylene Glycol (TEG) is considered as a common choice. The addition of nanoparticles to TEG as the base fluid is one of the prevalent method to improve thermal properties of TEG. In this study, an experimental investigation was performed on thermal conductivity of TEG-based nanofluids with 20 and 40 nm diameter c...

In this work, we present experimental studies on the cross-plane thermal conductivity of InAs/AlSb superlattices. The thermal conductivities of MBE grown InAs/AlSb superlattices are measured using the 3ω method from 80-300 K. The influence of the growth temperature and post annealing is investigated. Significant reductions in thermal conductivity are observed in these superlattices compared to ...