Prediction of the effective parameters of the nanofluids using the generalized stochastic perturbation method

A comprehensive review of thermo-physical properties and convective heat transfer to nanofluids

“…The mechanical, optical, electrical, magnetic, and thermal properties of nanoparticles are better than those of conventional bulk materials with coarse grain structures [34,35]. Recognizing the opportunity to apply nanotechnology in thermal engineering, Stephen Choi and his colleagues at the ANL (Argonne National Laboratory) proposed the concept of nanofluids in 1994 and investigated issues related to fundamentals and applications of nanofluids.…”

Developing an ANFIS-based swarm concept model for estimating the relative viscosity of nanofluids

“…The word 'nanoparticle' refers to solid particles with a diameter of less than 100 nm. In general, nanofluids have a volumetric fraction of less than 4% nanoparticles (Kamiński & Ossowski, 2014). Nanofluids usually contain TiO 2 , Al 2 O 3 , SiC, SiO 2 , and CuO as nanoparticles, and deionized water, water, ethylene glycol, polyalphaolefin, ethanol, or a mixture of ethylene glycol or propylene glycol with water as the base fluid (Mahbubul, Saidur, & Amalina, 2012).…”

Parameter estimation for the generalized fractional element network Zener model based on the Bayesian method

“…Plenty of methods have been developed for classical inverse problems [20,21], while research on that of fractional models is quite little. Nowadays, the Levenberg-Marquardt method is shown to be efficient to estimate the relaxation time and the order of fractional derivative in fractional single-phase-lag heat equation by Ghazizadeh et al [22].…”