Somchai Wongwises

Forced Convective Heat Transfer of Nanofluids - A Review of the Recent Literature

The forced convection of fluids has been investigated by numerous researchers, both experimentally and numerically. A good understanding of characteristics of nanofluid flowhas thoroughly been investigated in these studies. Since the nanoparticles behave more like a single-phase fluid than a solid–liquid mixture, it is assumed that nanofluids are ideally suited in the applications as their usage causes little or no penalty in pressure drop. In recent years, many researchers have tried to fill the gaps on this subject in the literature. To meet the demand for improving the performance of heat transfer equipment, re-examination of the individual components is considered to be essential. The addition of the nanoparticles to the base fluid is one of the significant issues for the optimal performance of heat transfer systems. This paper reports on most of the forced convective heat transfer literature occurring both in-tubes and in-channels regarding the use and preparation of nanofluids. The peer reviewed papers published in citation index journals up to 2012 have been selected for review in the paper. Classification of the papers has been performed according to the publication years. The critical information on the theoretical, experimental and numerical works is presented comprehensively for each paper.

Modeling and optimization of thermal conductivity and viscosity of MnFe 2 O 4 nanofluid under magnetic field using an ANN

This research investigates the applicability of an ANN and genetic algorithms for modeling and multiobjective optimization of the thermal conductivity and viscosity of water-based spinel-type MnFe2O4 nanofluid. Levenberg-Marquardt, quasi-Newton, and resilient backpropagation methods are employed to train the ANN. The support vector machine (SVM) method is also presented for comparative purposes. Experimental results demonstrate the efficacy of the developed ANN with the LM-BR training algorithm and the 3-10-10-2 structure for the prediction of the thermophysical properties of nanofluids in terms of the significantly superior accuracy compared to developing the correlation and employing SVM regression. Moreover, the genetic algorithm is implemented to determine the optimal conditions, i.e., maximum thermal conductivity and minimum nanofluid viscosity, based on the developed ANN.