Predicting thermophysical properties and flow characteristics of nanofluids using intelligent methods: focusing on ANN methods

Abstract

Nanofluids have attracted much attention of researchers during the past years due to its excellent properties. Albeit many theoretical and experimental examinations were conducted to evaluate the thermophysical properties of dissimilar nanofluids, researchers are not successful to find good theories for determining the viscosity and thermal conductivity of nanofluids. Although experimental approaches are more reliable compared to theoretical methods, they are usually difficult to do due to the need for specific equipment. The goal of this study is to review summaries of the most important work performed in the field of various nanofluid properties. In addition, the neural network application in predicting the nanofluid properties in different equipments has been studied. Artificial neural networks (ANNs) are one of the artifact intelligent branches that are inspired by the human brain function in identifying phenomena. They can be used to predict and model the phenomena. One of its applications is to predict and model the nanofluid thermophysical properties. This paper introduces the development of neural networks initially. Then, a summary of recent studies on the prediction modeling of nanofluid physical properties based on ANNs is reported. According to enough samples, it seems that ANN is an operational method to predict the nanofluid thermophysical properties. Finally, a general model for all nanofluids and the effect of all conditions on the nanofluid properties are proposed for future research.