A Review on Nanofluids Thermal Properties Determination Using Intelligent Techniques or Soft Computing Tools

Nanofluids are the dispersion of nano-sized particles into base fluids. Nanofluids have wide scope for applying as coolant in many of the engineering fields because of its higher thermal conductivity and more desirable thermal properties. Numerous mathematical models and experimental models have been proposed to predict the thermo physical properties for the past two decades. It has been noticed that many discrepancies between the mathematical and experimental results of thermo physical properties of nanofluids, in particular, thermal conductivity and viscosity. To mitigate those discrepancies, Intelligent Techniques with flexible mathematical structure that is capable of identifying complex non-linear relationships between input and output data were utilized to accurately predict the thermal properties of nanofluids. The data mining model based on genetic neural network has been widely applied to the procedure of data mining on thermal physical properties of nanofluids to acquire the pattern knowledge. This paper is to review the thermal conductivity of nanofluids research publications which are inter linked with soft computing tools. The outcome of this review shall lead to optimize the nanofluids properties while applying heat transfer nanofluids and to reduce the experimental test runs and number of hypothesis posed by different investigators.