Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network

Abstract

In this paper, an artificial neural network is implemented for the sake of predicting the thermal conductivity ratio of TiO2-Al2O3/water nanofluid. TiO2-Al2O3/water in the role of an innovative type of nanofluid was synthesized by the sol–gel method. The results indicated that 1.5 vol.% of nanofluids enhanced the thermal conductivity by up to 25%. It was shown that the heat transfer coefficient was linearly augmented with increasing nanoparticle concentration, but its variation with temperature was nonlinear. It should be noted that the increase in concentration may cause the particles to agglomerate, and then the thermal conductivity is reduced. The increase in temperature also increases the thermal conductivity, due to an increase in the Brownian motion and collision of particles. In this research, for the sake of predicting the thermal conductivity of TiO2-Al2O3/water nanofluid based on volumetric concentration and temperature functions, an artificial neural network is implemented. In this way, for predicting thermal conductivity, SOM (self-organizing map) and BP-LM (Back Propagation-Levenberq-Marquardt) algorithms were used. Based on the results obtained, these algorithms can be considered as an exceptional tool for predicting thermal conductivity. Additionally, the correlation coefficient values were equal to 0.938 and 0.98 when implementing the SOM and BP-LM algorithms, respectively, which is highly acceptable.

Improved Thermophysical Properties and Energy Efficiency of Aqueous Ionic Liquid/MXene Nanofluid in a Hybrid PV/T Solar System

“…Water, thermal oil, ethylene glycol (EG), and ionic liquid are being employed as conventional working fluids in a hybrid solar PV/T system. Inclusion of solid nanoparticles into the heat transfer fluids can substantially improve optical and thermophysical properties [ 9 , 10 , 11 ]. The inclusion of nano-sized solid particles into base fluids, referred to as ‘nanofluids’, significantly alters thermophysical properties of the base liquids especially thermal conductivity [ 12 , 13 , 14 , 15 , 16 ].…”

Computational study of the application of Al2O3 nanoparticles to forced convection of high-Reynolds swirling jets for engineering cooling processes

“…Recent works in the literature are using advanced Artificial Intelligence techniques to characterize thermophysical properties of nanofluids. For instance, Sadeghzadeh et al (2020) used Artificial Neural Networks (ANN) to predict the thermophysical properties of a TiO 2 −Al 2 O 3 /water nanofluid, which was previously synthesized in the laboratory. In their work, relationships among concentration, temperature, thermal conductivity and viscosity were analyzed.…”

Experimental and numerical investigation on convective heat transfer in actively heated bundle-pipe

“…The design and operation of the bundle-pipeline systems require accurate prediction of heat transfer and temperature profiles in different pipes along the length of the bundle, which in turn, necessitates the availability of accurate correlations for heat transfer coefficients on different surfaces in the bundle system (Ahmadi et al, 2019;Sadeghzadeh et al, 2020).…”