Publication: Viscosity and rheological behavior of Al2O3-Fe2O3/water-EG based hybrid nanofluid: A new correlation based on mixture ratio
Date
2023
Authors
Vicki Wanatasanappan V.
Kumar Kanti P.
Sharma P.
Husna N.
Abdullah M.Z.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier B.V.
Abstract
The present study is a pure experimental investigation of the viscosity and rheological properties of the Al2O3-Fe2O3 hybrid nanofluid and the development of a new correlation. The main purpose of the study is to evaluate the effect of the Al2O3-Fe2O3 mixture ratio on the viscosity property and develop a correlation for the viscosity prediction. The Al2O3 and Fe2O3 were first characterized using XRD diffraction and the FESEM technique. The nanofluid was prepared using a two-step method using base fluid consisting of water and ethylene glycol mixture at 60/40 ratios. Five different Al2O3-Fe2O3 nanoparticle compositions were investigated experimentally for the viscosity and rheological properties at temperatures between 0 and 100 �C. The experimental data shows that the Al2O3-Fe2O3 composition of 40/60 resulted in the highest viscosity value at all temperatures investigated, while the 60/40 composition recorded the lowest viscosity value. Besides, the increase in temperature of nanofluid shows a maximum viscosity reduction of 87.2 % as the temperature is increased from 0 to 100 �C. Also, the rheological analysis on a hybrid nanofluid for all compositions of Al2O3-Fe2O3 indicates a Newtonian fluid characteristic. The experimental research data was utilized to create an artificial neural network (ANN)-based architecture. An autoregressive method called the Bayesian approach was adopted for training hyperparameters. During model training, the autoregressive technique assisted in achieving outstanding correlation values of more than 99.99 % with minimal mean squared errors as low as 0.000036. � 2023 Elsevier B.V.
Description
Keywords
Al<sub>2</sub>O<sub>3</sub>-Fe<sub>2</sub>O<sub>3</sub> , ANN , Bayesian optimization , Hybrid nanofluid , Machine learning , Rheological behaviour , Viscosity , Alumina , Aluminum oxide , Bayesian networks , Ethylene , Ethylene glycol , Hematite , Machine learning , Nanofluidics , Neural networks , Newtonian liquids , Rheology , Al2O3-fe2O3 , Bayesian optimization , Hybrid nanofluid , Machine-learning , Mixture ratio , Nanofluids , New correlations , Rheological behaviour , Rheological property , Viscosity properties , Viscosity