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  Scopus ID: 21100926589

Characterizing TiO2-ZnO Hybrid Nanofluid Viscosity, Correlation Modeling Through Artificial Neural Network, and Rheological Behavior Analysis

Vitthal Sadashiv Gutte, Mayuri Hanmant Molawade, Nilima Zade, Yogita Hande, Shrihari Mahadeo Bondar and Poonam Chandrakant Bhosale

Abstract

This work is an experimental exploration of the viscosity and rheological properties of titanium dioxide-zinc oxide (TiO2-ZnO) hybrid nanofluid’s (HNFs), and the establishment of a novel correlation. The primary goal of this research is to assess how changing the TiO2-ZnO ratio impacts this attribute and to establish a correlation for predicting viscosity. X-ray diffraction (XRD) was used to initially analyze the TiO2 and ZnO. The nanofluid was made in two stages, with a base fluid of a 45/55 water/ethylene glycol (EG) mixture. From 0 to 90°C, the experimental viscosity and rheological parameters of six distinct TiO2-ZnO nanoparticle compositions were determined. The experimental data shows that the 45/55 composition of TiO2-ZnO yielded the highest viscosity value over the temperature range tested, whereas the 55/45 composition yielded the lowest. Additionally, as the temperature of nanofluid is raised from 0 to 90°C, its maximum viscosity decreases by 87.2%. The rheological study of a HNF shows that all TiO2-ZnO mixtures exhibit Newtonian fluid behavior. An ANN (Artificial Neural Network)-based framework was developed with the help of the experimental research data. For the purpose of training hyperparameters, the Bayesian approach, an autoregressive technique, was chosen. The autoregressive method helped train the model to get excellent correlation values of over 99.1% with MSE (Mean square error) as small as 0.000042.

Published on: October 12, 2023
doi: 10.17756/nwj.2023-s3-061
Citation: Gutte VS, Molawade MH, Zade N, Hande Y, Bondar SM, et al. 2023. Characterizing TiO2-ZnO Hybrid Nanofluid Viscosity, Correlation Modeling Through Artificial Neural Network, and Rheological Behavior Analysis. NanoWorld J 9(S3): S332-S339.

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