relation: https://khub.utp.edu.my/scholars/20217/
title: Application of Machine Learning Algorithms in Predicting Rheological Behavior of BN-diamond/Thermal Oil Hybrid Nanofluids
creator: Ali, A.
creator: Noshad, N.
creator: Kumar, A.
creator: Ilyas, S.U.
creator: Phelan, P.E.
creator: Alsaady, M.
creator: Nasir, R.
creator: Yan, Y.
description: The use of nanofluids in heat transfer applications has significantly increased in recent times due to their enhanced thermal properties. It is therefore important to investigate the flow behavior and, thus, the rheology of different nanosuspensions to improve heat transfer performance. In this study, the viscosity of a BN-diamond/thermal oil hybrid nanofluid is predicted using four machine learning (ML) algorithms, i.e., random forest (RF), gradient boosting regression (GBR), Gaussian regression (GR) and artificial neural network (ANN), as a function of temperature (25�65 °C), particle concentration (0.2�0.6 wt.), and shear rate (1�2000 s�1). Six different error matrices were employed to evaluate the performance of these models by providing a comparative analysis. The data were randomly divided into training and testing data. The algorithms were optimized for better prediction of 700 experimental data points. While all ML algorithms produced R2 values greater than 0.99, the most accurate predictions, with minimum error, were obtained by GBR. This study indicates that ML algorithms are highly accurate and reliable for the rheological predictions of nanofluids. © 2024 by the authors.
date: 2024
type: Article
type: PeerReviewed
identifier:   Ali, A. and Noshad, N. and Kumar, A. and Ilyas, S.U. and Phelan, P.E. and Alsaady, M. and Nasir, R. and Yan, Y.  (2024) Application of Machine Learning Algorithms in Predicting Rheological Behavior of BN-diamond/Thermal Oil Hybrid Nanofluids.  Fluids, 9 (1).       
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183392395&doi=10.3390%2ffluids9010020&partnerID=40&md5=bb02f64211b51714d45c32576d29dea4
relation: 10.3390/fluids9010020
identifier: 10.3390/fluids9010020