NDLI: Prediction of Viscosity of Nanofluids Using Artificial Neural Networks

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Zhao, Ningbo Li, Shuying Wang, Zhitao Cao, Yunpeng
Copyright Year	2014
Abstract	The viscosity of nanofluids can be affected by many factors. In pursuit of such improved accuracy, model-based viscosity prediction methods have become more complicated. Therefore, there is a need to find an alternative approach that is able to provide a quick solution to viscosity prediction for nanofluids. In this paper, a novel viscosity prediction approach using artificial neural networks (ANN) is introduced as an alternative to the model-based viscosity prediction approach to provide a quick and accurate estimation of nanofluids viscosity. Radial basis function (RBF) neural networks has been utilized to form viscosity prediction architectures. Alumina (Al2O3)-water nanofluids from existing literatures were used to test the effectiveness of the proposed method. The results showed that RBF neural network model had a reasonable agreement in predicting experimental data. The findings of this paper indicated that the ANN model was an effective method for prediction of the viscosity of nanofluids and had better prediction accuracy and simplicity compared with the other existing theoretical methods.
File Format	PDF
ISBN	9780791849569
DOI	10.1115/IMECE2014-40354
Volume Number	Volume 8B: Heat Transfer and Thermal Engineering
Conference Proceedings	ASME 2014 International Mechanical Engineering Congress and Exposition
Language	English
Publisher Date	2014-11-14
Publisher Place	Montreal, Quebec, Canada
Access Restriction	Subscribed
Subject Keyword	Viscosity Water Neural network models Architecture Theoretical methods Artificial neural networks Nanofluids
Content Type	Text
Resource Type	Article

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