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Prediction of Viscosity of Nanofluids Using Artificial Neural Networks

[+] Author Affiliations
Ningbo Zhao, Shuying Li, Zhitao Wang, Yunpeng Cao

Harbin Engineering University, Harbin, China

Paper No. IMECE2014-40354, pp. V08BT10A093; 10 pages
doi:10.1115/IMECE2014-40354
From:
  • ASME 2014 International Mechanical Engineering Congress and Exposition
  • Volume 8B: Heat Transfer and Thermal Engineering
  • Montreal, Quebec, Canada, November 14–20, 2014
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-4956-9
  • Copyright © 2014 by ASME

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.

Copyright © 2014 by ASME

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