Application of Artificial Neural Network (ANN) for modeling oxide-based nanofluids dynamic viscosity

被引：63

作者：

Longo, Giovanni A. ^{[1
]}

Zilio, Claudio ^{[1
]}

Ortombina, Ludovico ^{[1
]}

Zigliotto, Mauro ^{[1
]}

机构：

[1] Univ Padua, Dept Management & Engn DTG, Padua, Italy

来源：

INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER | 2017年 / 83卷

关键词：

Nanofluids; Dynamic viscosity; ANN; THERMAL-CONDUCTIVITY EQUATIONS; CORRESPONDING STATES FRAMEWORK; HEAT-TRANSFER; THERMOPHYSICAL PROPERTIES; AQUEOUS SUSPENSIONS; CARBON NANOTUBES; PURE FLUIDS; NANO-FLUIDS; GLYCOL; PREDICTION;

D O I：

10.1016/j.icheatmasstransfer.2017.03.003

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

This paper presents an Artificial Neural Network (ANN) model for predicting the dynamic viscosity of oxide nanoparticles suspension in water and ethylene glycol. The model accounts for the effect of temperature, nano particle volume fraction, nanoparticle diameter, cluster of nanoparticles average size, and base fluid properties. The model was trained on a set of data obtained by the present authors and tested on data coming from other authors. The model shows a fair agreement in predicting experimental data: the mean absolute percentage error (MAPE) is 4.15%. The characteristic parameters of the ANN model are reported in details in the paper. (C) 2017 Elsevier Ltd. All rights reserved.

引用

页码：8 / 14

页数：7

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