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氧化铝-水纳米流体热导率和粘度的实验与人工神经网络预测

Experiment and Artificial Neural Network Prediction of Thermal Conductivity and Viscosity for Alumina-Water Nanofluids.

作者信息

Zhao Ningbo, Li Zhiming

机构信息

College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China.

出版信息

Materials (Basel). 2017 May 19;10(5):552. doi: 10.3390/ma10050552.

DOI:10.3390/ma10050552
PMID:28772913
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5459072/
Abstract

To effectively predict the thermal conductivity and viscosity of alumina (Al₂O₃)-water nanofluids, an artificial neural network (ANN) approach was investigated in the present study. Firstly, using a two-step method, four Al₂O₃-water nanofluids were prepared respectively by dispersing different volume fractions (1.31%, 2.72%, 4.25%, and 5.92%) of nanoparticles with the average diameter of 30 nm. On this basis, the thermal conductivity and viscosity of the above nanofluids were analyzed experimentally under various temperatures ranging from 296 to 313 K. Then a radial basis function (RBF) neural network was constructed to predict the thermal conductivity and viscosity of Al₂O₃-water nanofluids as a function of nanoparticle volume fraction and temperature. The experimental results showed that both nanoparticle volume fraction and temperature could enhance the thermal conductivity of Al₂O₃-water nanofluids. However, the viscosity only depended strongly on Al₂O₃ nanoparticle volume fraction and was increased slightly by changing temperature. In addition, the comparative analysis revealed that the RBF neural network had an excellent ability to predict the thermal conductivity and viscosity of Al₂O₃-water nanofluids with the mean absolute percent errors of 0.5177% and 0.5618%, respectively. This demonstrated that the ANN provided an effective way to predict the thermophysical properties of nanofluids with limited experimental data.

摘要

为了有效预测氧化铝(Al₂O₃)-水纳米流体的热导率和粘度,本研究探讨了一种人工神经网络(ANN)方法。首先,采用两步法,分别通过分散不同体积分数(1.31%、2.72%、4.25%和5.92%)、平均直径为30nm的纳米颗粒制备了四种Al₂O₃-水纳米流体。在此基础上,在296至313K的不同温度下对上述纳米流体的热导率和粘度进行了实验分析。然后构建了一个径向基函数(RBF)神经网络,以预测Al₂O₃-水纳米流体的热导率和粘度与纳米颗粒体积分数和温度的函数关系。实验结果表明,纳米颗粒体积分数和温度均可提高Al₂O₃-水纳米流体的热导率。然而,粘度仅强烈依赖于Al₂O₃纳米颗粒体积分数,且随温度变化略有增加。此外,对比分析表明,RBF神经网络具有出色的能力来预测Al₂O₃-水纳米流体的热导率和粘度,平均绝对百分比误差分别为0.5177%和0.5618%。这表明人工神经网络为利用有限的实验数据预测纳米流体的热物理性质提供了一种有效方法。

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本文引用的文献

1
Review on thermal properties of nanofluids: Recent developments.纳米流体的热物性综述:最新进展。
Adv Colloid Interface Sci. 2015 Nov;225:146-76. doi: 10.1016/j.cis.2015.08.014. Epub 2015 Sep 3.
2
Orthogonal least squares learning algorithm for radial basis function networks.径向基函数网络的正交最小二乘学习算法
IEEE Trans Neural Netw. 1991;2(2):302-9. doi: 10.1109/72.80341.
3
Effect of aggregation kinetics on the thermal conductivity of nanoscale colloidal solutions (nanofluid).聚集动力学对纳米级胶体溶液(纳米流体)热导率的影响。
水基SnO₂/还原氧化石墨烯纳米流体的合成、稳定性及热导率特性研究
Materials (Basel). 2017 Dec 27;11(1):38. doi: 10.3390/ma11010038.
Nano Lett. 2006 Jul;6(7):1529-34. doi: 10.1021/nl060992s.