Prediction of unsteady mixed convection over circular cylinder in the presence of nanofluid- A comparative study of ANN and GEP

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2015-06-30 DOI:10.3329/JNAME.V12I1.21812

P. Dey, A. Sarkar, A. Das

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引用次数: 21

Abstract

Heat transfer due to forced convection of copper water based nanofluid in the presence of buoyancy has been predicted by the Artificial Neural network (ANN) Gene Expression Programming (GEP). The present nanofluid is formed by mixing copper nano particles in water and the volume fractions are considered here are 0% to 15% and the Reynolds number are varying from 80 to 180. The buoyancy effect is done by introducing Richardson number (Ri) as 1 and -1. The back propagation algorithm is used to train the network. The present ANN and GEP models are trained by the input and output data which has been obtained from the numerical simulation, performed in finite volume based Computational Fluid Dynamics (CFD) commercial software Fluent. The numerical simulation based results are compared with the back propagation based ANN and GEP results. It is found that the mixed convection heat transfer of water based nanofluid can be predicted correctly by both ANN and GEP but GEP is found more efficient. It is also observed that the back propagation ANN and GEP both can predict the heat transfer characteristics of nanofluid very quickly compared to standard CFD method.

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纳米流体存在下圆柱上非定常混合对流的预测——神经网络与GEP的比较研究

利用人工神经网络(ANN)基因表达编程(GEP)方法预测了铜水基纳米流体在浮力作用下的强制对流换热。目前的纳米流体是由铜纳米颗粒在水中混合形成的，这里考虑的体积分数为0%至15%，雷诺数为80至180。通过引入理查德森数(Ri)为1和-1来实现浮力效应。采用反向传播算法对网络进行训练。目前的ANN和GEP模型是通过基于有限体积计算流体动力学(CFD)商业软件Fluent的数值模拟得到的输入和输出数据进行训练的。将基于数值模拟的结果与基于反向传播的ANN和GEP结果进行了比较。结果表明，人工神经网络和GEP都能准确预测水基纳米流体的混合对流换热过程，但GEP更有效。与标准CFD方法相比，反向传播神经网络和GEP都能快速预测纳米流体的传热特性。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.