Computing the Pressure Drop of Nanofluid Turbulent Flows in a Pipe Using an Artificial Neural Network Model

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Open Journal of Fluid Dynamics

ISSN Print: 2165-3852
ISSN Online: 2165-3860
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"Computing the Pressure Drop of Nanofluid Turbulent Flows in a Pipe Using an Artificial Neural Network Model"
written by Mahmoud S. Youssef, Ayman A. Aly, El-Shafei B. Zeidan,
published by Open Journal of Fluid Dynamics, Vol.2 No.4, 2012

has been cited by the following article(s):

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[1]	Development of ANN structural optimization framework for data-driven prediction of local two-phase flow parameters Progress in Nuclear Energy, 2022 DOI:10.1016/j.pnucene.2022.104176

[2]	Development of ANN structural optimization framework for data-driven prediction of local two-phase flow parameters Progress in Nuclear Energy, 2022 DOI:10.1016/j.pnucene.2022.104176

[3]	Experimental evaluation of heat transfer efficiency of nanofluid in a double pipe heat exchanger and prediction of experimental results using artificial neural networks Heat and Mass Transfer, 2017 DOI:10.1007/s00231-017-2068-6

[4]	A comprehensive review of thermo-physical properties and convective heat transfer to nanofluids Energy, 2015 DOI:10.1016/j.energy.2015.06.105

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