Hydrodynamic and thermal performance prediction of functionalized MWNT-based water nanofluids under the laminar flow regime using the adaptive neuro-fuzzy inference system

Adaptive Neuro-Fuzzy Inference System (ANFIS) opens a new gateway in understanding the complex behaviors and phenomena for different fields such as heat transfer in nanoparticles. The ANFIS method is a shortcut to find a nonlinear relation between input and output and results in valid outcomes, espe...

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Bibliographic Details
Published in:Numerical heat transfer. Part A, Applications Applications, 2016-07, Vol.70 (1), p.103-116
Main Authors: Savari, Maryam, Rashidi, Sajjad, Amiri, Ahmad, Shanbedi, Mehdi, Zeinali Heris, Saeed, Kazi, S. N.
Format: Article
Language:English
Subjects:
Adaptive systems
Artificial neural networks
Coefficients
Fuzzy logic
Heat transfer
Mathematical models
Nanofluids
Nanostructure
Simulation
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Summary:Adaptive Neuro-Fuzzy Inference System (ANFIS) opens a new gateway in understanding the complex behaviors and phenomena for different fields such as heat transfer in nanoparticles. The ANFIS method is a shortcut to find a nonlinear relation between input and output and results in valid outcomes, especially in engineering phenomena, which is used here for determining the convective heat transfer coefficient. Using the ANFIS, the critical parameters in heat transfer including convective heat transfer coefficient and pressure drop are determined. To realize this issue, the thermophysical properties of non-covalently and covalently functionalized multiwalled carbon nanotubes-based water nanofluid were investigated experimentally. The results of simulation and their comparison with the experimental results showed an excellent evidence on the validity of the model, which can be expanded for other conditions. The proposed method of ANFIS modeling may be applied to the optimization of carbon-based nanostructure-based water nanofluid in a circular tube with constant heat flux.
ISSN:1040-7782
1521-0634
DOI:10.1080/10407782.2016.1139974