Adaptive network-based fuzzy inference system analysis of mixed convection in a two-sided lid-driven cavity filled with a nanofluid

Aminossadati, S. M., Kargar, A. and Ghasemi, B. (2012) Adaptive network-based fuzzy inference system analysis of mixed convection in a two-sided lid-driven cavity filled with a nanofluid. International Journal of Thermal Sciences, 52 2: 102-111.


Author Aminossadati, S. M.
Kargar, A.
Ghasemi, B.
Title Adaptive network-based fuzzy inference system analysis of mixed convection in a two-sided lid-driven cavity filled with a nanofluid
Journal name International Journal of Thermal Sciences   Check publisher's open access policy
ISSN 1290-0729
Publication date 2012-02
Year available 2011
Sub-type Article (original research)
DOI 10.1016/j.ijthermalsci.2011.09.004
Volume 52
Issue 2
Start page 102
End page 111
Total pages 10
Place of publication Issy les Moulineaux, France
Publisher Elsevier Masson
Collection year 2012
Language eng
Formatted abstract A numerical study of laminar mixed convection in a two-sided lid-driven cavity filled with a water — Al2Onanofluid is presented. The top and bottom walls of the cavity are kept at different temperatures and can slide in the same or opposite direction. The vertical walls are thermally insulated. An Adaptive Networkbased Fuzzy Inference System (ANFIS) approach is developed, trained and validated using the results of a Computational Fluid Dynamics (CFD) analysis. The results show that ANFIS can successfully be used to predict the fluid velocity and temperature as well as the heat transfer rate of the cavity, with reduced computation time and without compromising the accuracy.
Keyword Mixed convection
Lid-driven cavity
Richardson number
ANFIS
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Available online 2 October 2011.

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mechanical & Mining Engineering Publications
Official 2012 Collection
 
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Created: Tue, 15 Nov 2011, 16:00:44 EST by Dr Saiied Aminossadati on behalf of School of Mechanical and Mining Engineering