Abstract—This paper highlights the application of adaptive neuro-fuzzy inference system (ANFIS) to model the free convection heat transfer in a horizontal cavity with adiabatic vertical and isothermally horizontal walls and adiabatic diverters. The main focus of the present paper is to consider the effects of diverter angle and Rayleigh number variation on average free convection heat transfer in the horizontal cavity. The training data for optimizing the ANFIS structure is obtained experimentally by a Mach-Zehnder interferometer. A hybrid learning algorithm consists of gradient descend method and least-squares method is used for ANFIS training. The proposed ANFIS developed using MATLAB functions. For the best ANFIS structure obtained in this study, the maximum errors of the train and test data were found to be 0.148% and 6.651%, respectively. Also, the mean relative errors of the train and test data were found to be 0.049% and 2.54%, respectively. The predicted results show that ANFIS can predict the experimental results precisely.
Index Terms—Free convection; heat transfer; horizontal cavity; diverters; modeling; ANFIS.
The authors are with the Department of Mechanical Engineering, Kermanshah University of Technology, Kermanshah, Iran (e-mail: Alimohammad.karami@yahoo.com; e-mail: t.yousefi2686@ yahoo.com) (e-mail: Ehsan.rezaei@yahoo.com; e-mail: damoon_mk@yahoo.com).
Cite: Alimohammad Karami, Tooraj Yousefi, Ehsan Rezaei, and Damoon Ghashghaei, "Modeling the Free Convection Heat Transfer in a Horizontal Cavity with Flow Diverters Using Adaptive Neuro-Fuzzy Inference System," International Journal of Modeling and Optimization vol. 2, no. 1, pp. 25-30 , 2012.
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