Investigation of empirical correlations on the determination of condensation heat transfer characteristics during downward annular flow of R134a inside a vertical smooth tube using artificial intelligence algorithms
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Author list: Balcilar M., Dalkili็ A.S., Bolat B., Wongwises S.
Publisher: Springer
Publication year: 2011
Journal: Journal of Mechanical Science and Technology (1738-494X)
Volume number: 25
Issue number: 10
Start page: 2683
End page: 2701
Number of pages: 19
ISSN: 1738-494X
eISSN: 1976-3824
Languages: English-Great Britain (EN-GB)
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Abstract
The heat transfer characteristics of R134a during downward condensation are investigated experimentally and numerically. While the convective heat transfer coefficient, two-phase multiplier and frictional pressure drop are considered to be the significant variables as output for the analysis, inputs of the computational numerical techniques include the important two-phase flow parameters such as equivalent Reynolds number, Prandtl number, Bond number, Froude number, Lockhart and Martinelli number. Genetic algorithm technique (GA), unconstrained nonlinear minimization algorithm-Nelder-Mead method (NM) and non-linear least squares error method (NLS) are applied for the optimization of these significant variables in this study. Regression analysis gave convincing correlations on the prediction of condensation heat transfer characteristics using ฑ30% deviation band for practical applications. The most suitable coefficients of the proposed correlations are depicted to be compatible with the large number of experimental data by means of the computational numerical methods. Validation process of the proposed correlations is accomplished by means of the comparison between the various correlations reported in the literature. ฉ 2011 The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg.
Keywords
Nelder-mead method, Non-linear least squares, Unconstrained nonlinear minimization algorithm
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