A Critical Review on the Determination of Convective Heat Transfer Coefficient During Condensation in Smooth and Enhanced Tubes

Author(s):  
Ahmet Selim Dalkiliç ◽  
Ali Celen ◽  
Mohamed M. Awad ◽  
Somchai Wongwises

Heat exchangers using in-tube condensation have great significance in the refrigeration, automotive and process industries. Effective heat exchangers have been rapidly developed due to the demand for more compact systems, higher energy efficiency, lower material costs and other economic incentives. Enhanced surfaces, displaced enhancement devices, swirl-flow devices and surface tension devices improve the heat transfer coefficients in these heat exchangers. This study is a critical review on the determination of the condensation heat transfer coefficient of pure refrigerants flowing in vertical and horizontal tubes. The authors’ previous publications on this issue, including the experimental, theoretical and numerical analyses are summarized here. The lengths of the vertical and horizontal test sections varied between 0.5 m and 4 m countercurrent flow double-tube heat exchangers with refrigerant flowing in the inner tube and cooling water flowing in the annulus. The measured data are compared to theoretical and numerical predictions based on the solution of the artificial intelligence methods and CFD analyses for the condensation process in the smooth and enhanced tubes. The theoretical solutions are related to the design of double tube heat exchangers in refrigeration, air conditioning and heat pump applications. Detailed information on the in-tube condensation studies of heat transfer coefficient in the literature is given. A genetic algorithm (GA), various artificial neural network models (ANN) such as multilayer perceptron (MLP), radial basis networks (RBFN), generalized regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS), and various optimization techniques such as unconstrained nonlinear minimization algorithm-Nelder-Mead method (NM), non-linear least squares error method (NLS), and Ansys CFD program are used in the numerical solutions. It is shown that the convective heat transfer coefficient of laminar and turbulent condensing film flows can be predicted by means of theoretical and numerical analyses reasonably well if there is a sufficient amount of reliable experimental data. Regression analysis gave convincing correlations, and the most suitable coefficients of the proposed correlations are depicted as compatible with the large number of experimental data by means of the computational numerical methods.

Author(s):  
Ahmet Selim Dalkiliç ◽  
Ali Celen ◽  
Mohamed M. Awad ◽  
Somchai Wongwises

Heat exchangers using in-tube condensation have great significance in the refrigeration, automotive and process industries. Effective heat exchangers have been rapidly developed due to the demand for more compact systems, higher energy efficiency, lower material costs and other economic incentives. Enhanced surfaces, displaced enhancement devices, swirl-flow devices and surface tension devices improve the heat transfer coefficients in these heat exchangers. This study is a critical review on the determination of the condensation heat transfer coefficient of pure refrigerants flowing in vertical and horizontal tubes. The authors’ previous publications on this issue, including the experimental, theoretical and numerical analyses are summarized here. The lengths of the vertical and horizontal test sections varied between 0.5 m and 4 m countercurrent flow double-tube heat exchangers with refrigerant flowing in the inner tube and cooling water flowing in the annulus. The measured data are compared to theoretical and numerical predictions based on the solution of the artificial intelligence methods and CFD analyses for the condensation process in the smooth and enhanced tubes. The theoretical solutions are related to the design of double tube heat exchangers in refrigeration, air conditioning and heat pump applications. Detailed information on the in-tube condensation studies of heat transfer coefficient in the literature is given. A genetic algorithm (GA), various artificial neural network models (ANN) such as multilayer perceptron (MLP), radial basis networks (RBFN), generalized regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS), and various optimization techniques such as unconstrained nonlinear minimization algorithm-Nelder-Mead method (NM), non-linear least squares error method (NLS), and Ansys CFD program are used in the numerical solutions. It is shown that the convective heat transfer coefficient of laminar and turbulent condensing film flows can be predicted by means of theoretical and numerical analyses reasonably well if there is a sufficient amount of reliable experimental data. Regression analysis gave convincing correlations, and the most suitable coefficients of the proposed correlations are depicted as compatible with the large number of experimental data by means of the computational numerical methods.


2020 ◽  
pp. 238-238
Author(s):  
Adel Bouali ◽  
Salah Hanini ◽  
Brahim Mohammedi ◽  
Mouloud Boumahdi

The flow and heat transfer characteristics in a nuclear power plant in the event of a serious accident are simulated by boiling water in an inclined rectangular channel. In this study an artificial neural network model was developed with the aim of predicting heat transfer coefficient (HTC) for flow boiling of water in inclined channel, the network was designed and trained by means of 520 experimental data points that were selected from within the literature. orientation ,mass flux, quality and heat flow which were employed to serve as variables of input of multiple layer perceptron (MLP) neural network, whereas the analogous HTC was selected to be its output. Via the method of trial-and-error, MLP network with 30 neurons in the hidden layer was attained as optimal ANN structure. The fact that is was enabled to predict accurately the HTC. For the training set, the mean relative absolute error (MRAE) is about 0.68 % and the correlation coefficient (R) is about 0.9997. As for the testing and validation set they are respectively about 0.60 % and 0.9998 and about 0.79 % and 0.9996. The comparison of the developed ANN model with experimental data and empirical correlations in vertical channel under the low flow rate and low quality shows a good agreement.


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