A Critical Review on the Determination of Pressure Drop During Condensation in Smooth and Enhanced Tubes

2013 ◽  
Author(s):  
Ahmet Selim Dalkiliç ◽  
Ali Celen ◽  
Mohamed M. Awad ◽  
Somchai Wongwises
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Experimental Data ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Critical Review ◽  
Heat Exchangers ◽  
Numerical Analyses ◽  
Enhanced Tubes

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.

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

2013 ◽  
Author(s):  
Ahmet Selim Dalkiliç ◽  
Ali Celen ◽  
Mohamed M. Awad ◽  
Somchai Wongwises
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Experimental Data ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Critical Review ◽  
Heat Exchangers ◽  
Convective Heat Transfer Coefficient ◽  
Enhanced Tubes

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.

scholarly journals Using artificial neural network for predicting heat transfer coefficient during flow boiling in an inclined channel

2020 ◽  
pp. 238-238
Author(s):  
Adel Bouali ◽  
Salah Hanini ◽  
Brahim Mohammedi ◽  
Mouloud Boumahdi
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Flow Boiling ◽  
Low Flow ◽  
Inclined Channel ◽  
Artificial Neural

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.

Experimental investigations on nucleate pool boiling over micro-finned cylindrical surfaces

2022 ◽  
pp. 095440622110627
Author(s):  
Balkrushna Shah ◽  
Kathit Shah ◽  
Parth Patel ◽  
Vikas J Lakhera
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Dimensional Analysis ◽  
Heat Exchangers ◽  
Boiling Heat Transfer ◽  
Pool Boiling ◽  
Experimental Investigations ◽  
Boiling Heat Transfer Coefficient

The nucleate pool boiling heat transfer over micro-finned cylindrical surfaces has application in the heat exchangers used in thermal power plants and chemical industries. The estimation of boiling heat transfer coefficient is an important parameter in the design of two-phase heat exchangers using micro-finned cylindrical surfaces. In the present work, related experimental investigations on four micro-finned cylindrical surfaces with different surface geometry using refrigerant R-141b at atmospheric pressure are conducted to determine the boiling heat transfer coefficient over micro-finned cylindrical surfaces. A correlation is developed by dimensional analysis wherein the effects of geometrical parameters, operating pressure and thermo-physical properties of fluids are taken into consideration and dimensional analysis conducted using Buckingham π-theorem. The correlation developed utilizes experimental data obtained over the present study as well as from previous studies by various researchers including experimental data for water over different micro-finned cylindrical surfaces at 1 bar by Mehta and Kandlikar, experimental data for R123 at 0.97 bar by Saidi et al. and experimental data for R134a over micro-finned cylindrical surface at 6.1 bar, 8.1 bar, 10.1 bar and 12.2 bar by Rocha et al. The heat flux ranging from 5 to 1100 kW/m2 are considered for the analysis. The data points have been compared with the proposed correlation and the absolute average deviation of the whole data set was obtained as 13.43% with root mean square deviation of 0.0273. All the predicted values were within ±15% of the experimental values of the boiling heat transfer coefficient.

A Critical Review on the Numerical Methods of Two-Phase Flows

2012 ◽  
Author(s):  
Muhammet Balcilar ◽  
Ahmet Selim Dalkiliç ◽  
Ali Çelen ◽  
Nurullah Kayaci ◽  
Somchai Wongwises
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Experimental Data ◽  
Numerical Methods ◽  
Critical Review ◽  
Optimization Techniques ◽  
Numerical Analyses ◽  
Heat Transfer Characteristics ◽  
Two Phase ◽  
Transfer Characteristics

The two-phase flow processes play a significant role in the heat transfer processes in the chemical and power industry, including in nuclear power plants. This study is a critical review on the determination of the heat transfer characteristics of pure refrigerants flowing in vertical and horizontal tubes. The authors’ previous publications on this issue, including the 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 numerical predictions based on the solution of the artificial intelligence methods and CFD analyses for the condensation and evaporation processes in the smooth and enhanced tubes. The theoretical solutions are related to the design of passive containment cooling systems (PCCS) in simplified water boiling reactors (SWBR). 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 Fluent CFD program are used in the numerical solution. It is shown that the heat transfer characteristics of laminar and turbulent condensing and evaporating film flows such as heat transfer coefficient and pressure drop can be predicted by means of 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. Dependency of the output of the ANNs from various numbers of input values is also shown for condensing and evaporating flows.

scholarly journals Modeling of overall heat transfer coefficient of a concentric double pipe heat exchanger with limited experimental data by using curve fitting and ANN combination

2019 ◽  
Vol 23 (6 Part A) ◽  
pp. 3579-3590 ◽  
Author(s):  
Necati Kocyigit ◽  
Huseyin Bulgurcu
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Heat Transfer Coefficient ◽  
Heat Exchanger ◽  
Transfer Coefficient ◽  
Curve Fitting ◽  
Overall Heat Transfer Coefficient ◽  
Artificial Neural

The modeling accuracy of artificial neural networks (ANN) was evaluated by using limited heat exchanger data acquired experimentally. The artificial neural networks were used for predicting the overall heat transfer coefficient of a concentric double pipe heat exchanger where oil flowed inside the inner tube while the water flowed in the outer tube. In the cases of parallel and counter flows, the experimental data were collected by testing heat exchanger in wide range of operating conditions. Curve fitting and artificial neural network combination was used for the estimation of the overall heat transfer coefficient to compensate the experimental errors in the data. The curve fitting was used to detect the trend and generate data points between the experimentally collected points. The artificial neural network was trained better from the generated data set. The feed forward type artificial neural network was trained by using the Levenberg-Marquardt algorithm. Two backpropagation network type artificial neural network algorithms were also used, and their performance were compared with the estimation of the Levenberg-Marquardt algorithm. The average estimation error between the predictions and the experimental data were in the range of 1.31e?4 to 4.35e?2%. The study confirmed that curve fitting and artificial neural network combination could be used effectively to estimate the overall heat transfer coefficient of heat exchanger.

Neural Network Analysis of Free Convection Around Isothermal Elliptic Tube

2006 ◽  
Author(s):  
Mehdi Ashjaee ◽  
Reza Afzali ◽  
Mohammad Niknami ◽  
Mehdi Amiri ◽  
Tooraj Yousefi
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Experimental Data ◽  
Heat Transfer Coefficient ◽  
Free Convection ◽  
Transfer Coefficient ◽  
Convection Heat Transfer ◽  
Axis Ratio ◽  
Elliptical Cross ◽  
Data Points

An artificial neural network (ANN) was applied successfully to predict laminar free convection heat transfer coefficient from an isothermal horizontal cylinder of elliptical cross section confined between two adiabatic walls. Neural networks were used since they constitute a general, powerful function-approximator tool proving able to represent a convectional heat transfer coefficient precisely in the present case. The input database for the network includes 171 experimental data points. The experiment is carried out using Mach-Zehnder Interferometry. Tube axis ratio, wall spacing to miner axis ratio of tube and Rayleigh number are variable parameters or the experimental study. The values of the average Nusselt numbers predicted by the network are in very good agreement with the available experimental data. Therefore the network is used to predict the unavailable data points within the range of our experimental results.

Development of a New Correlation and Post Processing of Heat Transfer Coefficient and Pressure Drop of Functionalized COOH MWCNT Nanofluid by Artificial Neural Network

2018 ◽  
Vol 14 (2) ◽  
pp. 104-112 ◽  
Author(s):  
Mohammad Hemmat Esfe ◽  
Somchai Wongwises ◽  
Saeed Esfandeh ◽  
Ali Alirezaie
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Heat Transfer Coefficient ◽  
Pressure Drop ◽  
Transfer Coefficient ◽  
Regression Coefficient ◽  
New Correlation ◽  
The Neural Network

Background: Because of nanofluids applications in improvement of heat transfer rate in heating and cooling systems, many researchers have conducted various experiments to investigate nanofluid's characteristics more accurate. Thermal conductivity, electrical conductivity, and heat transfer are examples of these characteristics. Method: This paper presents a modeling and validation method of heat transfer coefficient and pressure drop of functionalized aqueous COOH MWCNT nanofluids by artificial neural network and proposing a new correlation. In the current experiment, the ANN input data has included the volume fraction and the Reynolds number and heat transfer coefficient and pressure drop considered as ANN outputs. Results: Comparing modeling results with proposed correlation proves that the empirical correlation is not able to accurately predict the experimental output results, and this is performed with a lot more accuracy by the neural network. The regression coefficient of neural network outputs was equal to 99.94% and 99.84%, respectively, for the data of relative heat transfer coefficient and relative pressure drop. The regression coefficient for the provided equation was also equal to 97.02% and 77.90%, respectively, for these two parameters, which indicates this equation operates much less precisely than the neural network. Conclusion: So, relative heat transfer coefficient and pressure drop of nanofluids can also be modeled and estimated by the neural network, in addition to the modeling of nanofluid’s thermal conductivity and viscosity executed by different scholars via neural networks.

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