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.

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.

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.

Forced Convection Heat Transfer of Nanofluids: A Review

2017 ◽  
Author(s):  
Aditya Kuchibhotla ◽  
Debjyoti Banerjee
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Thermal Conductivity ◽  
Heat Capacity ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Specific Heat ◽  
Forced Convection ◽  
Convection Heat Transfer ◽  
Convection Heat

Stable homogeneous colloidal suspensions of nanoparticles in a liquid solvents are termed as nanofluids. In this review the results for the forced convection heat transfer of nanofluids are gleaned from the literature reports. This study attempts to evaluate the experimental data in the literature for the efficacy of employing nanofluids as heat transfer fluids (HTF) and for Thermal Energy Storage (TES). The efficacy of nanofluids for improving the performance of compact heat exchangers were also explored. In addition to thermal conductivity and specific heat capacity the rheological behavior of nanofluids also play a significant role for various applications. The material properties of nanofluids are highly sensitive to small variations in synthesis protocols. Hence the scope of this review encompassed various sub-topics including: synthesis protocols for nanofluids, materials characterization, thermo-physical properties (thermal conductivity, viscosity, specific heat capacity), pressure drop and heat transfer coefficients under forced convection conditions. The measured values of heat transfer coefficient of the nanofluids varies with testing configuration i.e. flow regime, boundary condition and geometry. Furthermore, a review of the reported results on the effects of particle concentration, size, temperature is presented in this study. A brief discussion on the pros and cons of various models in the literature is also performed — especially pertaining to the reports on the anomalous enhancement in heat transfer coefficient of nanofluids. Furthermore, the experimental data in the literature indicate that the enhancement observed in heat transfer coefficient is incongruous compared to the level of thermal conductivity enhancement obtained in these studies. Plausible explanations for this incongruous behavior is explored in this review. A brief discussion on the applicability of conventional single phase convection correlations based on Newtonian rheological models for predicting the heat transfer characteristics of the nanofluids is also explored in this review (especially considering that nanofluids often display non-Newtonian rheology). Validity of various correlations reported in the literature that were developed from experiments, is also explored in this review. These comparisons were performed as a function of various parameters, such as, for the same mass flow rate, Reynolds number, mass averaged velocity and pumping power.

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 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.

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