Prediction of Heat Transfer Coefficient in Flow Boiling over Tube Bundles Using ANFIS

The applicability of the artificial intelligence technique called ANFIS (for adaptive neuro fuzzy inference system) to model the flow boiling heat transfer over a tube bundle is studied in this paper. The ANFIS model is trained and validated with the experimental data from literature. The heat flux, mass flux, and row height are taken as input and the flow boiling heat transfer coefficient as output. The developed model performance is evaluated in terms of performance parameters such as root mean square error, mean square error, correlation coefficient, variance accounted for, and computational time. The preceding parameters of the model are then determined for different combinations of type and number of membership functions. The model is found to predict experimental heat transfer coefficient within an error of ±5%. The developed model is also compared with the artificial neural network model and is found to be better in predicting the flow boiling heat transfer coefficient. The developed model is further used to observe the variation of heat transfer coefficient of the individual rows and bundle for intermediate value of parameters such as heat flux and mass flux that are not included in the analysis of experimental data. The analysis is able to provide complete information about variation of heat transfer coefficient of individual rows and the bundle with respect to heat flux and mass flux.     

ANN modeling of compact heat exchangers

This paper focuses on the heat transfer analysis of compact heat exchangers through artificial neural network (ANN). The ANN analysis includes heat transfer coefficient, pressure drop and Nusselt number in the compact heat exchangers by using available experimental results in a case study. In this study, data sets are established in 15 different test channel configurations. A feed‐forward back‐propagation algorithm is used in the learning process and testing the network. The learning process is applied to correlate the heat transfer analysis for different ratios of rib spacing and height, various Reynolds numbers, different inlet–outlet temperatures, heat transfer areas and hydraulic diameters. Various hidden numbers of the network are trained for the best prediction of the heat transfer analysis. Heat transfer coefficient, pressure drop and Nusselt number values are predicted by the network algorithm. The results are then compared with the experimental results of the case. The trained ANN results perform well in predicting the heat transfer coefficient, pressure drop and Nusselt number with an average absolute mean relative error of less than 6% compared with the experimental results for staggered cylindrical ribbed and staggered triangular ribbed of test channels in the case study. The ANN approach is found to be a suitable method for heat transfer analysis in compact heat exchangers. Copyright © 2007 John Wiley & Sons, Ltd.     

Multi-Objective Optimization of R-404A Vapor Condensation in Swirling Flow Using Genetic Algorithms

Applying twisted tape inserts as a passive improvement technique increases both pressure drop and heat transfer coefficient. In the design of heat exchangers, decreasing of pressure drop and increasing of heat transfer coefficient simultaneously comprise an important aim. In this study, multi-objective optimization is used to find optimum combinations of heat transfer coefficient and pressure drop during condensation of R404A vapor inside twisted-tape-inserted tubes. At first, Pareto-based multi-objective optimization is used to find the proper artificial neural networks based on the experimental data for prediction of heat transfer coefficient and pressure drop. In the next step, Pareto-based multi-objective optimization and previously obtained artificial neural networks are used to find optimal operation conditions that lead to optimum combinations of heat transfer coefficient and pressure drop. The corresponding optimal set of design variables, namely, mass velocity, vapor quality, and dimensional parameters of tubes, show the important design aspects.     

Convective heat transfer and pressure drop study on nanofluids in double-walled reactor by developing an optimal multilayer perceptron artificial neural network

The effect of nanofluid on the cooling performance and pressure drop of a jacked reactor has experimentally been investigated. Aqueous nanofluids of Al₂O₃ and CuO was used as the cool ant inside the cooling jacket of the reactor. The application of the artificial neural networks (ANNs) to predict the performance of a double-walled reactor has been studied. Different architectures of artificial neural networks were developed to predict the convective heat transfer and pressure drop of nanofluids. The experimental results are used for training and testing the ANNs based on two optimal models via feed-forward back-propagation multilayer perceptron (MLP). The comparison of statistical criteria of different network shows that the optimal structure for predicting the convective heat transfer coefficient is the MLP network with one hidden layer and 10 neurons, which has been trained with Levenberg–Marquardt (LM) algorithm. The predicted pressure drop values by the MLP network with two hidden layers and 6 neurons in the each layer has been used from LM training algorithm, which showed a reasonable agreement with the experimental results.     

Prediction of Heat Transfer Rates for Shell-and-Tube Heat Exchangers by Artificial Neural Networks Approach

This work used artificial neural network(ANN)to predict the heat transfer rates of shell-and-tube heatexchangers with segmental baffles or continuous helical baffles,based on limited experimental data.The BackPropagation (BP) algorithm was used in training the networks.Different network configurations were alsostudied.The deviation between the predicted results and experimental data was less than 2%.Comparison withcorrelation for prediction shows ANN superiority.It is recommended that ANN can be easily used to predict theperformances of thermal systems in engineering applications,especially to model heat exchangers for heattransfer analysis.     

Modeling of heat transfer coefficient in the furnace of CFB boilers by artificial neural network approach

The present work introduces a way of predicting the local heat transfer coefficient in the combustion chamber of the circulating fluidized bed boiler (CFB) by the artificial neural network (ANN) approach.Neural networks have been successfully applied to calculate the local overall heat transfer coefficient for membrane walls, Superheater I (SH I, Omega Superheater) and Superheater II (SH II, Wing-Walls) in the combustion chamber of the 260 MWe CFB boiler. The previously verified numerical model has been used to obtain the overall heat transfer coefficients, necessary for training and testing the ANN. It has been shown, that the neural networks give quick and accurate results as an answer to the input pattern. The local heat transfer coefficients evaluated using the developed ANN model have been in a good agreement with numerical and experimental results.     

Heat transfer analysis of phase change process in a finned-tube thermal energy storage system using artificial neural network

In this study, a feed-forward back-propagation artificial neural network (ANN) algorithm is proposed for heat transfer analysis of phase change process in a finned-tube, latent heat thermal energy storage system. Heat storage through phase change material (PCM) around the finned tube is experimentally studied. A numerical study is performed to investigate the effect of fin and flow parameter by the solving governing equations for the heat transfer fluid, pipe wall and phase change material. Learning process is applied to correlate the total heat stored in different fin types of tubes, various Reynolds numbers and different inlet temperatures. A number of hidden numbers of ANN are trained for the best output prediction of the heat storage. The predicted total heat storage values obtained by an ANN model with extensive sets of non-training experimental data are then compared with experimental measurements and numerical results. The trained ANN model with an absolute mean relative error of 5.58% shows good performance to predict the total amount of heat stored. The ANN results are found to be more accurate than the numerical model results. The present study using ANN approach for heat transfer analysis in phase change heat storage process appears to be significant for practical thermal energy storage applications.     

Performance predictions of laminar and turbulent heat transfer and fluid flow of heat exchangers having large tube-diameter and large tube-row by artificial neural networks

In this work an artificial neural network (ANN) is used to correlate experimentally determined and numerically computed Nusselt numbers and friction factors of three kinds of fin-and-tube heat exchangers having plain fins, slit fins and fins with longitudinal delta-winglet vortex generators with large tube-diameter and large the number of tube rows. First the experimental data for training the network was picked up from the database of nine samples with tube outside diameter of 18 mm, number of tube rows of six, nine, twelve, and Reynolds number between 4000 and 10,000. The artificial neural network configuration under consideration has twelve inputs of geometrical parameters and two outputs of heat transfer Nusselt number and fluid flow friction factor. The commonly-implemented feed-forward back propagation algorithm was used to train the neural network and modify weights. Different networks with various numbers of hidden neurons and layers were assessed to find the best architecture for predicting heat transfer and flow friction. The deviation between the predictions and experimental data was less than 4%. Compared to correlations for prediction, the performance of the ANN-based prediction exhibits ANN superiority. Then the ANN training database was expanded to include experimental data and numerical data of other similar geometries by computational fluid dynamics (CFD) for turbulent and laminar cases with the Reynolds number of 1000–10,000. This in turn indicated the prediction has a good agreement with the combined database. The satisfactory results suggest that the developed ANN model is generalized to predict the turbulent or/and laminar heat transfer and fluid flow of such three kinds of heat exchangers with large tube-diameter and large number of tube rows. Also in this paper the weights and biases corresponding to the neural network architecture are provided so that future research can be carried out. It is recommended that ANNs might be used to predict the performances of thermal systems in engineering applications, especially to model heat exchangers for heat transfer analysis.     

Predictions of heat transfer coefficients of supercritical carbon dioxide using the overlapped type of local neural network

An overlapped type of local neural network is proposed to improve accuracy of the heat transfer coefficient estimation of the supercritical carbon dioxide. The idea of this work is to use the network to estimate the heat transfer coefficient for which there is no accurate correlation model due to the complexity of the thermo-physical properties involved around the critical region. Unlike the global approximation network (e.g. backpropagation network) and the local approximation network (e.g. the radial basis function network), the proposed network allows us to match the quick changes in the near-critical local region where the rate of heat transfer is significantly increased and to construct the global smooth perspective far away from that local region. Based on the experimental data for carbon dioxide flowing inside a heated tube at the supercritical condition, the proposed network significantly outperformed some the conventional correlation method and the traditional network models.     

Mechanisms proposed through experimental investigations on thermophysical properties and forced convective heat transfer characteristics of various nanofluids – A review

Experimental investigations on thermophysical properties and forced convective heat transfer characteristics of various nanofluids are reviewed and the mechanisms proposed for the alteration in their values or characteristics due to the addition of nanoparticles are summarized in this review. A comprehensive review on the experimental works on specific application of nanofluids is also presented. As the literature in this area is spread over a span of two decades, this review could be useful for researchers to have an accurate screening of wide range of experimental investigations on thermophysical properties, forced convective heat transfer characteristics, the mechanisms involved and applications of various nanofluids.     

Investigation of pool boiling of nanofluids using artificial neural networks and correlation development techniques

The nucleate pool boiling heat transfer characteristics of TiO₂ nanofluids are investigated to determine the important parameters' effects on the heat transfer coefficient and also to have reliable empirical correlations based on the neural network analysis. Nanofluids with various concentrations of 0.0001, 0.0005, 0.005, and 0.01 vol.% are employed. The horizontal circular test plate, made from copper with different roughness values of 0.2, 2.5 and 4 μm, is used as a heating surface. The artificial neural network (ANN) training sets have the experimental data of nucleate pool boiling tests, including temperature differences between the temperatures of the average heater surface and the liquid saturation from 5.8 to 25.21 K, heat fluxes from 28.14 to 948.03 kW m^− 2. The pool boiling heat transfer coefficient is calculated using the measured results such as current, voltage, and temperatures from the experiments. Input of the ANNs are the 8 numbers of dimensional and dimensionless values of the test section, such as thermal conductivity, particle size, physical properties of the fluid, surface roughness, concentration rate of nanoparticles and wall superheating, while the outputs of the ANNs are the heat flux and experimental pool boiling heat transfer coefficient from the analysis. The nucleate pool boiling heat transfer characteristics of TiO₂ nanofluids are modeled to decide the best approach, using several ANN methods such as multi-layer perceptron (MLP), generalized regression neural network (GRNN) and radial basis networks (RBF). Elimination process of the ANN methods is performed together with the copper and aluminum test sections by means of a 4-fold cross validation algorithm. The ANNs performances are measured by mean relative error criteria with the use of unknown test sets. The performance of the method of MLP with 10-20-1 architecture, GRNN with the spread coefficient 0.7 and RBFs with the spread coefficient of 1000 and a hidden layer neuron number of 80 are found to be in good agreement, predicting the experimental pool boiling heat transfer coefficient with deviations within the range of ± 5% for all tested conditions. Dependency of output of the ANNs from input values is investigated and new ANN based heat transfer coefficient correlations are developed, taking into account the input parameters of ANNs in the paper.     

Neural networks analysis of thermal characteristics on plate-fin heat exchangers with limited experimental data

In this paper, an application of artificial neural networks (ANNs) was presented to predict the pressure drop and heat transfer characteristics in the plate-fin heat exchangers (PFHEs). First, the thermal performances of five different PFHEs were evaluated experimentally. The Colburn factor j and friction factor f to different type fins were obtained under various experimental conditions. Then, a feed-forward neural network based on back propagation algorithm was developed to model the thermal performance of the PFHEs. The ANNs was trained using the experimental data to predict j and f factors in PFHEs. Different network configurations were also examined for searching a better network for prediction. The predicted values were found to be in good agreement with the actual values from the experiments with mean squared errors (MSE) less than 1.5% for j factor and 1% for f factor, respectively. This demonstrated that the neural network presented can help the engineers and manufacturers predict the thermal characteristics of new type fins in PFHEs under various operating conditions.     

A new approach for the prediction of the heat transfer rate of the wire-on-tube type heat exchanger––use of an artificial neural network model

This study presents an application of artificial neural networks (ANNs) to predict the heat transfer rate of the wire-on-tube type heat exchanger. A back propagation algorithm, the most common learning method for ANNs, is used in the training and testing of the network. To solve this algorithm, a computer program was developed by using C++ programming language. The consistence between experimental and ANNs approach results was achieved by a mean absolute relative error <3%. It is suggested that the ANNs model is an easy modeling tool for heat engineers to obtain a quick preliminary assessment of heat transfer rate in response to the engineering modifications to the exchanger.     

Non-iterative estimation of heat transfer coefficients using artificial neural network models

The Inverse Heat Conduction Problem (IHCP) dealing with the estimation of the heat transfer coefficient for a solid /fluid assembly from the knowledge of inside temperature was accomplished using an artificial neural network (ANN). Two cases were considered: (a) a cube with constant thermophysical properties and (b) a semi-infinite plate with temperature dependent thermal conductivity resulting in linear and nonlinear problem, respectively. The Direct Heat Conduction Problems (DHCP) of transient heat conduction in a cube and in a semi-infinite plate with a convective boundary condition were solved. The dimensionless temperature-time history at a known location was then correlated with the corresponding dimensionless heat transfer coefficient/Biot number using appropriate ANN models. Two different models were developed for each case i.e. for a cube and a semi-infinite plate. In the first one, the ANN model was trained to predict Biot number from the slope of the dimensionless temperature ratio versus Fourier number. In the second, an ANN model was developed to predict the dimensionless heat transfer coefficient from non-dimensional temperature. In addition, the training data sets were transformed using a trigonometric function to improve the prediction performance of the ANN model. The developed models may offer significant advantages when dealing with repetitive estimation of heat transfer coefficient. The proposed approach was tested for transient experiments. A ‘parameter estimation’ approach was used to obtain Biot number from experimental data.     

Artificial neural network modeling of nanofluid flow in a microchannel heat sink using experimental data

The present paper deals with the artificial neural network modeling (ANN) of heat transfer coefficient and Nusselt number in TiO₂/water nanofluid flow in a microchannel heat sink. The microchannel comprises of 40 channels; each channel has a length of 4 cm, a width of 500 μm, and a height of 800 μm. In the ANN modeling of heat transfer coefficient and Nusselt number 23 and 72 datasets have been used, respectively. The experimental Nusselt number has been calculated based on three different thermal conductivity models, four volume fractions of 0, 0.5, 1, and 2%, two values of Reynolds number i.e. 400 and 1200 and three different heating rates including 50.6, 60.7, and 69.1 W. Therefore, the inputs that are introduced to the neural network are volume fraction of nanoparticles, Reynolds number, heating rate, and model number while the output of network is the Nusselt number. It is elucidated that an appropriately trained network can act as a good alternative for costly and time-consuming experiments on the nanofluid flow in microchannels. The average relative errors in the prediction of Nusselt number and heat transfer coefficients were 0.3% and 0.2%, respectively.     

Heat transfer using a correlation by neural network for natural convection from vertical helical coil in oil and glycerol/water solution

A physical-empirical model is designed to describe heat transfer of helical coil in oil and glycerol/water solution. It includes an artificial neural network (ANN) model working with equations of continuity, momentum and energy in each flow. The discretized equations are coupled using an implicit step by step method. The natural convection heat transfer correlation based on ANN is developed and evaluated. This ANN considers Prandtl number, Rayleigh number, helical diameter and coils turns number as input parameters; and Nusselt number as output parameter. The best ANN model was obtained with four neurons in the hidden layer with good agreement (R > 0.98). Helical coil uses hot water for the inlet flow; heat transfer by conduction in the internal tube wall is also considered. The simulated outlet temperature is carried out and compared with the experimental database in steady-state. The numerical results for the simulations of the heat flux, for these 91 tests in steady-state, have a R ≥ 0.98 with regard to experimental results. One important outcome is that this ANN correlation is proposed to predict natural convection heat transfer coefficient from helical coil for both fluids: oil and glycerol/water solution, thus saving time and improving general system performance.     

New correlations for heat transfer coefficients during direct cooling of products

An analytical method for determining the heat transfer coefficients of food products being cooled in water and in air flows is presented. Food products are idealized as geometrical solid objects of regular shapes. New correlations between heat transfer coefficients and cooling coefficients are developed in simple forms for practical use in the refrigeration industry. These correlations are then used to determine the heat transfer coefficient for a cylindrical carrot cooled in air flow as an illustrative example. In addition, evaluating the heat transfer coefficients for several products using the available experimental cooling coefficient values from the literature, two new correlations between the heat transfer coefficient and the cooling coefficient are also obtained for water and air cooling applications. The results show that the correlations presented in this article can determine the heat transfer coefficients of food products forced-convection cooling in a simple and accurate manner.     

Two applications of a numerical approach of heat transfer process within rock beds

A general method for solving the differential equations describing the heat transfer process within a rock bed is presented. A numerical model accounting for secondary phenomena such as thermal losses and conduction effect is developed. The results of the study are presented in the form of curves and empirical equations. Two applications of this theoretical model are then investigated. One is the elaboration of a new calculation method for the volumetric convective heat transfer coefficient using the compared results of theoretical modeling and experimental tests. The second application is a design method for solar applications of rock-bed storage with determination of optimal values for parameters such as air velocity, particle diameter and geometrical aspects of the storage unit.     

椭圆管散热器传热及阻力性能试验研究

对空气横掠片距不相等的叉排椭圆翅片管散热器的传热及阻力性能进行了试验研究,得到试件在一系列工况下的传热与管外流动阻力数据,并对试验数据进行分析计算,从总传热系数K中分离出管外空气侧的对流换热系数h,给出有工程应用价值的管外换热准则关系式及管外阻力准则关系式。认为椭圆管管外的平均换热效果优于圆管。在相同的流通截面积下椭圆管传热周边较长,换热面积相应增加,因此结构上允许布置得更紧凑。     

Prediction of the air temperature and humidity at the outlet of a cooling coil using neural networks

The main objective of this study is to predict air temperature and humidity at the outlet of a wire-on-tube type heat exchanger using neural networks. For this purpose, initially the heat exchanger was coupled to a refrigeration unit and placed in a wind tunnel. Afterwards, its performance was tested under various experimental conditions. We measured nine input parameters, namely, temperature and humidity of the air entering the coil, air velocity, frost weight, the temperature at the coil surface, mass flow rate of the heat transfer fluid and its temperatures at the inlet and outlet of the coil along with ambient temperature. Additionally, we measured temperature and humidity of the air leaving the coil as the output parameters. Then, a feed-forward neural network based on backpropagation algorithm was developed to model the thermal performance of the coil. The artificial neural network (ANN) was trained using the experimental data to predict the air conditions at the outlet of the coil. The predicted values are found to be in good agreement with the actual values from the experiments with mean relative errors less than 1% for outlet air temperature and 2% for outlet humidity. This demonstrates that the neural network presented can help the manufacturer predict the performance of cooling coils in air-conditioning systems under various operating conditions.