scholarly journals Prediction of the Oscillatory Heat Transfer Coefficient in Thermoacoustic Refrigerators

2021 ◽  
Vol 13 (17) ◽  
pp. 9509
Author(s):  
Mosa Machesa ◽  
Lagouge Tartibu ◽  
Modestus Okwu
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Artificial Neural Network ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Soft Computing ◽  
Particle Swarm Optimisation ◽  
Soft Computing Techniques ◽  
Artificial Neural ◽  
Thermoacoustic Refrigerators

Thermoacoustic refrigerators are emerging devices that make use of meaningful high-pressure sound waves to induce cooling. Despite the accelerated progress in the field of thermoacoustics, knowledge of the heat transfer process in the heat exchange of the devices is still developing. This work applies different soft computing techniques, namely, an artificial neural network trained by particle swarm optimisation (ANN-PSO), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural networks (ANNs) to predict the oscillatory heat transfer coefficient in the heat exchangers of a thermoacoustic device. This study provides the details of the parametric analysis of an artificial neural network model trained by particle swarm optimisation. The solution model considers the number of neurons, the swarm population, and the acceleration factors to develop and analyse the architecture of several models. The regression model (R2) and mean squared error (MSE) were used to evaluate the accuracy of the models. The result showed that the proposed soft computing techniques can potentially be used for the modelling and the analysis of the oscillatory heat transfer coefficient with a higher level of accuracy. The result reported in this study implies that the prediction of the OHTC can be considered for the enhancement of thermoacoustic refrigerators performances.

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.

Prediction of the Heat Transfer Coefficient in a Bubble Column Using an Artificial Neural Network

2008 ◽  
Vol 6 (1) ◽  
Author(s):  
Adel A. Al-Hemiri ◽  
Nada S. Ahmedzeki
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Artificial Neural Network ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Operating Conditions ◽  
Liquid Viscosity ◽  
Input Parameters ◽  
Artificial Neural ◽  
The Heat Transfer Coefficient

An artificial neural network (ANN) was applied for the prediction of the heat transfer coefficient in bubble columns, in order to obtain a general model and to facilitate the scale up of these multiphase contactors, covering a wide range of operating conditions, physical properties, and column dimensions, obtained from literature. A large number of data was collected (more than 1000) via a comprehensive literature survey. Selected parameters affecting the heat transfer coefficient were organized in six groups to serve as the input parameters. These were: gas superficial velocity, gas density, liquid density, diameter of the column, liquid viscosity, and gas hold-up. Four Back-Propagation Networks (BPNNS) were built. Two were trained using a different number of input parameters. The first ANN was trained with six inputs, which were the aforementioned parameters. The second was trained with three inputs only. These were gas velocity, liquid viscosity and gas hold-up. Each ANN was examined for two structures i.e., one hidden layer and two hidden layers. Comparison between these networks was made to find the optimal ANN structure with minimum %AARE and the maximum correlation coefficient (%R). It was found that the ANN structure of [6-13-1] with a %AARE of 16.2 and a %R of 94 was the best.

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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