Application of artificial neural network for heat transfer in porous cone

2018 ◽  
Author(s):  
Abdulgaphur Athani ◽  
N. Ameer Ahamad ◽  
Irfan Anjum Badruddin
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Artificial Neural Network ◽  
Artificial Neural

Application of Artificial Neural Network for the Heat Transfer Investigation Around a High-Pressure Gas Turbine Rotor Blade

2011 ◽  
Author(s):  
Ibrahim Eryilmaz ◽  
Sinan Inanli ◽  
Baris Gumusel ◽  
Suha Toprak ◽  
Cengiz Camci
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Artificial Neural Network ◽  
High Pressure ◽  
Rotor Blade ◽  
Incidence Angle ◽  
Transfer Coefficients ◽  
Experimental Conditions ◽  
Heat Transfer Coefficients ◽  
Artificial Neural

This paper presents the preliminary results of using artificial neural networks in the prediction of gas side convective heat transfer coefficients on a high pressure turbine blade. The artificial neural network approach which has three hidden layers was developed and trained by nine inputs and it generates one output. Input and output data were taken from an experimental research program performed at the von Karman Institute for Fluid Dynamics by Camci and Arts [5,6] and Camci [7]. Inlet total pressure, inlet total temperature, inlet turbulence intensity, inlet and exit Mach numbers, blade wall temperature, incidence angle, specific location of measurement and suction/pressure side specification of the blade were used as input parameters and calculated heat transfer coefficient around a rotor blade used as output. After the network is trained with experimental data, heat transfer coefficients are interpolated for similar experimental conditions and compared with both experimental measurements and CFD solutions. CFD analysis was carried out to validate the algorithm and to determine heat transfer coefficients for a closely related test case. Good agreement was obtained between CFD results and neural network predictions.

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.

A Simple Heat Transfer Correlation for Three Inlet Configurations Using Artificial Neural Network in the Complex Transition Flow Regime

2003 ◽  
Author(s):  
A. J. Ghajar ◽  
L. M. Tam ◽  
S. C. Tam
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Artificial Neural Network ◽  
Least Squares Method ◽  
Transition Flow ◽  
Transfer Coefficients ◽  
Transition Flow Regime ◽  
Bell Mouth ◽  
Data Points ◽  
Artificial Neural

Local forced and mixed heat transfer coefficients were measured by Ghajar and Tam (1994) along a stainless steel horizontal circular tube fitted with reentrant, square-edged, and bell-mouth inlets under uniform wall heat flux condition. For the experiments the Reynolds, Prandtl, and Grashof numbers varied from about 280 to 49000, 4 to 158, and 1000 to 2.5×105, respectively. The heat transfer transition regions were established by observing the change in the heat transfer behavior. The data in the transition region were correlated by using the traditional least squares method. The correlation predicted the transitional data with an average absolute deviation of about 8%. However, 30% of the data were predicted with 10 to 20% deviation. The reason is due to the abrupt change in the heat transfer characteristic and its intermittent behavior. Since the value of heat transfer coefficient has a direct impact on the size of the heat exchanger, a more accurate correlation has been developed using the artificial neural network (ANN). A total of 1290 data points (441 for reentrant, 416 for square-edged, and 433 for bell mouth) were used. The accuracy of the new correlation is excellent with the majority of the data points predicted with less than 10% deviation.

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