Prediction of the Pool Boiling Critical Heat Flux Using Artificial Neural Network

2006 ◽  
Vol 29 (4) ◽  
pp. 770-777 ◽  
Author(s):  
H. Metin Ertunc
Keyword(s):  
Neural Network ◽  
Heat Flux ◽  
Artificial Neural Network ◽  
Critical Heat Flux ◽  
Pool Boiling ◽  
Artificial Neural

Applications of Artificial Neural Network for the Prediction of Pool Boiling Curves

2002 ◽  
Author(s):  
Guanghui Su ◽  
K. Fukuda ◽  
K. Morita
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Pool Boiling ◽  
Convergence Criterion ◽  
Training Process ◽  
Transient Heating ◽  
Wall Superheat ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Best Fit

Artificial neural network (ANN) has the advantage that the best-fit correlations of experimental data will no longer be necessary for predicting unknowns from the known parameters. The ANN was applied to predict the pool boiling curves in this paper. The database of experimentel data presented by Berenson, Dhuga et al., and Bui and Dhir etc. were used in the analysis. The database is subdivided in two subsets. The first subset is used to train the network and the second one is used to test the network after the training process. The input parameters of the ANN are: wall superheat ΔTw, surface roughness, steady/transient heating/transient cooling, subcooling, Surface inclination and pressure. The output parameter is heat flux q. The proposed methodology allows us to achieve the accuracy that satisfies the user’s convergence criterion and it is suitable for pool boiling curve data processing.

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