Applications of Artificial Neural Network for the Prediction of Pool Boiling Curves
- Kyushu University, 6-10-1, Hakozaki, Higashi-ku, Fukuoka 812-81 (Japan)
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 experimental 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 {delta}T{sub w}, 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. (authors)
- Research Organization:
- The ASME Foundation, Inc., Three Park Avenue, New York, NY 10016-5990 (United States)
- OSTI ID:
- 21072812
- Resource Relation:
- Conference: ICONE-10: 10. international conference on nuclear engineering, Arlington - Virginia (United States), 14-18 Apr 2002; Other Information: Country of input: France
- Country of Publication:
- United States
- Language:
- English
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