Applications of artificial neural network for the prediction of pool boiling curves

Guanghui Su, K. Fukuda, Koji Morita

Research output: Contribution to conferencePaperpeer-review

4 Citations (Scopus)

Abstract

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 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.

Original languageEnglish
Pages853-860
Number of pages8
DOIs
Publication statusPublished - Oct 19 2002
Event10th International Conference on Nuclear Engineering (ICONE 10) - Arlington, VA, United States
Duration: Apr 14 2002Apr 18 2002

Other

Other10th International Conference on Nuclear Engineering (ICONE 10)
CountryUnited States
CityArlington, VA
Period4/14/024/18/02

All Science Journal Classification (ASJC) codes

  • Nuclear Energy and Engineering

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