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Application of artificial neural network for the critical flow prediction of discharge nozzle

Xu, Hong; Tang, Tao; Zhang, Baorui; Liu, Yuechan

Abstract:

System thermal-hydraulic (STH) code is adopted for nuclear safety analysis. The critical flow model (CFM) is significant for the accuracy of STH simulation. To overcome the defects of current CFMs (low precision or long calculation time), a CFM based on a genetic neural network (GNN) has been developed in this work. To build a powerful model, besides the critical mass flux, the critical pressure and critical quality were also considered in this model, which was seldom considered before. Comparing with the traditional homogeneous equilibrium model (HEM) and the Moody model, the GNN model can predict the critical mass flux with a higher accuracy (approximately 80% of results are within the ±20% error limit); comparing with the Leung model and the Shannak model for critical pressure prediction, the GNN model achieved the best results (more than 80% prediction results within the ±20% error limit). For the critical quality, similar precision is achieved. The GNN-based CFM in this work is meaningful for the STH code CFM development.


Verlagsausgabe §
DOI: 10.5445/IR/1000138046
Veröffentlicht am 01.10.2021
Originalveröffentlichung
DOI: 10.1016/j.net.2021.08.038
Scopus
Zitationen: 3
Web of Science
Zitationen: 2
Dimensions
Zitationen: 5
Cover der Publikation
Zugehörige Institution(en) am KIT Fakultät für Mathematik (MATH)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 03.2022
Sprache Englisch
Identifikator ISSN: 1738-5733, 0372-7327, 2234-358X
KITopen-ID: 1000138046
Erschienen in Nuclear Engineering and Technology
Verlag Elsevier
Band 54
Heft 3
Seiten 834-841
Vorab online veröffentlicht am 13.09.2021
Schlagwörter Two-phase critical flow; Nuclear safety; Genetic neural network (GNN); Artificial neural network (ANN); Genetic algorithm (GA); Critical pressure ratio
Nachgewiesen in Scopus
Dimensions
Web of Science
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