2024
DOI: 10.1155/2024/6162232
|View full text |Cite
|
Sign up to set email alerts
|

Time-Series Forecasting of a Typical PWR Undergoing Large Break LOCA

Michal Kaminski,
Aya Diab

Abstract: In this work, a machine learning (ML) metamodel is developed for the time-series forecasting of a typical nuclear power plant response undergoing a loss of coolant accident (LOCA). The plant model of choice is based on the APR1400 nuclear reactor. The key systems and components of APR1400 relevant to the investigated scenario are modelled using the thermal-hydraulic code, RELAP5/MOD3.4, following the description published in the design control document. The model is tested under a spectrum of initial and bound… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
3

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
references
References 38 publications
0
0
0
Order By: Relevance