Time-Series Forecasting of a Typical PWR Undergoing Large Break LOCA
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 t...
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Science and Technology of Nuclear Installations |
| Online Access: | http://dx.doi.org/10.1155/2024/6162232 |
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| author | Michal Kaminski Aya Diab |
| author_facet | Michal Kaminski Aya Diab |
| author_sort | Michal Kaminski |
| collection | DOAJ |
| description | 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 boundary conditions via propagation of key uncertain parameters (UPs) which are derived from the phenomena identification and ranking table (PIRT). This is achieved by loosely coupling RELAP5/MOD3.4 with the statistical tool, Dakota. The most probable nuclear power plant (NPP) response was calculated using the best estimate plus uncertainty (BEPU) approach. Next, the database generated from the NPP system response was used as an input for the ML model. The NPP system response was represented by peak cladding temperature (PCT), safety injection system (SIT), mass flow rate, reactor power, and primary system pressure. In this research, two regression models were tested with reasonably good performance, namely, the gated recurrent unit (GRU) and the long short-term memory (LSTM). |
| format | Article |
| id | doaj-art-1d7ea2add87a435ea19e847aa3108f5d |
| institution | OA Journals |
| issn | 1687-6083 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Science and Technology of Nuclear Installations |
| spelling | doaj-art-1d7ea2add87a435ea19e847aa3108f5d2025-08-20T02:01:40ZengWileyScience and Technology of Nuclear Installations1687-60832024-01-01202410.1155/2024/6162232Time-Series Forecasting of a Typical PWR Undergoing Large Break LOCAMichal Kaminski0Aya Diab1Department of NPP EngineeringDepartment of NPP EngineeringIn 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 boundary conditions via propagation of key uncertain parameters (UPs) which are derived from the phenomena identification and ranking table (PIRT). This is achieved by loosely coupling RELAP5/MOD3.4 with the statistical tool, Dakota. The most probable nuclear power plant (NPP) response was calculated using the best estimate plus uncertainty (BEPU) approach. Next, the database generated from the NPP system response was used as an input for the ML model. The NPP system response was represented by peak cladding temperature (PCT), safety injection system (SIT), mass flow rate, reactor power, and primary system pressure. In this research, two regression models were tested with reasonably good performance, namely, the gated recurrent unit (GRU) and the long short-term memory (LSTM).http://dx.doi.org/10.1155/2024/6162232 |
| spellingShingle | Michal Kaminski Aya Diab Time-Series Forecasting of a Typical PWR Undergoing Large Break LOCA Science and Technology of Nuclear Installations |
| title | Time-Series Forecasting of a Typical PWR Undergoing Large Break LOCA |
| title_full | Time-Series Forecasting of a Typical PWR Undergoing Large Break LOCA |
| title_fullStr | Time-Series Forecasting of a Typical PWR Undergoing Large Break LOCA |
| title_full_unstemmed | Time-Series Forecasting of a Typical PWR Undergoing Large Break LOCA |
| title_short | Time-Series Forecasting of a Typical PWR Undergoing Large Break LOCA |
| title_sort | time series forecasting of a typical pwr undergoing large break loca |
| url | http://dx.doi.org/10.1155/2024/6162232 |
| work_keys_str_mv | AT michalkaminski timeseriesforecastingofatypicalpwrundergoinglargebreakloca AT ayadiab timeseriesforecastingofatypicalpwrundergoinglargebreakloca |