Using artificial intelligence to identify the success window of FLEX strategy under an extended station blackout

Alketbi, Salama; Diab, Aya

doi:10.1016/j.nucengdes.2021.111368

Cited by 8 publications

(4 citation statements)

References 15 publications

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“…However, the maximum value of the peak cladding temperature remains below the fuel acceptance criterion of 1477.0 K. As the decay heat decreases over time, the cladding temperature begins to decrease as well, demonstrating the effectiveness of the pressurized water reactor's response to beyond-design-basis external events. The PCTs are consistently kept well below the safety limit of 1477K, underscoring the efficacy of pressurized water reactors (PWRs) in coping with LOCA, particularly in light of the successful operation of FLEX equipment (Alketbi and Diab, 2021).…”

Section: Resultsmentioning

confidence: 94%

“…This meant that all active safety systems and critical safety functions were unavailable, jeopardizing the safety of the plant. However, critical safety functions could be restored by installing replacement AC power to run the basic equipment, (Alketbi and Diab, 2021). In the aftermath of the Fukushima accident and its implications for public acceptance of nuclear energy, the Nuclear Energy Institute (NEI) of the United States proposed the concept of Diverse and Flexible Coping Strategies (FLEX) and the corresponding FLEX Support Guidelines (FSG) for special accidents caused by external events.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty study of LOCA accidents for developing FLEX strategy in nuclear power plants

DUO,

HUANG,

2024

Mechanical Engineering Journal

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As an aftermath of the Fukushima nuclear accident and its implications for public acceptance of nuclear energy, the nuclear industry has begun to seriously rethink and improve the safety strategies of nuclear power plants. The beyond-design-basis external events, such as the earthquake and tsunami in Fukushima nuclear accident, may lead to an extreme accident which may lead to the failure of the traditional strategies. In the recent decade, the concept of FLEX strategies has been accepted in the nuclear engineering community and attracted a large number of researchers to investigate it for nuclear accident mitigation. The development of FLEX strategies is divided into two steps. One is an uncertainty study of system response, especially the safety injection system. The other is training and testing of machine learning (ML)-based FLEX strategies and to verify the effectiveness of ML algorithms in the development of FLEX strategies for nuclear power plants. This paper focuses on first step uncertainty study of the nuclear accidents by using statistical algorithm to generate a database of incidents and system responses. The conclusion of this paper is useful for analyzing the characteristic of typical nuclear accidents and benefits to the FLEX strategy development, which will be focused on in the near future.

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Section: Resultsmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Uncertainty study of LOCA accidents for developing FLEX strategy in nuclear power plants

DUO,

HUANG,

2024

Mechanical Engineering Journal

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“…A fast-running model conditional autoencoder (CAE), known as an auto-associative neural network (AANN), can predict the pressure as well as peak cladding temperature (PCT) in ARP1400 under a small break LOCA (SBLOCA) scenario [6]. Similarly, an ANN model that can also support the implementation of the diverse and fexible coping strategy (FLEX) has been developed for an extended SBO [7,8]. Prediction of the peak cladding temperature (PCT) and infuence of the FLEX strategy for APR1400 undergoing an extended station blackout (SBO) was performed using an artifcial neural network (ANN) [9].…”

Section: Introductionmentioning

confidence: 99%

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

Kaminski,

Diab

2024

Science and Technology of Nuclear Installations

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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).

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“…Recently, the International Atomic Energy Agency (IAEA) has urged the nuclear community to integrate ML in the industry within the framework of emerging technologies, given its superior capability in handling big-data (IAEA, 2020). In fact, the potential of using ML technology has been explored to estimate some key figures of merit such as the power pin peaking factor (Bae et al, 2008), the wall temperature at critical heat flux (Park et al, 2020), the flow pattern identification (Lin, 2020), to detect anomalies and warn of equipment failure (Ahsan and Hassan, 2013;Chen and Jahanshahi, 2018;Devereux et al, 2019); to determine core configuration and core loading pattern optimization (Siegelmann et al, 1997;Faria and Pereira, 2003;Erdogan and Gekinli, 2003;Zamer et al, 2014;Nissan, 2019), to identify initiating events and categorize accidents (Santosh et al, 2003;Na et al, 2004;Lee and Lee, 2006;Ma and Jiang, 2011;Pinheiro et al, 2020;Farber and Cole, 2020) and to determine of key performance metrics and safety parameters (Ridlluan et al, 2009;Montes et al, 2009;Farshad Faghihi and Seyed, 2011;Patra et al, 2012;Young, 2019;Park et al, 2020;Alketbi and Diab, 2021), and in radiation protection for isotope identification and classification (Keller and Kouzes, 1994;Abdel-Aal and Al-Haddad, 1997;Chen, 2009;Kamuda and Sullivan, 2019), etc. However, it is worth noting that the application of ML in nuclear safety is still limited despite its potential to enhance performance, safety, as well as economics of plant operation (Chai et al, 2003) which warrants further research (Gomez Fernandez et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning to Predict the Fuel Peak Cladding Temperature for a Large Break Loss of Coolant Accident

Sallehhudin

Diab

2021

Front. Energy Res.

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In this paper the use of machine learning (ML) is explored as an efficient tool for uncertainty quantification. A machine learning algorithm is developed to predict the peak cladding temperature (PCT) under the conditions of a large break loss of coolant accident given the various underlying uncertainties. The best estimate approach is used to simulate the thermal-hydraulic system of APR1400 large break loss of coolant accident (LBLOCA) scenario using the multidimensional reactor safety analysis code (MARS-KS) lumped parameter system code developed by Korea Atomic Energy Research Institute (KAERI). To generate the database necessary to train the ML model, a set of uncertainty parameters derived from the phenomena identification and ranking table (PIRT) is propagated through the thermal hydraulic model using the Dakota-MARS uncertainty quantification framework. The developed ML model uses the database created by the uncertainty quantification framework along with Keras library and Talos optimization to construct the artificial neural network (ANN). After learning and validation, the ML model can predict the peak cladding temperature (PCT) reasonably well with a mean squared error (MSE) of ∼0.002 and R2 of ∼0.9 with 9 to 11 key uncertain parameters. As a bounding accident scenario analysis of the LBLOCA case paves the way to using machine learning as a decision making tool for design extension conditions as well as severe accidents.

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Using artificial intelligence to identify the success window of FLEX strategy under an extended station blackout

Cited by 8 publications

References 15 publications

Uncertainty study of LOCA accidents for developing FLEX strategy in nuclear power plants

Uncertainty study of LOCA accidents for developing FLEX strategy in nuclear power plants

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

Using Machine Learning to Predict the Fuel Peak Cladding Temperature for a Large Break Loss of Coolant Accident

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