Nuclear Power Plant accident identification system with “don’t know” response capability: Novel deep learning-based approaches

Pinheiro, Victor Henrique Cabral; Santos, Marcelo C.; Desterro, Filipe Santana Moreira do; Schirru, Roberto; Pereira, Cláudio Márcio do Nascimento Abreu

doi:10.1016/j.anucene.2019.107111

Cited by 28 publications

(2 citation statements)

References 17 publications

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“…It has the ability to standardize, serialize, mass produce, research, and develop a new generation of the intelligent nuclear industry. Therefore, applying DL technologies to all segments of the complete nuclear industry chain can promote the development of informatization, digitization, intelligence of the nuclear industry [51][52][53] . Among them, the nuclear power industry involves nuclear fuel supply, nuclear power equipment manufacturing, nuclear power engineering design and construction, nuclear power technical service and guarantee, nuclear spent fuel reprocessing, radioactive waste disposal, etc.…”

Section: Introductionmentioning

confidence: 99%

Deep learning in nuclear industry: A survey

Tang

Gao

et al. 2022

Big Data Min. Anal.

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As a high-tech strategic emerging comprehensive industry, the nuclear industry is committed to the research, production, and processing of nuclear fuel, as well as the development and utilization of nuclear energy.Nowadays, the nuclear industry has made remarkable progress in the application fields of nuclear weapons, nuclear power, nuclear medical treatment, radiation processing, and so on. With the development of artificial intelligence and the proposal of "Industry 4.0", more and more artificial intelligence technologies are introduced into the nuclear industry chain to improve production efficiency, reduce operation cost, improve operation safety, and realize risk avoidance. Meanwhile, deep learning, as an important technology of artificial intelligence, has made amazing progress in theoretical and applied research in the nuclear industry, which vigorously promotes the development of informatization, digitization, and intelligence of the nuclear industry. In this paper, we first simply comb and analyze the intelligent demand scenarios in the whole industrial chain of the nuclear industry. Then, we discuss the data types involved in the nuclear industry chain. After that, we investigate the research status of deep learning in the application fields corresponding to different data types in the nuclear industry. Finally, we discuss the limitation and unique challenges of deep learning in the nuclear industry and the future direction of the intelligent nuclear industry.

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Section: Introductionmentioning

confidence: 99%

Deep learning in nuclear industry: A survey

Tang

Gao

et al. 2022

Big Data Min. Anal.

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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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Diagnosis

Kim

2023

Lecture Notes in Energy

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Nuclear Power Plant accident identification system with “don’t know” response capability: Novel deep learning-based approaches

Cited by 28 publications

References 17 publications

Deep learning in nuclear industry: A survey

Deep learning in nuclear industry: A survey

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

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