Design and implementation of cloud platform for nuclear accident simulation

VVER-1200 (Water-Water Energetic Reactor) represents a significant advancement in nuclear power generation, emphasizing the continuous analysis and enhancement of safety systems for reliable operation. The proposed study focuses on simulating combined scenarios involving steam generator tube rupture (SGTR) and AC power loss using core algorithms and models within personal computer transient analyzer (PCTRAN). Reactor kinetic equations, thermal-hydraulic balance, and safety system models are discussed to elucidate their role in simulating SGTR and AC power loss. Safety criteria, boundaries and initial conditions are outlined to provide a comprehensive understanding of the simulation framework. The analysis delves into dynamic behavior of VVER-1200, placing emphasis on thermal-hydraulic implications, essential reactor parameters, and radiation monitoring to facilitate impact evaluation. Continuous monitoring and maintenance of safety systems are underscored to ensure stable core cooling, particularly during proposed transient conditions. Through meticulous analysis and comparison with established benchmarks, this study contributes to bolstering the safety and reliability of VVER-1200 reactors by identifying vulnerabilities, assessing mitigation strategies, and refining emergency response protocols. Practical implications of this study offer a crucial understanding of reactor behavior, safety system performance, and emergency response strategies, thereby improving safety, optimizing operational practices, and reducing risks in nuclear reactor accidents.

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A Survey of Artificial Intelligence Applications in Nuclear Power Plants

Jendoubi,

Asad

2024

IoT

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Nuclear power plants (NPPs) rely on critical, complex systems that require continuous monitoring to ensure safe operation under both normal and abnormal conditions. Despite the potential of artificial intelligence (AI) to enhance predictive capabilities in these systems, limited research has been conducted on the application of AI algorithms within NPPs. This presents a knowledge gap in the integration of AI for improving safety, reliability, and decision making in NPP. In this study, we explore the use of AI methods, including machine learning and real-time data analytics, applied to NPP components to address the nonlinearity and dynamic behavior inherent in reactor operations. Through the implementation of AI and Internet of Things (IoT) devices, we propose a system that enables early warning and real-time data transmission to regulatory authorities and decision-makers, ensuring better coordination during incidents. Lessons from past nuclear accidents, such as Chernobyl, emphasize the importance of timely information dissemination to mitigate risks. However, this integration also presents challenges, including cybersecurity risks and the need for updated regulations to address AI use in safety-critical environments. The results of this study highlight the urgent need for further research on the application of AI in NPPs, with a particular focus on addressing these challenges to ensure safe implementation.

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Design and implementation of cloud platform for nuclear accident simulation

Cited by 4 publications

References 25 publications

Cloud Platform Data Disaster Recovery Model

Cloud Platform Data Disaster Recovery Model

Holistic Approach to Safety and Operational Stability: Analyzing VVER-1200 Reactor Dynamics in SGTR and AC Power Loss

A Survey of Artificial Intelligence Applications in Nuclear Power Plants

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