Nuclear severe accident management system based on big data and deep learning

Abstract

In the event of a severe accident occurring at a nuclear power plant, it is necessary to promptly diagnose the state of the plant in order to implement appropriate strategies and measures for mitigating potentially severe consequences. This diagnosis is based on factors such as the timing of the initial event, the type and location of the accident, and the response information from the nuclear safety systems. Advancements in information technology (IT) have led to the application of big data and deep learning techniques in nuclear severity accident management. However, there is a scarcity of real-world data on nuclear severity accidents, with only limited information available from three notable incidents: the Three Mile Island nuclear accident in 1979, the Chernobyl nuclear accident in 1986, and the Fukushima nuclear accident in 2011. To address this issue, this paper proposes the use of an integrated nuclear severity accident simulation computer code to generate a substantial amount of deep learning training data. The resulting data and labels are saved into tens of thousands of small files. However, mainstream operating systems such as Linux, Windows, and MacOS, as well as file systems like Hadoop Distributed File System (HDFS), GlusterFS, Ceph, and MinIO, lack sufficient support for managing a large number of small files (LOSF). To overcome this limitation, a method that combines the SeaweedFS cloud storage database with deep learning techniques is proposed for the severity accident management system in Nuclear Power Plants (NPPs). The simulation-generated small files are efficiently transferred to the SeaweedFS cloud storage database using the Application Programming Interface (API) provided by Amazon Simple Storage Service (Amazon S3), ensuring parallel efficiency and optimal storage space utilization. Furthermore, the SeaweedFS database offers enhanced portability through the use of Docker images and containers. TensorFlow is utilized for deep learning training using the data stored in SeaweedFS. Following the training process, a private, scalable, and portable nuclear severity management system is established, integrating Artificial Intelligence (AI) and big data analytics to enable the quick diagnosis of nuclear severity accidents and the assessment of the damage sustained in NPPs.