Research on an AI-based Prediction Model for the Temperature Field of a Nuclear Power Plant Cable Fire Driven by Simulation Data

DOI: https://doi.org/10.62517/jike.202504409

Author(s)

Saili Yi, Zhijun Zhang

Affiliation(s)

School of Urban Construction and Safety Engineering, Shanghai Institute of Technology, Shanghai, China

Abstract

Nuclear power plants are a high-risk, high-safety requirement field. Once a fire occurs, it can lead to the failure of critical equipment, affecting the safety systems of nuclear power plants and increasing the risk of nuclear leakage. Fires in nuclear power plants are extremely rare events; therefore, the lack of training data sets has been a major obstacle in the development of artificial neural network prediction models in traditional research. Focusing on fire safety in nuclear reactor buildings, this study employs an integrated approach of numerical simulation and artificial intelligence. Specifically, numerical simulations of cable fires in a Chinese nuclear reactor building model are performed utilizing the Fire Dynamics Simulator (FDS).The study covers various fire scenarios and generates extensive three-dimensional spatial fire fields, providing a data foundation for effectively training deep neural network prediction models. On this basis, a fire temperature field prediction model based on CNN-Bi-LSTM is designed, which can significantly improve the accuracy and computational efficiency of fire temperature field analysis and prediction in nuclear reactor buildings, offering an important new approach for fire safety design in nuclear power plants.

Keywords

Nuclear Power Plant; Cable Fire; Numerical Simulation; FDS; Deep Learning Network; Artificial Intelligence

References

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