Nonlinear Reduced-order Analysis of Three-dimensional Thermal Stratification in the Upper Plenum of Lead-bismuth Cooled Fast Reactor Based on Graph Neural Network

Zeng Fulin; Zhao Pengcheng; Li Lingli; Liu Zijing; Li Wei

doi:10.13832/j.jnpe.2024.050044

Volume 46 Issue 3

Jun. 2025

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Article Navigation > Nuclear Power Engineering > 2025 > 46(3): 86-94

Zeng Fulin, Zhao Pengcheng, Li Lingli, Liu Zijing, Li Wei. Nonlinear Reduced-order Analysis of Three-dimensional Thermal Stratification in the Upper Plenum of Lead-bismuth Cooled Fast Reactor Based on Graph Neural Network[J]. Nuclear Power Engineering, 2025, 46(3): 86-94. doi: 10.13832/j.jnpe.2024.050044

Citation:

Zeng Fulin, Zhao Pengcheng, Li Lingli, Liu Zijing, Li Wei. Nonlinear Reduced-order Analysis of Three-dimensional Thermal Stratification in the Upper Plenum of Lead-bismuth Cooled Fast Reactor Based on Graph Neural Network[J]. Nuclear Power Engineering, 2025, 46(3): 86-94. doi: 10.13832/j.jnpe.2024.050044

Citation:

Zeng Fulin, Zhao Pengcheng, Li Lingli, Liu Zijing, Li Wei. Nonlinear Reduced-order Analysis of Three-dimensional Thermal Stratification in the Upper Plenum of Lead-bismuth Cooled Fast Reactor Based on Graph Neural Network[J]. Nuclear Power Engineering, 2025, 46(3): 86-94. doi: 10.13832/j.jnpe.2024.050044

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Nonlinear Reduced-order Analysis of Three-dimensional Thermal Stratification in the Upper Plenum of Lead-bismuth Cooled Fast Reactor Based on Graph Neural Network

doi: 10.13832/j.jnpe.2024.050044

1.
School of Nuclear Science and Technology, University of South China, Hengyang, Hunan, 421001, China
2.
Moscow Engineering Physics Institute, National Research Nuclear University, Moscow, 115409, Russia
3.
School of Resources Environment and Safety Engineering, University of South China, Hengyang, Hunan, 421001, China

Received Date: 2024-05-27
Rev Recd Date: 2024-07-12

Available Online: 2025-06-09

Publish Date: 2025-06-09

Abstract

Abstract

The thermal stratification phenomenon in the upper plenum of the lead-bismuth fast reactor has significant implications for the safety of internal components and the ability to remove residual heat. This paper focuses on analyzing this phenomenon. Firstly, high-precision full-order snapshots of the thermal stratification in the upper plenum of the lead-bismuth fast reactor are obtained using the computational fluid dynamics software FLUENT. Then, the graph neural network (GNN) is employed to construct a graph autoencoder (GAE) for nonlinear order reduction of the snapshots, and the reduced reconstruction results are compared with the linear reduction results obtained using Proper orthogonal decomposition (POD). Finally, a multilayer perceptron is used for online state recognition and predictive analysis of the thermal stratification snapshots. The research results demonstrate that the graph neural network, due to its high level of nonlinearity and its inherent advantage in nonlinear order reduction of large-scale CFD data, achieves comparable first-order modal reconstruction accuracy to that of POD with 30~50 basis functions. During the online process, the feature recognition and prediction of thermal stratification snapshots can be completed within a duration of 472 ms, with accuracy similar to the reconstruction accuracy. The related research results can provide a new analytical method support for the evolution mechanism analysis and consequence prediction of thermal stratification phenomenon in lead-bismuth fast reactors.
- Lead-bismuth cooled fast reactor,
- Thermal stratification,
- Nonlinear reduced-order model,
- Graph neural network (GNN),
- Proper orthogonal decomposition (POD)

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References(21)

References

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