Research on Prediction of Transient Parameters in Tod Bundle Subchannel Based on POD-ML Method

Xu Yujie; Mo Jinhong; Dong Xiaomeng; Liu Yong; Xu Anqi; Yu Yang

doi:10.13832/j.jnpe.2024.080031

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Xu Yujie, Mo Jinhong, Dong Xiaomeng, Liu Yong, Xu Anqi, Yu Yang. Research on Prediction of Transient Parameters in Tod Bundle Subchannel Based on POD-ML Method[J]. Nuclear Power Engineering. doi: 10.13832/j.jnpe.2024.080031

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Xu Yujie, Mo Jinhong, Dong Xiaomeng, Liu Yong, Xu Anqi, Yu Yang. Research on Prediction of Transient Parameters in Tod Bundle Subchannel Based on POD-ML Method[J]. Nuclear Power Engineering. doi: 10.13832/j.jnpe.2024.080031

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Research on Prediction of Transient Parameters in Tod Bundle Subchannel Based on POD-ML Method

doi: 10.13832/j.jnpe.2024.080031

Xu Yujie^1
,,
Mo Jinhong¹,
Dong Xiaomeng^{1
,
,},
Liu Yong¹,
Xu Anqi¹,
Yu Yang^{2, 3}

1.
School of Physics and Optoelectronic Engineering, Shenzhen Key Laboratory of Nuclear and Radiation Safety, China Institute of Nuclear Energy and Safety, Shenzhen University, Shenzhen, Guangdong, 518060, China
2.
National Key Laboratory of Parallel and Distributed Computing, National University of Defense Technology, Changsha, 410073, China
3.
Nuclear Power Institute of China, Chengdu, 610213, China

Received Date: 2024-08-12
Rev Recd Date: 2024-12-26

Available Online: 2025-01-15

Abstract

Abstract

Model reduction (ROM) effectively reduces the complexity of physical models by mapping full-order conservation equations to lower-order subspaces or building data-driven proxy models. Compared with traditional computational fluid dynamics (CFD) simulation, the reduced order model is more efficient in large-scale simulation. In this paper, a reduced order model framework is proposed by combining POD with machine learning (ML) to predict mass flow parameters in beam subchannels. The comparison of two different forecasting methods shows that both methods have advantages and disadvantages in long-term and short-term forecasting, which can provide a scheme for other complex system forecasting in the future.
- model reduction technique,
- Bundle subchannel,
- Machine learning

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References(11)

References

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