Research on Fast Prediction Method of Fuel Rod Steady-state Temperature Distribution Based on PINN

Liu Zhenhai; Zhang Tao; Qi Feipeng; Zhang Kun; Li Yuanming; Zhou Yi; Li Wenjie

doi:10.13832/j.jnpe.2024.S1.0039

Volume 45 Issue S1

Jun. 2024

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Article Navigation > Nuclear Power Engineering > 2024 > 45(S1): 39-44

Liu Zhenhai, Zhang Tao, Qi Feipeng, Zhang Kun, Li Yuanming, Zhou Yi, Li Wenjie. Research on Fast Prediction Method of Fuel Rod Steady-state Temperature Distribution Based on PINN[J]. Nuclear Power Engineering, 2024, 45(S1): 39-44. doi: 10.13832/j.jnpe.2024.S1.0039

Citation:

Liu Zhenhai, Zhang Tao, Qi Feipeng, Zhang Kun, Li Yuanming, Zhou Yi, Li Wenjie. Research on Fast Prediction Method of Fuel Rod Steady-state Temperature Distribution Based on PINN[J]. Nuclear Power Engineering, 2024, 45(S1): 39-44. doi: 10.13832/j.jnpe.2024.S1.0039

Citation:

Liu Zhenhai, Zhang Tao, Qi Feipeng, Zhang Kun, Li Yuanming, Zhou Yi, Li Wenjie. Research on Fast Prediction Method of Fuel Rod Steady-state Temperature Distribution Based on PINN[J]. Nuclear Power Engineering, 2024, 45(S1): 39-44. doi: 10.13832/j.jnpe.2024.S1.0039

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Research on Fast Prediction Method of Fuel Rod Steady-state Temperature Distribution Based on PINN

doi: 10.13832/j.jnpe.2024.S1.0039

Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, 610213, China

Received Date: 2023-12-28
Rev Recd Date: 2024-03-30
Publish Date: 2024-06-15

Abstract

Abstract

A fast prediction method of fuel rod steady-state temperature distribution base on Physical Informed Neural Network (PINN) is established in this research. The burnup, linear power, boundary temperature and space position are taken as characteristic parameters to solve the parametric solid heat conduction equations using PINN. Based on this method, rapid prediction models for the steady-state temperature distribution of fuel pellet and cladding were constructed. The calculation results show that the calculation speed of fast prediction models are about 1000 times faster than that of commercial finite element method software, and they also have high accuracy. The maximum relative deviation of the steady-state temperature prediction of fuel pellets and cladding is about 0.318% and 0.013% respectively compared with the validation set. The established PINN model can quickly and accurately predict the steady-state temperature distribution of fuel rods.
- Physical Informed Neural Network (PINN),
- Fuel rod steady-state temperature,
- Fast prediction,
- Fuel behavior

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References(6)

References

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