Research on Fuel Rod Damage Diagnosis Method Based on Big Data

Jing Futing; Lyu Huanwen; Tang Songqian; Wei Jianglin; Li Lan; Xia Mingming

doi:10.13832/j.jnpe.2024.S2.0150

Volume 45 Issue S2

Jan. 2025

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Jing Futing, Lyu Huanwen, Tang Songqian, Wei Jianglin, Li Lan, Xia Mingming. Research on Fuel Rod Damage Diagnosis Method Based on Big Data[J]. Nuclear Power Engineering, 2024, 45(S2): 150-155. doi: 10.13832/j.jnpe.2024.S2.0150

Citation:

Jing Futing, Lyu Huanwen, Tang Songqian, Wei Jianglin, Li Lan, Xia Mingming. Research on Fuel Rod Damage Diagnosis Method Based on Big Data[J]. Nuclear Power Engineering, 2024, 45(S2): 150-155. doi: 10.13832/j.jnpe.2024.S2.0150

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Research on Fuel Rod Damage Diagnosis Method Based on Big Data

doi: 10.13832/j.jnpe.2024.S2.0150

National Key Laboratory of Nuclear Reactor Technology, Nuclear Power Institute of China, Chengdu, 610213, China

Received Date: 2024-06-30
Rev Recd Date: 2024-09-03
Publish Date: 2025-01-06

Abstract

Abstract

Fuel rod damage diagnosis (FRDD) in nuclear power plants is a critical issue of concern for nuclear power plant operators and nuclear safety regulators. The application of big data and the nearest neighbor algorithm to fuel rod damage diagnosis has led to the development of a PWR (Pressurized Water Reactor) fuel rod damage diagnosis software. The software has been validated using operational cases of fuel rod damage in nuclear power plants and theoretical examples. The validation results are as follows: ① In terms of category analysis of fuel rod breach size, 80% of the analysis results are consistent with the theoretical examples; ② In terms of the analysis of the damaged fuel rod number, the maximum deviation from the theoretical examples is one rod. The FRDD methodology for PWRs, based on big data and the nearest neighbor algorithm, provides diagnostic results that are closer to the actual damage scenario. This allows for the timely detection of fuel rod damage and changes in the damage state, providing a reliable basis for operational decision-making and radiation protection after fuel rod damage. This approach can enhance the economic efficiency of nuclear power plant operations while ensuring safety.
- Fuel rod damage,
- Diagnosis method,
- Big data

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References(8)

References

[1]	IAEA. Review of fuel failures in water cooled reactors (2006-2015): An Update of IAEA Nuclear Energy Series no. NF-T-2.1[R]. Vienna: IAEA, 2019: 10-11.
[2]	BEYER C E. Methodology estimating number of failed fuel rods and defect size: EPRI-NP-6554[R]. Palo Alto: EPRI, 1989.
[3]	LEWIS B J, CHAN P K, EL-JABY A, et al. Fission product release modelling for application of fuel-failure monitoring and detection-an overview[J]. Journal of Nuclear Materials, 2017, 489: 64-83. doi: 10.1016/j.jnucmat.2017.03.037
[4]	Claude Leuthrot. Correlation between fission product activity in PWR water and characteristics of defects in fuel cladding. Fuel failure in normal operation of water reactors: experience, mechanisms and management: IAEA-TECDOC-709[R]. Vienna: IAEA, 1992: 67-71.
[5]	PARRAT D, GENIN J B, MUSANTE Y, et al. Failed rod diagnosis and primary circuit contamination level determination thanks to the DIADEME code: IAEA-TECDOC-1345[R]. Vienna: IAEA, 2003: 265-276.
[6]	CHIRON for WINDOWS—User’s Manual: a code for analyzing coolant and offgas activity in a light water reactor[R]. U. S. : EPRI, 1998.
[7]	CADE—User’s manual: coolant activity data evaluation personal computer program[R]. U. S. : Westinghouse Electric Company LLC, 1993.
[8]	景福庭. 裂变产物源项分析软件FPA的研发[J]. 中国核电,2015,8(S2): 11-15.