Research on Data Assimilation Technology for Nuclear Power Source Operating Conditions

Qi Lin; Wang Shuguang; Wang Xuesong; Jin Zhao

doi:10.13832/j.jnpe.2024.060004

Volume 46 Issue 3

Jun. 2025

Turn off MathJax

Article Contents

Article Navigation > Nuclear Power Engineering > 2025 > 46(3): 236-243

Qi Lin, Wang Shuguang, Wang Xuesong, Jin Zhao. Research on Data Assimilation Technology for Nuclear Power Source Operating Conditions[J]. Nuclear Power Engineering, 2025, 46(3): 236-243. doi: 10.13832/j.jnpe.2024.060004

Citation:

Qi Lin, Wang Shuguang, Wang Xuesong, Jin Zhao. Research on Data Assimilation Technology for Nuclear Power Source Operating Conditions[J]. Nuclear Power Engineering, 2025, 46(3): 236-243. doi: 10.13832/j.jnpe.2024.060004

Citation:

PDF( 4471 KB)

Research on Data Assimilation Technology for Nuclear Power Source Operating Conditions

doi: 10.13832/j.jnpe.2024.060004

1.
China Institute of Atomic Energy, Beijing, 102413, China
2.
School of Nuclear Science and Technology, Xi’an Jiaotong University. Xi’an, 710049, China

Received Date: 2024-06-03
Rev Recd Date: 2024-07-15

Available Online: 2025-06-09

Publish Date: 2025-06-09

Abstract

Abstract

To enhance the alignment of simulation model outputs with real data for space nuclear power systems, and to achieve ground-space synchronization and digital twin implementation during the in-orbit operational phase, thereby laying the groundwork for remote diagnostics and prognostics, this study employs the Ensemble Kalman Filter assimilation technique. A data assimilation module was developed in conjunction with the Thermal-hydraulic Analysis Code of Space Thermionic Nuclear System (TASTIN). This module was tested under various transient conditions, including reactor startup, reactivity insertion, and emergency shutdown. The results demonstrate that the assimilation efficiency of operational parameters exceeds 90% across these three transient scenarios. Consequently, the data assimilation method proposed in this paper can effectively correct the simulation model.
- Data assimilation,
- Nuclear power source,
- Ensemble Kalman filter,
- Operating condition simulation

FullText(HTML)

References(18)

References

[1]	ZHANG X L, SU G F, YUAN H Y, et al. Modified ensemble kalman filter for nuclear accident atmospheric dispersion: Prediction improved and source estimated[J]. Journal of Hazardous Materials, 2014, 280: 143-155. doi: 10.1016/j.jhazmat.2014.07.064
[2]	ZHENG D, LEUNG J, LEE B, et al. Data assimilation in the atmospheric dispersion model for nuclear accident assessments[J]. Atmospheric Environment, 2007, 41(11): 2438-2446. doi: 10.1016/j.atmosenv.2006.05.076
[3]	HEO J, TURINSKY P J, DOSTER J M. Optimization of thermal-hydraulic reactor system for SMRs via data assimilation and uncertainty quantification[J]. Nuclear Science and Engineering, 2013, 173(3): 293-311. doi: 10.13182/NSE11-113
[4]	胡永红. 小型飞行器高度定位数据融合方法[J]. 传感器技术,2003, 22(6): 24-26.
[5]	TUEGEL E J, INGRAFFEA A R, EASON T G, et al. Reengineering aircraft structural life prediction using a digital twin[J]. International Journal of Aerospace Engineering, 2011, 2011(1): 154798.
[6]	LI C Z, MAHADEVAN S, LING Y, et al. Dynamic Bayesian network for aircraft wing health monitoring digital twin[J]. AIAA Journal, 2017, 55(3): 930-941. doi: 10.2514/1.J055201
[7]	BUSQUIM E SILVA R, MARQUES A L F, CRUZ J J, et al. Reactivity estimation during a reactivity-initiated accident using the extended Kalman filter[J]. Annals of Nuclear Energy, 2015, 85: 753-762. doi: 10.1016/j.anucene.2015.06.031
[8]	黄喜军. 数据校正在火电机组性能诊断中的研究[D]. 南京: 东南大学,2018.
[9]	HAMILL T M, WHITAKER J S, SNYDER C. Distance-dependent filtering of background error covariance estimates in an ensemble kalman filter[J]. Monthly Weather Review, 2001, 129(11): 2776-2790. doi: 10.1175/1520-0493(2001)129<2776:DDFOBE>2.0.CO;2
[10]	ANDERSON J L, ANDERSON S L. A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts[J]. Monthly Weather Review, 1999, 127(12): 2741-2758. doi: 10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2
[11]	HOUTEKAMER P L, DEROME J. Methods for ensemble prediction[J]. Monthly Weather Review, 1995, 123(7): 2181-2196. doi: 10.1175/1520-0493(1995)123<2181:MFEP>2.0.CO;2
[12]	HOFFMAN R N, KALNAY E. Lagged average forecasting, an alternative to Monte Carlo forecasting[J]. Tellus A: Dynamic Meteorology and Oceanography, 1983, 35(2): 100-118. doi: 10.3402/tellusa.v35i2.11425
[13]	ZHOU B B, DU J, DIMEGO G. Introduction to the NCEP very short range ensemble forecast system (VSREF)[C]//14th Conference on Aviation, Range, and Aerospace Meteorology. Atlanta: AMS, 2010.
[14]	傅娜,陈葆德,谭燕,等. 基于快速更新同化的滞后短时集合预报试验及检验[J]. 气象,2013, 39(10): 1247-1256.
[15]	JIE W H, WU T W, WANG J, et al. Improvement of 6-15 day precipitation forecasts using a time-lagged ensemble method[J]. Advances in Atmospheric Sciences, 2014, 31(2): 293-304. doi: 10.1007/s00376-013-3037-8
[16]	ABARBANEL H D I. Predicting the future: completing models of observed complex systems[M]. New York: Springer, 2013: 125-127.
[17]	陈鹤,杨大文,刘钰,等. 集合卡尔曼滤波数据同化方法改进土壤水分模拟效果[J]. 农业工程学报,2016, 32(02): 99-104.
[18]	朱继洲. 核反应堆安全分析[M]. 第三版. 西安: 西安交通大学出版社,2018: 109-111.