Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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A simple data assimilation method to improve atmospheric dispersion based on Lagrangian puff model

  • Li, Ke (State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Co., Ltd) ;
  • Chen, Weihua (State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Co., Ltd) ;
  • Liang, Manchun (Department of Engineering Physics, Tsinghua University) ;
  • Zhou, Jianqiu (State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Co., Ltd) ;
  • Wang, Yunfu (State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Co., Ltd) ;
  • He, Shuijun (Department of Engineering Physics, Tsinghua University) ;
  • Yang, Jie (Beijing Global Safety Technology Co., Ltd) ;
  • Yang, Dandan (Beijing Global Safety Technology Co., Ltd) ;
  • Shen, Hongmin (Beijing Global Safety Technology Co., Ltd) ;
  • Wang, Xiangwei (Department of Engineering Physics, Tsinghua University)
  • Received : 2020.08.31
  • Accepted : 2021.01.30
  • Published : 2021.07.25
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Abstract

To model the atmospheric dispersion of radionuclides released from nuclear accident is very important for nuclear emergency. But the uncertainty of model parameters, such as source term and meteorological data, may significantly affect the prediction accuracy. Data assimilation (DA) is usually used to improve the model prediction with the measurements. The paper proposed a parameter bias transformation method combined with Lagrangian puff model to perform DA. The method uses the transformation of coordinates to approximate the effect of parameters bias. The uncertainty of four model parameters is considered in the paper: release rate, wind speed, wind direction and plume height. And particle swarm optimization is used for searching the optimal parameters. Twin experiment and Kincaid experiment are used to evaluate the performance of the proposed method. The results show that the proposed method can effectively increase the reliability of model prediction and estimate the parameters. It has the advantage of clear concept and simple calculation. It will be useful for improving the result of atmospheric dispersion model at the early stage of nuclear emergency.

Keywords

Acknowledgement

This work was supported by the State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment under Grant number K-A2019.403.

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