In order to investigate the feasibility of data assimilation in a real nuclear accident environment, measurements of Fukushima nuclear accident were considered. The data assimilation system was constructed by using the Lagrangian puff model as the radioactive material diffusion model, and 86 group real dose rate data from the accident as the observations, and the Ensemble Kalman Filter algorithm as the assimilation algorithm. The experimental results show that the assimilated nuclear accident radiation field is in good agreement with the actual measurements, the land contaminated areas are concentrated in the northwest of the nuclear power plant. With the increase of the real measurements, the error of the radiation field decreases with time. Compared with the results with no assimilation, the uncertainty of assimilated dose rate was reduced more than 80%. Through the data assimilation, the whole error of the radiation field is about 30%. The utilization of the real measurements can reduce the uncertainty of the model prediction.
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2017 25th International Conference on Nuclear Engineering
July 2–6, 2017
Shanghai, China
Conference Sponsors:
- Nuclear Engineering Division
ISBN:
978-0-7918-5782-3
PROCEEDINGS PAPER
Data Assimilation for Fukushima Nuclear Accident Assessments
Biao Yuan,
Biao Yuan
Institute of NBC Defence, Beijing, China
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Liangyu Wang,
Liangyu Wang
Institute of NBC Defence, Beijing, China
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Lijun Zhang
Lijun Zhang
Institute of NBC Defence, Beijing, China
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Mei Xu
Institute of NBC Defence, Beijing, China
Biao Yuan
Institute of NBC Defence, Beijing, China
Liangyu Wang
Institute of NBC Defence, Beijing, China
Lijun Zhang
Institute of NBC Defence, Beijing, China
Paper No:
ICONE25-66102, V004T06A005; 8 pages
Published Online:
October 17, 2017
Citation
Xu, M, Yuan, B, Wang, L, & Zhang, L. "Data Assimilation for Fukushima Nuclear Accident Assessments." Proceedings of the 2017 25th International Conference on Nuclear Engineering. Volume 4: Nuclear Safety, Security, Non-Proliferation and Cyber Security; Risk Management. Shanghai, China. July 2–6, 2017. V004T06A005. ASME. https://doi.org/10.1115/ICONE25-66102
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