Automated release rate inversion and plume bias correction for atmospheric radionuclide leaks: A robust and general remediation to imperfect radionuclide transport modeling

Sheng Fang; Shuhan Zhuang; Xinpeng Li; Hong Li

doi:10.1016/j.scitotenv.2020.142140

Automated release rate inversion and plume bias correction for atmospheric radionuclide leaks: A robust and general remediation to imperfect radionuclide transport modeling

Sci Total Environ. 2021 Feb 1:754:142140. doi: 10.1016/j.scitotenv.2020.142140. Epub 2020 Sep 2.

Authors

Sheng Fang¹, Shuhan Zhuang², Xinpeng Li³, Hong Li²

Affiliations

¹ Institute of Nuclear and New Energy Technology, Collaborative Innovation Centre of Advanced Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Tsinghua University, Beijing 100084, China. Electronic address: fangsheng@tsinghua.edu.cn.
² Institute of Nuclear and New Energy Technology, Collaborative Innovation Centre of Advanced Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Tsinghua University, Beijing 100084, China.
³ Institute of Nuclear and New Energy Technology, Collaborative Innovation Centre of Advanced Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Tsinghua University, Beijing 100084, China; School of Nuclear Science and Engineering, North China Electric Power University, Beijing 102206, China.

PMID: 33254859
DOI: 10.1016/j.scitotenv.2020.142140

Abstract

The release rate is vital in assessing the international environmental risk of atmospheric radionuclide leaks, and it usually can only be obtained through inversion. However, such inversion is vulnerable to the inevitable plume biases in radionuclide transport modeling, leading to inaccurate estimates and risk assessment. This paper describes an automated method that estimates the release rate while comprehensively correcting plume biases, including both the plume range and transport pattern. The spatial correlation of predictions is used to simplify the difficult task of direct plume adjustment to that of tuning the predictions inside a correlation-adjusted plume. An ensemble-based algorithm is proposed to automatically calculate the spatial correlation. The proposed method is validated using two radionuclide transport models with mild and severe plume biases and data from two wind tunnel experiments, and its performance is compared with that of the standard approach and a recent state-of-the-art method. The results demonstrate that our method corrects the plume biases with high accuracy (Pearson's Correlation Coefficient = 1.0000, Normalized Mean Square Error ≤ 1.03 × 10^-3) and reduces the estimation error by nearly two orders of magnitude at best. The proposed approach achieves near-optimal performance with fully automated parameterization, keeping the lowest error levels in our validation cases for various measurement sets.

Keywords: Plume biases; Radionuclide emission; Source inversion; Wind tunnel experiment.