A source term estimation method for a nuclear accident using atmospheric dispersion models

1Modeling the downwind hazard area resulting from the unknown release of an 2 atmospheric contaminant requires estimation of the source characteristics of a localized 3 source from concentration or dosage observations and use of this information to model 4 the subsequent transport and dispersion of the contaminant. This source term estimation 5 problem is mathematically challenging because airborne material concentration 6 observations and wind data are typically sparse and the turbulent wind field chaotic. 7Methods for addressing this problem fall into three general categories: forward modeling, 8 inverse modeling, and nonlinear optimization. Because numerous methods have been 9 developed on various foundations, they often have a disparate nomenclature. This 10 situation poses challenges to those facing a new source term estimation problem, 11 particularly when selecting the best method for the problem at hand. There is, however, 12 much commonality between many of these methods, especially within each category. 13Here we seek to address the difficulties encountered when selecting an STE method by 14 providing a synthesis of the various methods that highlights commonalities, potential 15 opportunities for component exchange, and lessons learned that can be applied across 16 methods. 17 18

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Multi-source term estimation based on parallel particle filtering and dynamic state space in unknown radiation environments

Bai

Zhu

et al. 2023

Building and Environment

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A source term estimation method for a nuclear accident using atmospheric dispersion models

Cited by 5 publications

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Analysis of ground deposition of radionuclides under different wind fields from the Fukushima Daiichi accident

Analysis of ground deposition of radionuclides under different wind fields from the Fukushima Daiichi accident

Paradigms and commonalities in atmospheric source term estimation methods

Multi-source term estimation based on parallel particle filtering and dynamic state space in unknown radiation environments

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