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Related Experiment Video

Updated: Jan 16, 2026

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band

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Published on: May 2, 2018

Physics-informed optimization for emergency radiation assessment with temporal correction under meteorological

Junni He¹, Minghua Lyu², Zhixin Qiu²

¹School of Safety Science, Tsinghua University, Beijing, 10084, China; Institute of Public Safety Research, Tsinghua University, Beijing, 10084, China.

Journal of Environmental Radioactivity

|September 27, 2025

Summary

This study enhances nuclear accident radiation dose assessment by using a physics-informed optimization framework to correct wind field data, improving plume prediction accuracy.

Keywords:

Genetic algorithm optimization Lagrangian puff model Nuclear safety Radionuclide dispersion simulation

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Area of Science:

Nuclear Engineering
Environmental Science
Computational Science

Background:

Accurate radiation dose assessment is critical for nuclear accident emergency response.
Meteorological uncertainties, particularly wind data, significantly impact plume behavior simulations.

Purpose of the Study:

To develop a physics-informed optimization framework to dynamically correct wind field data.
To mitigate discrepancies in radiation dose assessment caused by meteorological uncertainty.

Main Methods:

Integrated a physical radiation assessment model (Lagrangian puff model + point kernel integration) with a genetic algorithm.
Employed dimensionality reduction to simplify 3D gamma dose integration to 1D.
Validated the framework using the Fukushima Daiichi Unit 1 first venting scenario.

Main Results:

Temporal optimization significantly improved the alignment of estimated and observed plume passage times.
Reduced fractional bias (FB) and normalized mean square error (NMSE) at the Main Gate by 57.82% and 90.69%, respectively.
Achieved substantial improvements at MP8 station (97.88% FB, 92.19% NMSE) and increased FAC2 from 9.5% to 52.4%.

Conclusions:

The proposed framework effectively enhances predictive accuracy for emergency radiation dose assessment.
Optimized operational decision-making under complex atmospheric conditions is achievable.
Physics-informed optimization is a viable approach for real-time environmental monitoring and emergency response.