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Related Experiment Video

Updated: Nov 14, 2025

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Multi-nuclide source term estimation method for severe nuclear accidents from sequential gamma dose rate based on a

Yongsheng Ling1, Qi Yue2, Tian Huang2

  • 1Department of Nuclear Science and Technology, Nanjing University of Aeronautics and Astronautics, 211106 Nanjing, China; Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, 215021 Suzhou, China.

Journal of Hazardous Materials
|March 8, 2021
PubMed
Summary

A new Bayesian-optimized recurrent neural network model estimates radioactive nuclide emission rates from nuclear accidents using gamma dose rate data. This method offers a faster, more accurate alternative to traditional simulations, improving emergency response capabilities.

Keywords:
Bayesian optimizationEnvironmental monitoring dataInterRasNuclear emergency managementSource term inversion

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Area of Science:

  • Nuclear Engineering
  • Environmental Science
  • Computational Science

Background:

  • Severe nuclear accidents can release unknown amounts of radioactive material.
  • Accurate source term information is crucial for assessing accident consequences and informing decisions.
  • Existing methods often rely on complex, time-consuming atmospheric dispersion simulations.

Purpose of the Study:

  • To develop a novel model for estimating multi-nuclide emission rates during nuclear accidents.
  • To improve the speed and accuracy of radiological assessment using real-time monitoring data.
  • To provide a method that does not require prior information or extensive simulations.

Main Methods:

  • A recurrent neural network (RNN) model was developed and optimized using Bayesian methods.
  • The model estimates radionuclide emission rates from sequential off-site gamma dose rate monitoring data.
  • Simulated datasets were generated using the International Radiological Assessment System with six key radionuclides and meteorological parameters.

Main Results:

  • The proposed RNN-Bayesian model accurately estimates nuclide emission rates using gamma dose rate data.
  • Model accuracy improves continuously with sequential data input.
  • A mean absolute percentage error below 7% for Te-132 was achieved over a 10-hour period.

Conclusions:

  • The RNN-Bayesian model provides a rapid and effective tool for estimating radioactive source terms in nuclear accidents.
  • This approach bypasses the need for complex atmospheric dispersion simulations, enabling faster decision-making.
  • The method demonstrates significant potential for enhancing nuclear accident consequence assessment and emergency response.