Abstract

The estimation of radioactivity release following an accident in a nuclear power plant is crucial due to its short and long-term impacts on the surrounding population and the environment. In the case of any accidental release, the activity needs to be estimated quickly and reliably to effectively plan a rapid emergency response and design an appropriate evacuation strategy. The accurate prediction of incurred dose rate during normal or accident scenario is another important aspect. In this article, three different non-linear estimation techniques, extended Kalman filter, unscented Kalman filter, and cubature Kalman filter are proposed in order to estimate release activity and to improve the prediction of dose rates. Radionuclide release rate, average wind speed, and height of release are estimated using the dose rate monitors data collected in proximity of the release point. Further, the estimates are employed to improve the prediction of dose rates. The atmospheric dispersion phenomenon of radioactivity release is modelled using the Gaussian plume model. The Gaussian plume model is then employed for the calculation of dose rates. A variety of atmospheric and accident related scenarios for single source and multiple sources are studied in order to assess the efficacy of the proposed filters. Statistical measures have been used in order to validate the performance of the proposed approaches.

Highlights

  • Nuclear power plants (NPP) and installations are potential sources of release of radionuclides into the atmosphere

  • Accident management of an NPP is decided by the atmospheric dispersion model, which predicts the spatio-temporal diffusion of a radionuclide containment in the atmosphere

  • Nonlinear Kalman filter-based approaches have been proposed for radionuclide release activity estimation

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Summary

Introduction

Nuclear power plants (NPP) and installations are potential sources of release of radionuclides into the atmosphere. The rendered results are trustworthy for near-field dispersion cases Based on this rationale, the GPD model has been considered in this work to model radionuclide release in atmosphere and for the calculation of dose rates. Ensemble Kalman filters (EnKF) [15,16] have been proposed by Zheng et al to develop data assimilation techniques combining model predictions and measurements for the design of emergency response system using the Monte Carlo atmospheric dispersion model. Formulation of three nonlinear estimation approaches, EKF, UKF, and CKF for radionuclide release estimation; estimation of source parameters like release rate, wind speed, and release height; prediction improvement of dose rate measurements at different detectors; simulation analysis of different beyond design basis scenarios; and, statistical performance analysis and comparison of the proposed estimation algorithms.

Modelling of Radioactivity Release
Atmospheric Dispersion Model
Dose Rate Model
Nonlinear Estimation Techniques
Extended Kalman Filter
Unscented Kalman Filter
Cubature Kalman Filter
Application to Radionuclide Release Estimation
Variation in Radionuclides Release Rate
Variation in Mean Wind Speed
Variation in Effective Height of Release
Variation in Atmospheric Condition
Multiple Release Points
Statistical Performance Assessment
Tuning of Covariance Matrices
Discussion
Conclusions

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