Abstract
In the present work, a online data assimilation approach, based on the Kalman filter algorithm, is proposed for the source term reconstruction in accidental events with dispersion of radioactive agents in air. For this purpose a Gaussian plume model of dispersion in air is embedded in the Kalman filter algorithm to estimate unknown scenario parameters, such as the coordinates and the intensity of the source, on the basis of measurements collected by a mobile sensor. The approach was tested against pseudo-experimental data produced with both the Gaussian plume model and the Lagrangian puff model SCIPUFF. The results show the good capabilities of the proposed approach in retrieving the values of the unknown parameters when (i) one or more release parameters are poorly known and (ii) a sufficient number of experimental measurements describing the evolution of the dispersion process can be collected in a short time by means of mobile sensors. Thanks to its flexibility and computational efficiency, and due to the exploitation of the Kalman filter potentialities through the use of a simplified model of dispersion in air, the proposed approach can constitute a useful tool for the management of emergency scenarios.
Highlights
When dealing with the analysis of accidental atmospheric releases of radionuclides, or more in general, hazardous contaminants, predictive computational models constitute essential tools to provide decision-makers with quantitative information for both emergency operations and post-accident management [1]
The proposed approach has been first tested using pseudo-experimental data generated by the Gaussian plume model (Section 3.1), with data generated by means of the SCIPUFF model (Section 3.2)
An online source term estimation algorithm based on a Kalman filtering algorithm, within which is embedded a Gaussian plume model, is proposed
Summary
When dealing with the analysis of accidental atmospheric releases of radionuclides, or more in general, hazardous contaminants, predictive computational models constitute essential tools to provide decision-makers with quantitative information for both emergency operations and post-accident management [1]. As numerical weather prediction (NWP) tools do not have adequate resolution for microscale scenarios, some effort for the determination of the wind field is often required as well For these reasons, approaches based on computational fluid dynamics (CFD) were adopted in recent years to deal with miscroscale scenarios [2,3,4]. In the Lagrangian approach, a large number of pseudo-particles of the released agent are tracked along their trajectories [7,8] Such trajectories are determined as the result of two contributions, i.e., deterministic advection by the wind field and stochastic diffusion due to turbulence. In both Eulerian and Lagrangian approaches, the wind field is usually obtained from standard NWP models. Gaussian dispersion models [9] are widely employed and are among the most commonly used tools in regulatory air dispersion modelling for fast screening purposes at different scales
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