Implementation of Novel Evolutional Algorithm for 3-Dimensional Radiation Mapping and Gamma-Field Reconstruction within the Chornobyl Sarcophagus

Abstract

This work presents the application of a novel evolutional algorithmic approach to determine and reconstruct the specific 3-dimensional source location of gamma-ray emissions within the shelter object, the sarcophagus of reactor Unit 4 of the Chornobyl Nuclear Power Plant. Despite over 30 years having passed since the catastrophic accident, the high radiation levels combined with strict safety and operational restrictions continue to preclude many modern radiation detection and mapping systems from being extensively or successfully deployed within the shelter object. Hence, methods for reconstructing the intense and evolving gamma fields based on the limited inventory of available data are crucially needed. Such data is particularly important in planning the demolition of the unstable structures that comprise the facility, as well as during the prior operations to remove fuel containing materials from inside the sarcophagus and reactor Unit 4. For this approach, a simplified model of gamma emissions within the shelter object is represented by a series of point sources, each regularly spaced on the shelter object’s exterior surface, whereby the calculated activity values of these discrete sources are considered as a population in terms of evolutionary algorithms. To assess the numerical reconstruction, a fitness function is defined, comprising the variation between the known activity values (obtained during the commissioning of the New Safe Confinement at the end of 2019 on the level of the main crane system, located just below the arch above the shelter object) and the calculated values at these known locations for each new population. The final algorithm’s performance was subsequently verified using newly obtained information on the gamma dose-rate on the roof of the shelter object during radiation survey works at the end of 2021. With only 7000 iterations, the algorithm attained an MAPE percentage error of less than 23%, which the authors consider as satisfactory, considering that the relative error of the measurements is ±17%. While a simple initial application is presented in this work, it is demonstrated that evolutional algorithms could be used for radiation mapping with an existing network of radiation sensors, or, as in this instance, based on historic gamma-field data.