Probabilistic Model Chain Methodology for evaluating radiological consequences of accidents in High-Temperature Gas-Cooled Reactor

Abstract

Dose consequence analysis models are pivotal in Nuclear Safety Analysis, particularly for assessing radiological consequences following nuclear accidents. As many new reactor designs, including High-Temperature Gas-Cooled Reactors (HTGRs), are expected to be located closer to populated areas and propose significantly smaller Emergency Planning Zones (EPZs), accurate consequence analysis becomes increasingly vital. This paper presents a novel Probabilistic Model Chain Methodology (PMCM), beginning with core state estimation using SERPENT2, followed by source term characterization through the Idaho National Laboratory Model, and culminating in the generation of weather scenarios based on empirical models. These data are produced probabilistically, leading to simulations of radionuclide transport and dose assessment using the JRODOS system. The methodology is illustrated using the HTGR TeResa $40M{W}_{th}$ design and a Depressurized Loss of Forced Cooling accident scenario for the reactor’s location at the National Centre for Nuclear Research in Poland. The resulting dose spatial results are compared with dose limits, as well as dose values across distances from the site, highlighting the highest dose maps. This is achieved through integrated uncertainty across several models, enhancing potential modularity and applicability of the methodology to other reactor types and accident scenarios.