A Bayesian Network Approach to Accident Management and Estimation of Source Terms for Emergency Planning
An approach to accident management and estimation of radiological releases for application to emergency planning is developed using the Bayesian Belief Network (BN) method, which is coupled to the Accident Diagnostics Analysis and Management (ADAM) system. This approach uses a BN to diagnose the initial and boundary conditions of the accident using the actual observable and sensor data from a plant. The topology and conditional probability values in the main part of the network are derived mainly from a level-2 PSA model that also includes the impact of various severe accident prevention and mitigation features that are included in current Severe Accident Management Guidelines (SAMGs). In this approach, the network links represent causality and logical consistency relationships. The plant instrumentation database and operator are queried in order to enter findings into the input nodes of the network (observables), after which posterior values of belief at the output (i. e., accident initial and boundary condition) nodes are post-processed (i. e., eliminating redundant or very similar scenarios) into a list accident scenarios ranked in accordance with te the inferred likelihood of occurrence. These results include the characteristics of the accident initiator and the availability of various systems, which are in turn used as inputs to ADAM to calculate the various phenomenological outcomes and their resulting radiological source terms for the top-ranked scenarios.
KeywordsSource Term Hide Node Emergency Planning Bayesian Belief Network Core Damage
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