Upcoming Events

The Assessment of Low Probability Containment Failure Modes Using Dynamic Probabilistic Risk Assessment

August 27, 2013 9:00AM to 10:00AM
Presenter 
Acacia Brunett, Ohio State University
Location 
Building 208, Room A138
Type 
Seminar
Series 
Abstract:
Although low probability containment failure modes in nuclear power plants may lead to large releases of radioactive material, these modes are typically crudely modeled in system level codes and have large associated uncertainties. More recently, dynamic probabilistic risk assessment (PRA) techniques have been developed which are capable of mechanistically and consistently exploring the effects of rare phenomena on these low probability failure modes while accounting for the associated uncertainties.

The purpose of this work is to utilize dynamic PRA tools to reassess the low probability containment failure modes considered in NUREG-1150 and the mechanisms driving these failure modes. This analysis focuses on the low probability phenomena occurring during a station blackout (SBO) with late power recovery in the Zion Nuclear Power Plant, a Westinghouse pressurized water reactor (PWR). Subsequent to NUREG-1150, significant experimentation and modeling regarding the mechanisms driving these failure modes have been performed. In light of this improved understanding, the NUREG-1150 containment failure modes are reviewed in this work using the current state of knowledge.

For some unresolved mechanisms, such as containment loading from high pressure melt ejection (HPME) and combustion events, additional analyses are performed using the accident simulation tool MELCOR to explore the bounding containment loads for realistic scenarios. A methodology for assessing the likelihood of combustible gas ignition is presented in which the characteristics of the ignition source, in addition to the properties of the existing gas mixture, are utilized to determine the probability of the combustion event.

This work also explores the feasibility of using dynamic event trees (DETs) to analyze low probability phenomena. The flexibility of this approach is demonstrated through the rediscretization of containment fragility curves used in construction of the DET to show convergence to a true solution. This technique reduces the computational burden introduced through extremely fine fragility curve discretization by subsequent refinement of fragility curve only in regions of interest while still utilizing previously executed scenarios.