Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Accident management is an important component to maintaining risk at acceptable levels for all complex systems, such as nuclear power plants. With the introduction of passive, or inherently safe, reactor designs the focus has shifted from management by operators to allowing the system's design to take advantage of natural phenomena to manage the accident. Inherently and passively safe designs are laudable, but nonetheless extreme boundary conditions can interfere with the design attributes which facilitate inherent safety, thus resulting in unanticipated and undesirable end states. This report examines an inherently safe and small sodium fast reactor experiencing a variety of beyond design basis events with the intent of exploring the utility of a Dynamic Bayesian Network to infer the state of the reactor to inform the operator's corrective actions. These inferences also serve to identify the instruments most critical to informing an operator's actions as candidates for hardening against radiation and other extreme environmental conditions that may exist in an accident. This reduction in uncertainty serves to inform ongoing discussions of how small sodium reactors would be licensed and may serve to reduce regulatory risk and cost for such reactors.

Sandia National Laboratories (SNL) has conducted an uncertainty analysi s (UA) on the Fukushima Daiichi unit (1F1) accident progression wit h the MELCOR code. Volume I of the 1F1 UA discusses the physical modeling details and time history results of the UA. Volume II of the 1F1 UA discusses the statistical viewpoint. The model used was developed for a previous accident reconstruction investigation jointly sponsored by the US Department of Energy (DOE) and Nuclear Regulatory Commission (NRC). The goal of this work was to perform a focused evaluation of uncertainty in core damage progression behavior and its effect on key figures - of - merit (e.g., hydrogen production, fraction of intact fuel, vessel lower head failure) and in doing so assess the applicability of traditional sensitivity analysis techniques .

Abstract not provided.

Severe accidents pose unique challenges for nuclear power plant operating crews, including limitations in plant status information and lack of detailed diagnosis and response planning support. Simulation-based PRA provides an opportunity to garner detailed insight into severe accidents; this insight has implications for both HRA and accident management. In this work, we present a framework leveraging simulation-based PRA methods to provide real-time diagnostic support for nuclear power plant operators during severe accidents. This paper presents a prototype model for diagnosing reactor system states associated with loss of flow and transient overpower accidents after an earthquake in a generic Sodium Fast Reactor. We discuss a vision for using this framework to enhance human performance and modelling.

Accident management is an important component to maintaining risk at acceptable levels for all complex systems, such as nuclear power plants. With the introduction of self - correcting, or inherently safe, reactor designs the focus has shifted from management by operators to allowing the syste m's design to manage the accident. While inherently and passively safe designs are laudable, extreme boundary conditions can interfere with the design attributes which facilitate inherent safety , thus resulting in unanticipated and undesirable end states. This report examines an inherently safe and small sodium fast reactor experiencing a beyond design basis seismic event with the intend of exploring two issues : (1) can human intervention either improve or worsen the potential end states and (2) can a Bayes ian Network be constructed to infer the state of the reactor to inform (1). ACKNOWLEDGEMENTS The author s would like to acknowledge the U.S. Department of E nergy's Office of Nuclear Energy for funding this research through Work Package SR - 14SN100303 under the Advanced Reactor Concepts program. The authors also acknowledge the PRA teams at A rgonne N ational L aborator y , O ak R idge N ational L aborator y , and I daho N ational L aborator y for their continue d contributions to the advanced reactor PRA mission area.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Probabilistic Risk Assessment (PRA) is the primary tool used to risk-inform nuclear power regulatory and licensing activities. Risk-informed regulations are intended to reduce inherent conservatism in regulatory metrics (e.g., allowable operating conditions and technical specifications) which are built into the regulatory framework by quantifying both the total risk profile as well as the change in the risk profile caused by an event or action (e.g., in-service inspection procedures or power uprates). Dynamical Systems (DS) analysis has been used to understand unintended time-dependent feedbacks in both industrial and organizational settings. In dynamical systems analysis, feedback loops can be characterized and studied as a function of time to describe the changes to the reliability of plant Structures, Systems and Components (SSCs). While DS has been used in many subject areas, some even within the PRA community, it has not been applied toward creating long-time horizon, dynamic PRAs (with time scales ranging between days and decades depending upon the analysis). Understanding slowly developing dynamic effects, such as wear-out, on SSC reliabilities may be instrumental in ensuring a safely and reliably operating nuclear fleet. Improving the estimation of a plant's continuously changing risk profile will allow for more meaningful risk insights, greater stakeholder confidence in risk insights, and increased operational flexibility.

Abstract not provided.

This report overviews crosscutting regulatory topics for nuclear fuel cycle facilities for use in the Fuel Cycle Research & Development Nuclear Fuel Cycle Evaluation and Screening study. In particular, the regulatory infrastructure and analysis capability is assessed for the following topical areas: Fire Regulations (i.e., how applicable are current Nuclear Regulatory Commission (NRC) and/or International Atomic Energy Agency (IAEA) fire regulations to advance fuel cycle facilities) Consequence Assessment (i.e., how applicable are current radionuclide transportation tools to support risk-informed regulations and Level 2 and/or 3 PRA) While not addressed in detail, the following regulatory topic is also discussed: Integrated Security, Safeguard and Safety Requirement (i.e., how applicable are current Nuclear Regulatory Commission (NRC) regulations to future fuel cycle facilities which will likely be required to balance the sometimes conflicting Material Accountability, Security, and Safety requirements.)

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.