Dynamic Importance Measures in the ADAPT Framework
- Reliability and Risk Analysis Department, Sandia National Laboratories, P.O. Box 5800, Albuquerque, NM 87185 (United States)
- Nuclear Engineering Program, The Ohio State University, 201 West 19th Avenue, Columbus, OH 43210 (United States)
Probabilistic Risk Assessment (PRA) uses fault tree/event tree analysis to evaluate the environmental impacts of nuclear power plants due to internal or external initiating events. In a fault tree, basic failure events are assembled using primarily And/Or logic to determine the combinations of failures that lead to failure of the system as a whole. An event tree is forward-facing, and begins with a single initiating event. From there the analysis considers what event may occur next, and branches out among the possible configurations of occurrence and non-occurrence of that event. This branching continues until user-defined end states are reached. End states may include a safe and stable configuration of the plant, or any number of differing failure states. Both fault and event trees require basic event probabilities as inputs to provide insight on the likelihood of different outcomes. PRA has been applied to nuclear power plants as an analysis tool since the 1975 Reactor Safety Study. In a traditional PRA, the order of events is prescribed by the analyst and each event is typically a binary aleatory uncertainty: occurrence or non-occurrence. Discrete dynamic event tree (DDET) analysis eliminates this subjectivity in ordering of events by using the output of a dynamic system model (simulator) to inform the branching. Branching conditions are triggered by the existence of a relevant plant state in the code, and therefore only occur as physically appropriate. The DDET approach also allows consideration of both epistemic and aleatory uncertainties on a phenomenologically and stochastically consistent platform. One current limitation of DDETs is the assessment of results, which is not as well developed as in traditional PRA. A concept that is used in PRA to assess the significance of a basic event is the concept of importance measures (IMs). In traditional PRA, IMs only consider the likelihood of occurrence of non-occurrence of a basic event. The application of importance measures to DDETs must take into consideration not only the occurrence and non-occurrence of an event, but also uncertain timing or severity. This work describes three general dynamic importance measures (DIMs) as implemented in the Analysis of Dynamic Accident Progression Trees (ADAPT) DDET driver code. The measures account for the change in progression of the DDET resulting from the different values of each uncertain variable under investigation. The DIMs are applied to a sample DDET to demonstrate the type of insights that may be gained from each one. These measures will facilitate comparison of the impact of dynamic branching events on a consequence of interest, which also allow DDETs to be used in prioritizing plant investments. (authors)
- OSTI ID:
- 23042715
- Journal Information:
- Transactions of the American Nuclear Society, Vol. 115; Conference: 2016 ANS Winter Meeting and Nuclear Technology Expo, Las Vegas, NV (United States), 6-10 Nov 2016; Other Information: Country of input: France; 14 refs.; available from American Nuclear Society - ANS, 555 North Kensington Avenue, La Grange Park, IL 60526 (US); ISSN 0003-018X
- Country of Publication:
- United States
- Language:
- English
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