Mining data in a dynamic PRA framework

Mandelli, Diego; Maljovec, Daniel; Alfonsi, Andrea; Parisi, Carlo; Talbot, Paul; Cogliati, Joshua J.; Smith, Curtis; Rabiti, Cristian

doi:10.1016/j.pnucene.2018.05.004

Mining data in a dynamic PRA framework

Journal Article · Sat May 26 00:00:00 EDT 2018 · Progress in Nuclear Energy

DOI:https://doi.org/10.1016/j.pnucene.2018.05.004· OSTI ID:1478243

^[1]; Maljovec, Daniel ^[1]; ^[1]; ^[1]; ^[1]; ^[1]; ^[1]; ^[1]

Idaho National Lab. (INL), Idaho Falls, ID (United States)

Computational (also known as Dynamic) Probabilistic Risk Assessment (PRA) methods employs system simulation codes coupled with stochastic analysis tools in order to determine probabilities of certain outcomes such as system failure. In contrast to classical PRA methods (i.e., Event-Tree and Fault-Tree) in which timing and sequencing of events is set by the analyst, accident progression is dictated by the system control logic and its interaction with system temporal evolution. Due to the nature of the problem, Simulation-Based PRA methods require large amount of computationally expensive simulation runs. In addition, these methods usually generate a large number of simulation runs (database storage may be on the order of gigabytes or higher). In this paper we investigate and apply several methods and algorithms to analyze these large amounts of time-dependent data. Furthermore, the scope of this article is to present a broad overview of methods and algorithms that can be used to improve data quality and to analyze and extract information from large data sets containing time dependent data. In this context, “extracting information” means constructing input-output correlations, finding commonalities, and identifying outliers.

View Accepted Manuscript (DOE)

Research Organization:: Idaho National Laboratory (INL), Idaho Falls, ID (United States)

Sponsoring Organization:: USDOE Office of Nuclear Energy (NE)

Grant/Contract Number:: AC07-05ID14517

OSTI ID:: 1478243

Alternate ID(s):: OSTI ID: 22831278

Report Number(s):: INL/JOU--18-44526-Rev000

Journal Information:: Progress in Nuclear Energy, Journal Name: Progress in Nuclear Energy Journal Issue: C Vol. 108; ISSN 0149-1970

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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