Distributed Dynamic Event Tree Generation for Reliability and Risk Assessment

Rutt, Benjamin; Çatalyürek, Ümit V.; Hakobyan, Aram; Metzroth, Kyle; Aldemir, Tunc; Denning, Richard; Dunagan, Sean; Kunsman, D.M.

doi:10.1109/clade.2006.1652056

Cited by 14 publications

(9 citation statements)

References 33 publications

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“…More specifically, this framework evaluates the evolution of event trees to describe various paths of component and environmental influence interactions between initiating events and possible end states. DPRA employs system-level models to represent complex systems and determine possible evolutions during scenarios of concern (Williams et al 2017a; Rutt et al 2006). This "bottom-up" technique statistically evaluates simulation run-based data across a range of stochastically and deterministically described evolution of DETs.…”

Section: Dynamic Probabilistic Risk Assessment (Dpra)mentioning

confidence: 99%

Using Systems Theory to Address Complex Challenges to International Spent Nuclear Fuel Transportation

Williams

2018

INCOSE International Symp

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Simply looking at a world map suggests new, more complex set of risks and threats will challenge successful international spent nuclear fuel (SNF) transportation operations. Whether related to multimodal transfers of SNF casks or inconsistency in multijurisdictional control measures, international SNF transportation represents a clear example of new threats and risk stemming from a multifaceted, globalized operating environment. In response to recent work out of Sandia National Laboratories (SNL) suggesting that new, system‐theoretic analysis techniques better ensure safe, secure and safeguarded international transportation of SNF, this paper explores the ability of basic systems theory concepts—interdependence, hierarchy and emergence—to better understand and address these complexities in system analysis.

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Section: Dynamic Probabilistic Risk Assessment (Dpra)mentioning

confidence: 99%

Using Systems Theory to Address Complex Challenges to International Spent Nuclear Fuel Transportation

Williams

2018

INCOSE International Symp

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“…RAVEN (Risk Analysis and Virtual control ENviroment) is a software framework that allows the user to perform generic statistical analysis. By statistical analysis we include: Sampling of codes: either stochastic (e.g., Monte-Carlo [13] and Latin Hypercube Sampling [14]) or deterministic (e.g., grid and Dynamic Event Tree [15] Post-processing of the sampled data and generation of statistical parameters (e.g., mean, variance, covariance matrix) Figure 5 shows a general overview of the elements that comprise the RAVEN statistical framework:…”

Section: Raven Statistical Frameworkmentioning

confidence: 99%

“…In Equation 14 the first term denoted is a scalar proportional to the density estimate computed with the kernel and does not provide information regarding where the mode resides. Unlike the first term, the second term in Equation 14, is difference between the weighted mean (15) and the initial estimate . This term points in the direction of local increase in density using kernel , hence provides a means to find the mode of the density.…”

Section: Mean-shiftmentioning

confidence: 99%

“…When power is finally recovered then procedure ECA-0-1 or ECA-0-2 are entered according to some plant conditions. The Dynamic Event Tree (DET) [15] analysis consists in creating a control logic within the Thermal-Hydraulic code simulating the scenario. This is performed by adding a series of trigger that are activated every time that a branching condition is met, creating consequently a set of branch.…”

Section: Pwr Station Black-outmentioning

confidence: 99%

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Data Analysis Approaches for the Risk-Informed Safety Margins Characterization Toolkit

Mandelli

Alfonsi

Maljovec

et al. 2016

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The RISMC project aims to develop new advanced simulation-based tools to perform Computational Risk Analysis (CRA) for the existing fleet of U.S. nuclear power plants (NPPs). These tools numerically model not only the thermal-hydraulic behavior of a reactor primary and secondary systems but also external event temporal evolution and component/system ageing. Thus, this is not only a multi-physics problem but also a multi-scale problem (both spatial, m-mm-m, and temporal, ms-s-minutes-years). As part of the RISMC CRA approach, a large amount of computationally expensive simulation runs may be required. In addition, these uncertainties and safety methods usually generate a large number of simulation runs (database storage may be on the order of gigabytes or higher). During the FY2016, we investigated, implemented and applied several methods and algorithms to analyze these large amounts of time-dependent data. The scope of this report is to present a broad overview of methods and algorithms that can be used 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. Some of the algorithms presented here have been developed or are under development within the RAVEN statistical framework.

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“…In turn, probabilistic dynamics methods can potentially cover the spectrum of event sequences and achieve a realistic probabilistic safety assessment [1][2][3][4][5]. Moreover, the application of these methods generally reduces the analyst-to-analyst variability, because it requires less expert judgement.…”

Section: Introductionmentioning

confidence: 99%

Consideration of human actions in combination with the probabilistic dynamics method Monte Carlo dynamic event tree

Kloos

Peschke

2008

Proceedings of the Institution of Mechanical Engineers, Part O:

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The various accident sequences to be considered in the framework of a probabilistic safety analysis (PSA) for a nuclear power plant derive from interactions between the physical process, technical system functions, operator performance and stochastic influences along the time axis. Probabilistic dynamics methods have been developed to adequately account for these interactions. They can potentially cover the spectrum of event sequences which may actually evolve and achieve a realistic probabilistic safety assessment. The probabilistic dynamics method MCDET is a combination of Monte Carlo simulation and the discrete dynamic event tree (DDET) method. It was implemented as a module which can operate in tandem with any deterministic code simulating the system and process dynamics. MCDET was supplemented by a so-called Crew-Module which permits to simulate the dynamics of human actions depending on but also acting on the system and process dynamics as modelled in the deterministic code and on stochastic influences as modelled in MCDET. This paper presents the Crew-Module and gives an overview of the results which may be obtained from its combination with MCDET and a deterministic dynamics code.

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Distributed Dynamic Event Tree Generation for Reliability and Risk Assessment

Cited by 14 publications

References 33 publications

Using Systems Theory to Address Complex Challenges to International Spent Nuclear Fuel Transportation

Using Systems Theory to Address Complex Challenges to International Spent Nuclear Fuel Transportation

Data Analysis Approaches for the Risk-Informed Safety Margins Characterization Toolkit

Consideration of human actions in combination with the probabilistic dynamics method Monte Carlo dynamic event tree

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