A Flooding Induced Station Blackout Analysis for a Pressurized Water Reactor Using the RISMC Toolkit

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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Section: Rismc Overviewmentioning

confidence: 99%

“…o Feature Scaling Normalization: this normalization brings all values in the range interval 7 The measures in this table refers to two -dimensional vectors and 8 refers to the covariance matrix. This implies that the covariance matrix needs to be computed prior determining .…”

Section: Data Pre-processingmentioning

confidence: 99%

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

Maljovec

et al. 2016

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“…Equation 9 shows that is equal to the area of the failure region weighted by the probability of being in the failure region itself (through the probability distribution function ( )). Figure 7 shows the limit surface in a 2-dimensional space generated in [17] using RAVEN coupled with RELAP-7 for a boiling water reactor station blackout initiating event. As part of the analysis, we were interested in the evaluation of the safety impacts of power uprate (reactor core power increased from 100 to 120%).…”

Section: Figure 7 -Example Of Limit Surface Calculation For Two Diffementioning

confidence: 99%

Improved Sampling Algorithms in the Risk-Informed Safety Margin Characterization Toolkit

Smith

et al. 2015

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The RISMC approach is developing an advanced set of methodologies and algorithms in order to perform Probabilistic Risk Analyses (PRAs). In contract to classical PRA methods, which are based on Event-Tree and Fault-Tree methods, the RISMC approach largely employs system simulator codes applied to stochastic analysis tools. The fundamental approach via simulation is to randomly perturb (by employing sampling algorithms) timing and sequencing of events and uncertain parameters of the physics-based models (e.g., system codes such as RELAP-7) in order to estimate stochastic outcomes such as off-normal and damage states of the facility. Further, these outcomes can be used to estimate other useful metrics such as the plant core damage probability. This modeling approach applied to complex systems such as nuclear power plants requires the analyst to perform a series of computationally-expensive simulation runs given a large set of uncertain parameters. Consequently, these types of analysis are potentially affected by two issues. Firstly, the space of the possible solutions (the issue space or the response surface) can be sampled only sparsely and this precludes the ability to fully analyze the impact of uncertainties on the system dynamics. Secondly, large amounts of data are generated and tools to generate knowledge from such data sets have not typically been used for safety analysis approaches. This report focuses on the first issue and, in particular, describes how we can use novel methods that optimize the information generated by the sampling process by sampling unexplored or risksignificant regions of the issue space; we call this approach adaptive (smart) sampling algorithms. These methods infer the system response using surrogate models constructed from existing samples and predict the best location of the next sample. By using enhanced methods, it is possible to understand features of the issue space with a smaller number of carefully selected samples. In this report, we will present how it is possible to perform adaptive sampling using the RISMC toolkit and highlight the advantages compared to more classical sampling approaches such as Monte-Carlo. We will employ RAVEN to perform such statistical analyses applied to both analytical cases and more complex cases using RELAP-7.iii CONTENTS EXECUTIVE SUMMARY .

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“…The RISMC approach employs both deterministic and stochastic methods in a single analysis framework (see Figure 2). In the deterministic method set we include:  Modeling of the thermal-hydraulic behavior of the plant [6]  Modeling of external events such as flooding [7]  Modeling of the operators responses to the accident scenario [8] Note that deterministic modeling of the plant or external events can be performed by employing specific simulator codes but also surrogate models (see Section 5), known as reduced order models (ROM). ROMs would be employed in order to decrease the high computational costs of employed codes.…”

Section: Rismc Approachmentioning

confidence: 99%

Reduced Order Model Implementation in the Risk-Informed Safety Margin Characterization Toolkit

Smith

et al. 2015

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