Light Water Reactor Sustainability Program: Analysis of Pressurized Water Reactor Station Blackout Caused by External Flooding Using the RISMC Toolkit

Smith, Curtis; Mandelli, Diego; Prescott, Steven; Alfonsi, Andrea; Rabiti, Cristian; Cogliati, Joshua J.; Kinoshita, Robert

doi:10.2172/1168617

Cited by 7 publications

(11 citation statements)

References 10 publications

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“…In [SM14] we have shown how this interaction occurs for a seismic-induced flooding initiating event. In that example, the flooding simulator was responsible to identify the status (failed or working) of the diesel generators and the plant switchyard.…”

Section: Figure 1 -Overview Of the Rismc Approachmentioning

confidence: 91%

“…In this report we focus on the deterministic modeling of external events and in particular flooding related events. In [SM14] we showed the feasibility of an analysis that couples an external event simulation code (i.e., the NEUTRINO flooding code) and a plant system simulation code (i.e., RELAP-7). That analysis shows not only the feasibility of this coupling but, more important, how external events are modeled in the RISMC approach.…”

Section: Introductionmentioning

confidence: 99%

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3D Simulation of External Flooding Events for the RISMC Pathway

Prescott

Mandelli

Sampath

et al. 2015

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Incorporating 3D simulations as part of the Risk-Informed Safety Margins Characterization (RISMIC) Toolkit allows analysts to obtain a more complete picture of complex system behavior for events including external plant hazards. External events such as flooding have become more important recently -however these can be analyzed with existing and validated simulated physics toolkits. In this report, we describe these approaches specific to flooding-based analysis using an approach called Smoothed Particle Hydrodynamics. The theory, validation, and example applications of the 3D flooding simulation are described. Integrating these 3D simulation methods into computational risk analysis provides a spatial/visual aspect to the design, improves the realism of results, and can prove visual understanding to validate the analysis of flooding.

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Section: Figure 1 -Overview Of the Rismc Approachmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

3D Simulation of External Flooding Events for the RISMC Pathway

Prescott

Mandelli

Sampath

et al. 2015

Self Cite

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“…Development of simulation-based fragility models for the top events mentioned in section Event Tree / Fault Tree Logic can be either a finite element (FE) or a smoothed-particle hydrodynamic (SPH) analysis (Bodda, 2018;Dinh et al, 2015;Lin, 2019;Prescott et al, 2015;Smith et al, 2014). In this study, the intensity measure for the initiating event is storm surge height.…”

Section: Fragility Estimatesmentioning

confidence: 99%

Risk informed validation framework for external flooding scenario

Bodda

Gupta

Dinh

2020

Nuclear Engineering and Design

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Safety of nuclear plants against external flooding has gained significant attention following the accident at Fukushima Daiichi nuclear power station. In United States, Oyster Creek nuclear plant was safely shutdown when high storm surge during hurricane Sandy caused a potential flooding threat. Subsequently, the nuclear energy industry experienced a significant activity in Probabilistic Risk Assessment (PRA) for external flooding. Increasingly, methods of computational fluid dynamics including advanced simulation codes are being considered to evaluate the sequence of events during different scenarios of flooding at a plant. One of the key limitations in the use of advanced codes for external flooding is related to a lack of credibility of such simulations. The motivation of this study is to develop a formal validation approach that provides a basis to quantify credibility of risk assessments that are based on advanced simulation codes. In this paper, we take a simple synthetic example to evaluate the applicability of the proposed framework to validation of flooding PRA scenario.

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“…An example of PWR SBO scenario generated using RELAP-7 is shown in Figure 22 For the scope of this report we wanted to show one of the capabilities of RAVEN to generate ROM and perform statistical analysis on them. For this case we collected the actual simulated data by RELAP-7 in [30], generated a ROM from such data and performed adaptive sampling on the ROM instead of the RELAP-7 code. In more detail, we performed the following steps:…”

Section: Recovery Time Of Dgsmentioning

confidence: 99%

“…1. Retrieved the hdf5 data generated by sampling RELAP-7 in [30] 2. Trained a ROM given the data retrieved in Step 1 3.…”

Section: Recovery Time Of Dgsmentioning

confidence: 99%

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

Mandelli

Smith

Alfonsi

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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Light Water Reactor Sustainability Program: Analysis of Pressurized Water Reactor Station Blackout Caused by External Flooding Using the RISMC Toolkit

Cited by 7 publications

References 10 publications

3D Simulation of External Flooding Events for the RISMC Pathway

3D Simulation of External Flooding Events for the RISMC Pathway

Risk informed validation framework for external flooding scenario

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

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