Reduced Order Modeling for Nonlinear Multi-Component Models

Maljovec

et al. 2016

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: Dtw Distancementioning

confidence: 99%

“…This metric can capture similarities between time series that are shifted in time. 16 Note that here he we have relaxed the requirement: As a reminder, the first path consists of transforming the time time-dependent data into static data. This is possible by performing the following steps in RAVEN:…”

Section: Dtw Distancementioning

confidence: 99%

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

Maljovec

et al. 2016

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“…These two issues, large number of samples required and computational cost of each simulation run, make it challenging to perform a full PRA analysis of complex systems such as nuclear power plants. In order to overcome this challenge, two possible solutions are available [3]: Both solutions rely on the development of Reduced Order Models (ROMs) [4], also known as surrogate models. ROMs are mathematical models that infer the structure of a given set of data points using a blend of regression and interpolation techniques.…”

Section: Stochastic Perturbation Of Internal Elements Of the Physics mentioning

confidence: 99%

“…As mentioned earlier this kind of sampling strategy requires not only simulator codes but also one, or possibly more, ROMs [4]. In our case, it is possible to view the code as a black-box that produces a set of output variables given a set of input parameters :…”

Section: Adaptive Sampling Algorithmmentioning

confidence: 99%

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

Smith

et al. 2015

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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“… Generation of Reduced Order Models [19] also known as Surrogate models  Post-processing of the sampled data and generation of statistical parameters (e.g., mean, variance, covariance matrix)…”

Section: Raven Statistical Frameworkmentioning

confidence: 99%

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

Smith

et al. 2015