Improving Limit Surface Search Algorithms in RAVEN Using Acceleration Schemes: Level II Milestone

Technical Report · Wed Jul 01 00:00:00 EDT 2015

DOI:https://doi.org/10.2172/1244620· OSTI ID:1244620

Alfonsi, Andrea ^[1]; Rabiti, Cristian ^[1]; Mandelli, Diego ^[1]; Cogliati, Joshua Joseph ^[1]; Sen, Ramazan Sonat ^[1]; Smith, Curtis Lee ^[1]

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1. Idaho National Laboratory (INL), Idaho Falls, ID (United States)

The RAVEN code is becoming a comprehensive tool to perform Probabilistic Risk Assessment (PRA); Uncertainty Quantification (UQ) and Propagation; and Verification and Validation (V&V). The RAVEN code is being developed to support the Risk-Informed Safety Margin Characterization (RISMC) pathway by developing an advanced set of methodologies and algorithms for use in advanced risk analysis. The RISMC approach uses system simulator codes applied to stochastic analysis tools. The fundamental idea behind this coupling approach to perturb (by employing sampling strategies) timing and sequencing of events, internal parameters of the system codes (i.e., uncertain parameters of the physics model) and initial conditions to estimate values ranges and associated probabilities of figures of merit of interest for engineering and safety (e.g. core damage probability, etc.). This approach applied to complex systems such as nuclear power plants requires performing a series of computationally expensive simulation runs. The large computational burden is caused by the large set of (uncertain) parameters characterizing those systems. Consequently, exploring the uncertain/parametric domain, with a good level of confidence, is generally not affordable, considering the limited computational resources that are currently available. In addition, the recent tendency to develop newer tools, characterized by higher accuracy and larger computational resources (if compared with the presently used legacy codes, that have been developed decades ago), has made this issue even more compelling. In order to overcome to these limitations, the strategy for the exploration of the uncertain/parametric space needs to use at best the computational resources focusing the computational effort in those regions of the uncertain/parametric space that are “interesting” (e.g., risk-significant regions of the input space) with respect the targeted Figures Of Merit (FOM): for example, the failure of the system, subject of the analysis. These methodologies are named, in the RAVEN environment, adaptive sampling strategies. These methodologies infer system responses from surrogate models constructed from already existing samples (produced using high fidelity simulations) and suggest the most relevant location (coordinate in the input space) of the next sampling point to be explored in the uncertain/parametric domain. When using those methodologies, it is possible to understand features of the system response with a small number of carefully selected samples. This report focuses on the development and improvement of the limit surface search. The limit surface is an important concept in system reliability analysis. Without going into the details, which will be covered later in the report, the limit surface could be briefly described as an hyper-surface in the system uncertainty/parametric space separating the regions leading to a prescribed system outcome. For example, if the uncertainty/parametric space is the one generated by the reactor power level and the duration of the batteries, the system is a nuclear power plant and the system outcome discriminating variable is the clad failure in a station blackout scenario, then the limit surface separates the combinations of reactor power level and battery duration that lead to clad failure from the ones that do not.

• Research Organization: Idaho National Lab. (INL), Idaho Falls, ID (United States)
• Sponsoring Organization: USDOE Office of Nuclear Energy (NE)
• DOE Contract Number: AC07-05ID14517
• OSTI ID: 1244620
• Report Number(s): INL/EXT-15-36100; M2LW-15IN0704047; TRN: US1601048
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS
R CODES
NUCLEAR POWER PLANTS
MATHEMATICAL MANIFOLDS
ALGORITHMS
MATHEMATICAL SPACE
SAFETY MARGINS
RISK ASSESSMENT
FAILURES
SAMPLING
COMPARATIVE EVALUATIONS
PROBABILISTIC ESTIMATION
REACTOR SAFETY
PERFORMANCE
PROBABILITY
SIMULATION
VALIDATION
STOCHASTIC PROCESSES
ACCURACY
DAMAGE
REACTOR CORES
RELIABILITY
VERIFICATION
Limit Surface
Mathematics
PRA
Reliability Surface
Uncertainty Quantification