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

Mandelli, Diego¹  Smith, Curtis Lee¹  Alfonsi, Andrea¹  Rabiti, Cristian¹  Cogliati, Joshua Joseph¹ 

[1]Idaho National Lab. (INL), Idaho Falls, ID (United States)

关键词: ALGORITHMS;    SAMPLING;    NUCLEAR POWER PLANTS;    MATHEMATICAL SPACE;    MONTE CARLO METHOD;    COMPARATIVE EVALUATIONS;    PROBABILISTIC ESTIMATION;    SAFETY MARGINS;    STOCHASTIC PROCESSES;    MATHEMATICAL SOLUTIONS;    RISK ASSESSMENT;    REACTOR CORES;    DAMAGE;    PROBABILITY;    RANDOMNESS;    COMPUTERIZED SIMULATION;    R CODES adaptive sampling;    PRA;   

DOI  :  10.2172/1260878
RP-ID  :  INL/EXT--15-35933
PID  :  OSTI ID: 1260878
Others  :  TRN: US1601559

美国|英语

来源: SciTech Connect

【 摘 要 】

The RISMC approach is developing advanced set of methodologies and algorithms in order to perform Probabilistic Risk Analyses (PRAs). In contrast 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 basic idea is to randomly perturb (by employing sampling algorithms) timing and sequencing of events and internal parameters of the system codes (i.e., uncertain parameters) in order to estimate stochastic parameters such as core damage probability. This approach applied to complex systems such as nuclear power plants requires to perform a series of computationally expensive simulation runs given a large set of uncertain parameters. These types of analysis are affected by two issues. Firstly, the space of the possible solutions (a.k.a., the issue space or the response surface) can be sampled only very 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 are not yet available. This report focuses on the first issue and in particular employs novel methods that optimize the information generated by the sampling process by sampling unexplored and risk-significant regions of the issue space: adaptive (smart) sampling algorithms. They infer system response from surrogate models constructed from existing samples and predict the most relevant location of the next sample. It is therefore possible to understand features of the issue space with a small 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 Monte-Carlo. We will employ RAVEN to perform such statistical analyses using both analytical cases but also another RISMC code: RELAP-7.