Incorporate Modeling Uncertainty into the Decision Making of Passive System Reliability Assessment

International audiencePassive safety features will play crucial roles in the development of the future generation nuclear power plant technologies. However because of the insufficient experiences, researches and validations are still necessary with the aim to prove the actual performance and reliability of the passive systems. Uncertain resources, which will influence the reliability and performance of such systems, can be divided into two groups: modeling uncertainties and parametric uncertainties. Up to now, the researchers have as good as established ways to quantify the effects caused by the parameter uncertainties, e.g. the variation of physical parameters (environment temperature, fabrication error, etc.) and have already got a number of achievements. In addition to the parameter uncertainty, the modeling uncertainty, e.g. uncertain physical phenomenon, uncertainties by different modeling techniques, etc. shall also be an important contributor to the passive system performance. How to take into account the effect caused by this kind of factors, there hasn't any mature approaches. In this paper, a survey of researches about the modeling uncertainty from the open literature is presented. A framework to incorporate the modeling uncertainty into the decision making of passive system reliability assessment will be proposed based on the survey and the discussion

Comparison of bootstrapped artificial neural networks and quadratic response surfaces for the estimation of the functional failure probability of a thermal-hydraulic passive system

International audienceIn this work, bootstrapped artificial neural network (ANN) and quadratic response surface (RS) empirical regression models are used as fast-running surrogates of a thermal-hydraulic (T-H) system code to reduce the computational burden associated with estimation of functional failure probability of a T-H passive system. The ANN and quadratic RS models are built on a few data representative of the input/output nonlinear relationships underlying the T-H code. Once built, these models are used for performing, in reasonable computational time, the numerous system response calculations required for failure probability estimation. A bootstrap of the regression models is implemented for quantifying, in terms of confidence intervals, the uncertainties associated with the estimates provided by ANNs and RSs. The alternative empirical models are compared on a case study of an emergency passive decay heat removal system of a gas-cooled fast reactor (GFR)

Quantitative functional failure analysis of a thermal-hydraulic passive system by means of bootstrapped Artificial Neural Networks

International audienceThe estimation of the functional failure probability of a thermal-hydraulic (T-H) passive system can be done by Monte Carlo (MC) sampling of the epistemic uncertainties affecting the system model and the numerical values of its parameters, followed by the computation of the system response by a mechanistic T-H code, for each sample. The computational effort associated to this approach can be prohibitive because a large number of lengthy T-H code simulations must be performed (one for each sample) for accurate quantification of the functional failure probability and the related statistics. In this paper, the computational burden is reduced by replacing the long-running, original T-H code by a fast-running, empirical regression model: in particular, an Artificial Neural Network (ANN) model is considered. It is constructed on the basis of a limited-size set of data representing examples of the input/output nonlinear relationships underlying the original T-H code; once the model is built, it is used for performing, in an acceptable computational time, the numerous system response calculations needed for an accurate failure probability estimation, uncertainty propagation and sensitivity analysis. The empirical approximation of the system response provided by the ANN model introduces an additional source of (model) uncertainty, which needs to be evaluated and accounted for. A bootstrapped ensemble of ANN regression models is here built for quantifying, in terms of confidence intervals, the (model) uncertainties associated with the estimates provided by the ANNs. For demonstration purposes, an application to the functional failure analysis of an emergency passive decay heat removal system in a simple steady-state model of a Gas-cooled Fast Reactor (GFR) is presented. The functional failure probability of the system is estimated together with global Sobol sensitivity indices. The bootstrapped ANN regression model built with low computational time on few (e.g., 100) data examples is shown capable of providing reliable (very near to the true values of the quantities of interest) and robust (the confidence intervals are satisfactorily narrow around the true values of the quantities of interest) point estimates

How to effectively compute the reliability of a thermal-hydraulic nuclear passive system

International audienceThe computation of the reliability of a thermal-hydraulic (T-H) passive system of a nuclear power plant can be obtained by (i) Monte Carlo (MC) sampling the uncertainties of the system model and parameters, (ii) computing, for each sample, the system response by a mechanistic T-H code and (iii) comparing the system response with pre-established safety thresholds, which define the success or failure of the safety function. The computational effort involved can be prohibitive because of the large number of (typically long) T-H code simulations that must be performed (one for each sample) for the statistical estimation of the probability of success or failure. The objective of this work is to provide operative guidelines to effectively handle the computation of the reliability of a nuclear passive system. Two directions of computation efficiency are considered: from one side, efficient Monte Carlo Simulation (MCS) techniques are indicated as a means to performing robust estimations with a limited number of samples: in particular, the Subset Simulation (SS) and Line Sampling (LS) methods are identified as most valuable; from the other side, fast-running, surrogate regression models (also called response surfaces or meta-models) are indicated as a valid replacement of the long-running T-H model codes: in particular, the use of bootstrapped Artificial Neural Networks (ANNs) is shown to have interesting potentials, including for uncertainty propagation.The recommendations drawn are supported by the results obtained in an illustrative application of literature

Methods for comparative assessment of active and passive safety systems with respect to reliability, uncertainty, economy, and flexibility

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Nuclear Science and Engineering, 2008.Includes bibliographical references.Passive cooling systems sometimes use natural circulation, and they are not dependent on emergency AC power or offsite power, which can make designs simpler through the reduction of emergency power supplying infrastructure. The passive system approach can lead to substantial simplification of the system as well as overall economic benefits, and passive systems are believed to be less vulnerable to accidents by component failures and human errors compared to active systems. The viewpoint that passive system design is more reliable and more economical than active system design has become generally accepted. However, passive systems have characteristics of a high level of uncertainty and low driving force for purposes of heat removal phenomena. These characteristics of passive systems can result in increasing system unreliability and may raise potential remedial costs during a system's lifetime. This study presents a comprehensive comparison of reliability and cost taking into account uncertainties and introduces the concept of flexibility using the example of active and passive residual heat removal systems in a PWR. The results show that the active system can have, for this particular application, greater reliability than the passive system. Because the passive system is economically optimized, its heat removal capacity is much smaller than that of the active system. Thus, functional failure probability of the passive system has a greater impact on overall system reliability than the active system. Moreover, considering the implications of flexibility upon remedial costs, the active system may more economical than the passive system because the active system has flexible design features for purposes of increasing heat removal capacity.by Jiyong Oh.Ph.D