Diego Mandelli

Probabilistic risk assessment modeling of digital instrumentation and control systems using two dynamic methodologies

The Markov/cell-to-cell mapping technique (CCMT) and the dynamic flowgraph methodology (DFM) are two system logic modeling methodologies that have been proposed to address the dynamic characteristics of digital instrumentation and control (I&C) systems and provide risk-analytical capabilities that supplement those provided by traditional probabilistic risk assessment (PRA) techniques for nuclear power plants. Both methodologies utilize a discrete state, multi-valued logic representation of the digital I&C system. For probabilistic quantification purposes, both techniques require the estimation of the probabilities of basic system failure modes, including digital I&C software failure modes, that appear in the prime implicants identified as contributors to a given system event of interest. As in any other system modeling process, the accuracy and predictive value of the models produced by the two techniques, depend not only on the intrinsic features of the modeling paradigm, but also and to a considerable extent on information and knowledge available to the analyst, concerning the system behavior and operation rules under normal and off-nominal conditions, and the associated controlled/monitored process dynamics. The application of the two methodologies is illustrated using a digital feedwater control system (DFWCS) similar to that of an operating pressurized water reactor. This application was carried out to demonstrate how the use of either technique, or both, can facilitate the updating of an existing nuclear power plant PRA model following an upgrade of the instrumentation and control system from analog to digital. Because of scope limitations, the focus of the demonstration of the methodologies was intentionally limited to aspects of digital I&C system behavior for which probabilistic data was on hand or could be generated within the existing project bounds of time and resources. The data used in the probabilistic quantification portion of the process were gathered partially from fault injection experiments with the DFWCS, separately conducted under conservative assumptions, partially from operating experience, and partially from generic data bases. The purpose of the quantification portion of the process was, purely to demonstrate the PRA-updating use and application of the methodologies, without making any particular claim regarding the specific validity and predictive value of the data utilized to illustrate the quantitative risk calculations produced from the qualitative information analytically generated by the models. A comparison of the results obtained from the Markov/CCMT and DFM regarding the event sequences leading to DFWCS failure modes show qualitative and quantitative consistency for the risk scenarios and sequences under consideration. The study also shows that: (a) the risk significance of the timing of system component failures may depend on factors that include the actual variability of initiating conditions of a dynamic transient, even within the nominal control range and (b) the range of dynamic outcomes may also be dependent on the choice of the assumed basic system-component failure modes included in the models, regardless of whether some of these would or would not be considered to have direct safety implications according to the traditional safety/non-safety equipment classifications.

SCENARIO CLUSTERING AND DYNAMIC PROBABILISTIC RISK ASSESSMENT

A challenging aspect of dynamic methodologies for probabilistic risk assessment (PRA), such as the Dynamic Event Tree (DET) methodology, is the large number of scenarios generated for a single initiating event. Such large amounts of information can be difficult to organize for extracting useful information. Furthermore, it is not often sufficient to merely calculate a quantitative value for the risk and its associated uncertainties. The development of risk insights that can increase system safety and improve system performance requires the interpretation of scenario evolutions and the principal characteristics of the events that contribute to the risk. For a given scenario dataset, it can be useful to identify the scenarios that have similar behaviors (i.e., identify the most evident classes), and decide for each event sequence, to which class it belongs (i.e., classification). It is shown how it is possible to accomplish these two objectives using the Mean-Shift Methodology (MSM). The MSM is a kernel-based, non-parametric density estimation technique that is used to find the modes of an unknown data distribution. The algorithm developed finds the modes of the data distribution in the state space corresponding to regions with highest data density as well as grouping the scenarios generated into clusters based on scenario temporal similarities. The MSM is illustrated using the data generated by a DET algorithm for the analysis of a simple level/temperature controller and reactor vessel auxiliary cooling system.

A Benchmark System for Comparing Reliability Modeling Approaches for Digital Instrumentation and Control Systems

Abstract There is an accelerating trend to upgrade and replace nuclear power plant analog instrumentation and control systems with digital systems. While various methodologies are available for the reliability modeling of these systems for plant probabilistic risk assessments, there is no benchmark system that can be used as the basis for methodology comparison. A system representative of the steam generator feedwater control systems in pressurized water reactors is proposed for such a comparison. Dynamic reliability modeling of the benchmark system for an example initiating event is illustrated using the Markov/cell-to-cell mapping technique and dynamic flowgraph methodologies.

Light Water Reactor Sustainability Program Status of Adaptive Surrogates within the RAVEN framework

................................................................................................................................................ iii FIGURES ....................................................................................................................................................... v TABLES ....................................................................................................................................................... vi ACRONYMS .............................................................................................................................................. vii

RAVEN Beta Release

This documents the release of the Risk Analysis Virtual Environment (RAVEN) code. A description of the RAVEN code is provided, and discussion of the release process for the M2LW-16IN0704045 milestone. The RAVEN code is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. RAVEN is capable of investigating the system response as well as the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. RAVEN has now increased in maturity enough for the Beta 1.0 release.

Proof-of-Concept Demonstrations for Computation-Based Human Reliability Analysis. Modeling Operator Performance During Flooding Scenarios

................................................................................................................................................. ii FIGURES...................................................................................................................................................... v TABLES ...................................................................................................................................................... vi ACRONYMS.............................................................................................................................................. vii

3D Simulation of External Flooding Events for the RISMC Pathway

................................................................................................................................................. ii FIGURES ..................................................................................................................................................... iv TABLES ....................................................................................................................................................... v ACRONYMS ............................................................................................................................................... vi

DAKOTA reliability methods applied to RAVEN/RELAP-7.

This report summarizes the result of a NEAMS project focused on the use of reliability methods within the RAVEN and RELAP-7 software framework for assessing failure probabilities as part of probabilistic risk assessment for nuclear power plants. RAVEN is a software tool under development at the Idaho National Laboratory that acts as the control logic driver and post-processing tool for the newly developed Thermal-Hydraulic code RELAP-7. Dakota is a software tool developed at Sandia National Laboratories containing optimization, sensitivity analysis, and uncertainty quantification algorithms. Reliability methods are algorithms which transform the uncertainty problem to an optimization problem to solve for the failure probability, given uncertainty on problem inputs and a failure threshold on an output response. The goal of this work is to demonstrate the use of reliability methods in Dakota with RAVEN/RELAP-7. These capabilities are demonstrated on a demonstration of a Station Blackout analysis of a simplified Pressurized Water Reactor (PWR).

RAVEN: Dynamic Event Tree Approach Level III Milestone

Conventional Event-Tree (ET) based methodologies are extensively used as tools to perform reliability and safety assessment of complex and critical engineering systems. One of the disadvantages of these methods is that timing/sequencing of events and system dynamics are not explicitly accounted for in the analysis. In order to overcome these limitations several techniques, also know as Dynamic Probabilistic Risk Assessment (DPRA), have been developed. Monte-Carlo (MC) and Dynamic Event Tree (DET) are two of the most widely used D-PRA methodologies to perform safety assessment of Nuclear Power Plants (NPP). In the past two years, the Idaho National Laboratory (INL) has developed its own tool to perform Dynamic PRA: RAVEN (Reactor Analysis and Virtual control ENvironment). RAVEN has been designed to perform two main tasks: 1) control logic driver for the new Thermo-Hydraulic code RELAP-7 and 2) post-processing tool. In the first task, RAVEN acts as a deterministic controller in which the set of control logic laws (user defined) monitors the RELAP-7 simulation and controls the activation of specific systems. Moreover, the control logic infrastructure is used to model stochastic events, such as components failures, and perform uncertainty propagation. Such stochastic modeling is deployed using both MC and DET algorithms. In the second task, RAVEN processes the large amount of data generated by RELAP-7 using data-mining based algorithms. This report focuses on the analysis of dynamic stochastic systems using the newly developed RAVEN DET capability. As an example, a DPRA analysis, using DET, of a simplified pressurized water reactor for a Station Black-Out (SBO) scenario is presented.