Identification of missing scenarios in ESDs using probabilistic dynamics

The event sequence diagram (ESD) framework can be used to qualitatively represent dynamic scenarios. The solution of ESDs can be performed in an analytical manner. Since the construction of ESDs has some inherent analyst dependence, there is scope for omitting scenarios due to certain simplifying assumptions. This is one of the prime drawbacks of the ESD framework. This paper presents an approach for identifying missing scenarios by combining ESDs with probabilistic dynamics. The approach also helps in reducing the variance of a Monte Carlo simulation procedures.     

The Event Sequence Diagram framework for dynamic Probabilistic Risk Assessment

Dynamic methodologies have become fairly established in academia. Their superiority over classical methods like Event Tree/Fault Tree techniques has been demonstrated. Despite this, dynamic methodologies have not enjoyed the support of the industry. One of the primary reasons for the lack of acceptance in the industry is that there is no easy way to qualitatively represent dynamic scenarios. This paper proposes to extend current Event Sequence Diagrams (ESDs) to allow modeling of dynamic situations. Under the proposed ESD representation, ESDs can be used in combination with dynamic methodology computational algorithms which will solve the underlying probabilistic dynamics equations. Once engineers are able to translate their knowledge of the system dynamics and accident evolution into simple ESDs, usage of dynamic methodologies will become more popular.     

Analysis of truncation limit in probabilistic safety assessment

A truncation limit defines the boundaries of what is considered in the probabilistic safety assessment and what is neglected. The truncation limit that is the focus here is the truncation limit on the size of the minimal cut set contribution at which to cut off. A new method was developed, which defines truncation limit in probabilistic safety assessment. The method specifies truncation limits with more stringency than presenting existing documents dealing with truncation criteria in probabilistic safety assessment do. The results of this paper indicate that the truncation limits for more complex probabilistic safety assessments, which consist of larger number of basic events, should be more severe than presently recommended in existing documents if more accuracy is desired. The truncation limits defined by the new method reduce the relative errors of importance measures and produce more accurate results for probabilistic safety assessment applications. The reduced relative errors of importance measures can prevent situations, where the acceptability of change of equipment under investigation according to RG 1.174 would be shifted from region, where changes can be accepted, to region, where changes cannot be accepted, if the results would be calculated with smaller truncation limit.     

A quantitative assessment of LCOs for operations using system dynamics

Limiting conditions for operations (LCOs) define the allowed outage times (AOTs) and the actions to be taken if the repair cannot be completed within the AOT. Typically plant shutdown is required. In situations where the risk associated with the action, i.e. the risk of plant shutdown given a failure of the safety system, may be substantial, a strategy is needed to control the plant risk. In this study the changing operation modes are evaluated quantitatively and dynamically using the tool of system dynamics. System dynamics has been developed to analyze the dynamic reliability of a complicated system. System dynamics using the Vensim software have been applied to LCOs assessment for an example system, the auxiliary feed water system of a reference nuclear power plant. Analysis results of both full power operation and shutdown operation have been compared for a measure of core damage frequency. The increase in core damage frequency is used as a measure in this study. A time dependent framework developed in this study has been shown to be very flexible in that it can be applied to assess LCOs quantitatively under any operational context of the Technical Specifications in Final Safety Analysis Report of the reference plant.     

The human error rate assessment and optimizing system HEROS — a new procedure for evaluating and optimizing the man–machine interface in PSA

A new procedure allowing the probabilistic evaluation and optimization of the man–machine system is presented. This procedure and the resulting expert system HEROS, which is an acronym for Human Error Rate Assessment and Optimizing System, is based on the fuzzy set theory. Most of the well-known procedures employed for the probabilistic evaluation of human factors involve the use of vague linguistic statements on performance shaping factors to select and to modify basic human error probabilities from the associated databases. This implies a large portion of subjectivity. Vague statements are expressed here in terms of fuzzy numbers or intervals which allow mathematical operations to be performed on them. A model of the man–machine system is the basis of the procedure. A fuzzy rule-based expert system was derived from ergonomic and psychological studies. Hence, it does not rely on a database, whose transferability to situations different from its origin is questionable. In this way, subjective elements are eliminated to a large extent. HEROS facilitates the importance analysis for the evaluation of human factors, which is necessary for optimizing the man–machine system. HEROS is applied to the analysis of a simple diagnosis of task of the operating personnel in a nuclear power plant.     

A framework to integrate software behavior into dynamic probabilistic risk assessment

Software plays an increasingly important role in modern safety-critical systems. Although, research has been done to integrate software into the classical probabilistic risk assessment (PRA) framework, current PRA practice overwhelmingly neglects the contribution of software to system risk. Dynamic probabilistic risk assessment (DPRA) is considered to be the next generation of PRA techniques. DPRA is a set of methods and techniques in which simulation models that represent the behavior of the elements of a system are exercised in order to identify risks and vulnerabilities of the system. The fact remains, however, that modeling software for use in the DPRA framework is also quite complex and very little has been done to address the question directly and comprehensively. This paper develops a methodology to integrate software contributions in the DPRA environment. The framework includes a software representation, and an approach to incorporate the software representation into the DPRA environment SimPRA. The software representation is based on multi-level objects and the paper also proposes a framework to simulate the multi-level objects in the simulation-based DPRA environment. This is a new methodology to address the state explosion problem in the DPRA environment. This study is the first systematic effort to integrate software risk contributions into DPRA environments.     

Real-Time Assessment and Diagnosis of Process Operating Performance

Over time, the performance of processes may deviate from the initial design due to process variations and uncertainties, making it necessary to develop systematic methods for online optimality assessment based on routine operating process data. Some processes have multiple operating modes caused by the set point change of the critical process variables to achieve different product specifications. On the other hand, the operating region in each operating mode can alter, due to uncertainties. In this paper, we will establish an optimality assessment framework for processes that typically have multi-mode, multi-region operations, as well as transitions between different modes. The kernel density approach for mode detection is adopted and improved for operating mode detection. For online mode detection, the model-based clustering discriminant analysis (MclustDA) approach is incorporated with some a priori knowledge of the system. In addition, multi-modal behavior of steady-state modes is tackled utilizing the mixture probabilistic principal component regression (MPPCR) method, and dynamic principal component regression (DPCR) is used to investigate transitions between different modes. Moreover, a probabilistic causality detection method based on the sequential forward floating search (SFFS) method is introduced for diagnosing poor or non-optimum behavior. Finally, the proposed method is tested on the Tennessee Eastman (TE) benchmark simulation process in order to evaluate its performance.     

GOALDS—Goal Based Damage Ship Stability and safety standards

The new probabilistic damaged stability regulations for dry cargo and passenger ships (SOLAS 2009), which entered into force on January 1, 2009, represent a major step forward in achieving an improved safety standard through the rationalisation and harmonization of damaged stability requirements. There are, however, serious concerns regarding the adopted formulation for the calculation of the survival probability of passenger ships, particularly for ROPAX and large cruise vessels. The present paper outlines the objectives, the methodology of work and main results of the EU-funded FP7 project GOALDS (Goal Based Damaged Stability, 2009–2012), which aims to address the above shortcomings by state-of-the-art scientific methods and by formulating a rational, goal-based regulatory framework, properly accounting for the damage stability properties of passenger ships and the risk of people onboard.     

Probabilistic approach to manipulator kinematics and dynamics

A high performance, high speed robotic arm must be able to manipulate objects with a high degree of accuracy and repeatability. As with any other physical system, there are a number of factors causing uncertainties in the behavior of a robotic manipulator. These factors include manufacturing and assembling tolerances, and errors in the joint actuators and controllers. In order to study the effect of these uncertainties on the robotic end-effector and to obtain a better insight into the manipulator behavior, the manipulator kinematics and dynamics are modeled using a probabilistic approach. Based on the probabilistic model, kinematic and dynamic performance criteria are defined to provide measures of the behavior of the robotic end-effector. Techniques are presented to compute the kinematic and dynamic reliabilities of the manipulator. The effects of tolerances associated with the various manipulator parameters on the reliabilities are studied. Numerical examples are presented to illustrate the procedures.     

A dynamic fault tree

The fault tree analysis is a widely used method for evaluation of systems reliability and nuclear power plants safety. This paper presents a new method, which represents extension of the classic fault tree with the time requirements. The dynamic fault tree offers a range of risk informed applications. The results show that application of dynamic fault tree may reduce the system unavailability, e.g. by the proper arrangement of outages of safety equipment. The findings suggest that dynamic fault tree is a useful tool to expand and upgrade the existing models and knowledge obtained from probabilistic safety assessment with additional and time dependent information to further reduce the plant risk.     

Assessment of human reliability based on evaluation of plant experience: requirements and implementation

A major problem in assessment of human failures in probabilistic safety assessment is the lack of empirical data needed for human reliability analysis (HRA). This problem is aggravated by the fact that different HRA methods use different parameters for the assessment and that HRA is currently enforced to provide data and methods for assessment of human reliability in new technical environments such as computerized control rooms, in accident management situations, or in low-power and shut down situations. Plant experience is one source to deal with this problem. In this paper, a method is presented that describes how plant experience about human failures and human performance may be used to support the process of analyzing and assessing human reliability. Based on considerations of requirements of HRA, a method is presented first which is able to describe and analyze human interactions that were observed within events. Implementation of the approach as a database application is outlined. Second, the main results of the application of the method to 165 boiling water reactor events are presented. Observed influencing factors on human performance are discussed; estimates for probabilities are calculated and compared with the data tables of the THERP handbook. An outline is given for using the presented method for the analysis of cognitive errors or organizational aspects.     

Advanced nuclear power plant regulation using risk-informed and performance-based methods

This paper proposes and discusses implications of a largely probabilistic regulatory framework using best-estimate, goal-driven, risk-informed, and performance-based methods. This framework relies on continuous probabilistic assessment of performance of a set of time-dependent, safety-critical systems, structures, components, and procedures that assure attainment of a broad set of overarching technology-neutral protective, mitigative, and preventive goals under all phases of plant operations. In this framework acceptable levels of performance are set through formal apportionment so that they are commensurate with the overarching goals. Regulatory acceptance would be the based on the confidence level with which the plant conforms to these goals and performance objectives. The proposed framework uses the traditional defense-in-depth design and operation regulatory philosophy when uncertainty in conforming to specific goals and objectives is high. Finally, the paper discusses the steps needed to develop a corresponding technology-neutral regulatory approach from the proposed framework.     

Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents: Part 5: Dynamic probabilistic simulation of the IDAC model

This is the last in a series of five papers that discuss the Information Decision and Action in Crew (IDAC) context for human reliability analysis (HRA) and example application. The model is developed to probabilistically predict the responses of the control room operating crew in nuclear power plants during an accident, for use in probabilistic risk assessments (PRA). The operator response spectrum includes cognitive, emotional, and physical activities during the course of an accident. This paper describes a dynamic PRA computer simulation program, accident dynamics simulator (ADS), developed in part to implement the IDAC model. This paper also provides a detailed example of implementing a simpler version of IDAC, compared with the IDAC model discussed in the first four papers of this series, to demonstrate the practicality of integrating a detailed cognitive HRA model within a dynamic PRA framework.     

Numerical homogenization of N‐component composites including stochastic interface defects

The purpose of this paper is to present a mathematical formulation and numerical analysis for a homogenization problem of random elastic composites with stochastic interface defects. The homogenization of composites so defined is carried out in two steps: (i) probabilistic averaging of stochastic discontinuities in the interphase region, (ii) probabilistic homogenization by extending the effective modules method to media random in the micro‐scale. To obtain such an approach the classical mathematical homogenization method is formulated for n‐component composite with random elastic components and implemented in the FEM‐based computer program. The article contains also numerous computational experiments illustrating stochastic sensitivity of the model to interface defects parameters and verifying statistical convergence of probabilistic simulation procedure. Copyright © 2000 John Wiley & Sons, Ltd.     

A probabilistic framework for detecting and identifying anomalies

This paper develops a probabilistic framework to detect and identify anomalies such as damage in structures. The framework is developed by introducing new terms and definitions with their corresponding mathematical formulation. An advantage of the new framework is that ill-conditioning in the identification problem is avoided and that a clear relation between measurements and modeling is established. Special results are then obtained in the form of bounds that allow for computationally efficient applications. An example application is then presented. The application is to detect and identify part-through cracks in a plate from surface strain measurements. In this application problem, the role of strain gauge size and measurement errors are considered and discussed.     

Dynamic reliability of digital-based transmitters

Dynamic reliability explicitly handles the interactions between the stochastic behaviour of system components and the deterministic behaviour of process variables. While dynamic reliability provides a more efficient and realistic way to perform probabilistic risk assessment than “static” approaches, its industrial level applications are still limited. Factors contributing to this situation are the inherent complexity of the theory and the lack of a generic platform. More recently the increased use of digital-based systems has also introduced additional modelling challenges related to specific interactions between system components. Typical examples are the “intelligent transmitters” which are able to exchange information, and to perform internal data processing and advanced functionalities. To make a contribution to solving these challenges, the mathematical framework of dynamic reliability is extended to handle the data and information which are processed and exchanged between systems components. Stochastic deviations that may affect system properties are also introduced to enhance the modelling of failures. A formalized Petri net approach is then presented to perform the corresponding reliability analyses using numerical methods. Following this formalism, a versatile model for the dynamic reliability modelling of digital-based transmitters is proposed. Finally the framework's flexibility and effectiveness is demonstrated on a substantial case study involving a simplified model of a nuclear fast reactor.     

Response and reliability analysis of a high-dimensional stochastic system

The efficiency of the probability density evolution method (PDEM) is improved in this paper by embedding the Kullback–Leibler (K–L) relative sensitivity in the response analysis of a stochastic dynamic system. The response reliability obtained and the probability density function of the response peaks are used for ranking to get a reduced set of random variables for the PDEM analysis. The need of complicated point selection technique with the high-dimensional uncertain variables is therefore alleviated. The proposed method is illustrated with the response analysis of a random crowd-structure system where the load randomness is considered. The acceleration response induced by the presence of the crowd is evaluated with the proposed method. Results obtained highlight the significant improvements in the computation efficiency of the probabilistic response analysis of a high-dimensional dynamic system.     

Risk-based inspection and maintenance in process plants and their practices in Taiwan

The content of risk-based assessment and management and risk-based inspection and maintenance (RBI&M) employed in process plants are addressed in this article. Probabilistic analyses including probabilistic risk assessment developed in the engineering field over the past few decades are reviewed first. Following that, RBI and risk-informed management employed in the nuclear industry and risk-based inspection, maintenance, and other risk-based approaches used in chemical plants as well as their developments are reviewed, respectively. While most of the above were developed and practiced in industrialized countries, the current situation in Taiwan is also mentioned. Special attention is paid to the possibility and difficulties of employing RBI&M in Taiwan’s power and process plants. The difficulties in adopting existing risk-based approaches to inspect and manage Kaohsiung’s underground pipelines are pointed out in particular. Comments are made at the end of the article.     

Domain decomposition of stochastic PDEs: Theoretical formulations

We present a novel theoretical framework for the domain decomposition of uncertain systems defined by stochastic partial differential equations. The methodology involves a domain decomposition method in the geometric space and a functional decomposition in the probabilistic space. The probabilistic decomposition is based on a version of stochastic finite elements based on orthogonal decompositions and projections of stochastic processes. The spatial decomposition is achieved through a Schur‐complement‐based domain decomposition. The methodology aims to exploit the full potential of high‐performance computing platforms by reducing discretization errors with high‐resolution numerical model in conjunction to giving due regards to uncertainty in the system. The mathematical formulation is numerically validated with an example of waves in random media. Copyright © 2008 John Wiley & Sons, Ltd.     

A framework for data-driven structural analysis in general elasticity based on nonlinear optimization: The dynamic case

In this article, we present an extension of the formulation recently developed by the authors to the structural dynamics setting. Inspired by a structure-preserving family of variational integrators, our new formulation relies on a discrete balance equation that establishes the dynamic equilibrium. From this point of departure, we first derive an “exact” discrete-continuous nonlinear optimization problem that works directly with data sets. We then develop this formulation further into an “approximate” nonlinear optimization problem that relies on a general constitutive model. This underlying model can be identified from a data set in an offline phase. To showcase the advantages of our framework, we specialize our methodology to the case of a geometrically exact beam formulation that makes use of all elements of our approach. We investigate three numerical examples of increasing difficulty that demonstrate the excellent computational behavior of the proposed framework and motivate future research in this direction.