Safety system consideration of a supercritical-water cooled fast reactor with simplified PSA

The probabilistic safety of the supercritical-water cooled fast reactor (SCFR) is evaluated with the simplified probabilistic safety assessment (PSA) methodology. SCFR has a once-through direct cycle where all feedwater flows through the core to the turbine at supercritical pressure. There are no recirculation loops in the once-through direct cycle system, which is the most important difference from the current light water reactor (LWR). The main objective of the present study is to assess the effect of this difference on the safety in the stage of conceptual design study. A safety system configuration similar to the advanced boiling water reactor (ABWR) is employed. At loss of flow events, no natural recirculation occurs. Thus, emergency core flow should be quickly supplied before the completion of the feedwater pump coastdown at a loss of flow accident. The motor-driven high pressure coolant injection (MD-HPCI) system cannot be used for the quick core cooling due to the delay of the emergency diesel generator (D/G) start-up. Accordingly, an MD-HPCI system in an ABWR is substituted by a turbine-driven (TD-) HPCI system for the SCFR. The calculated core damage frequency (CDF) is a little higher than that of the Japanese ABWR and a little lower than that of the Japanese BWR when Japanese data are employed for initiating event frequencies. Four alternatives to the safety system configurations are also examined as a sensitivity analysis. This shows that the balance of the safety systems designed here is adequate. Consequently, though the SCFR has a once-through coolant system, the CDF is not high due to the diversity of feedwater systems as the direct cycle characteristics.     

Cold-standby redundancy optimization for nonrepairable systems

A solution methodology is described and demonstrated to determine optimal design configurations for nonrepairable series-parallel systems with cold-standby redundancy. This problem formulation considers non-constant component hazard functions and imperfect switching. The objective of the redundancy allocation problem is to select from available components and to determine an optimal design configuration to maximize system reliability. For cold-standby redundancy, other formulations have generally required exponential component time-to-failure and perfect switching assumptions. For this paper, there are multiple component choices available for each subsystem and component time-to-failure is distributed according to an Erlang distribution. Optimal solutions are determined based on an equivalent problem formulation and integer programming. Compared to other available algorithms, the methodology presented here more accurately models many engineering design problems with cold-standby redundancy. Previously, it has been difficult to determine optimal solutions for this class of problems or even lo efficiently calculate system reliability. The methodology is successfully demonstrated on a large problem with 14 subsystems.     

Probabilistic safety assessment support for the maintenance rule at Duke Power Company

The Nuclear Regulatory Commission (NRC) published the Maintenance Rule on July 10, 1991 with an implementation date of July 10, 1996 [1]. Maintenance rule implementation at the Duke Power Company has used probabilistic safety assessment (PSA) insights to help focus the monitoring of structures, systems and components (SSC) performance and to ensure that maintenance is effectively performed. This paper describes how the probabilistic risk assessment (PRA)¹ group at the Duke Power Company provides support for the maintenance rule by performing the following tasks: (1) providing a member of the expert panel; (2) determining the risk-significant SSCs; (3) establishing SSC performance criteria for availability and reliability; (4) evaluating past performance and its impact on core damage risk as part of the periodic assessment; (5) providing input to the PRA matrix; (6) providing risk analyses of combinations of SSCs out of service; (7) providing support for the SENTINEL program; and (8) providing support for PSA training. These tasks are not simply tied to the initial implementation of the rule. The maintenance rule must be kept consistent with the current design and operation of the plant. This will require that the PRA models and the many PSA calculations performed to support the maintenance rule are kept up-to-date. Therefore, support of the maintenance rule will be one of the primary roles of the PSA group for the remainder of the life of the plant.     

Maximization of System Reliability with a Choice of Redundancy Strategies

Optimal solutions to the redundancy allocation problem are determined when either active or cold-standby redundancy can be selectively chosen for individual subsystems. This problem involves the selection of components and redundancy levels to maximize system reliability. Previously, solutions to the problem could only be found if analysts were restricted to a predetermined redundancy strategy for the complete system. Generally, it had been assumed that active redundancy was to be used. However, in practice both active and cold-standby redundancy may be used within a particular system design and the choice of redundancy strategy becomes an additional decision variable. Available optimization algorithms are inadequate for these design problems and better alternatives are required. The methodology presented here is specifically developed to accommodate the case where there is a choice of redundancy strategy. The problem is formulated with imperfect sensing and switching of cold-standby redundant components and k -Erlang distributed time-to-failure. Optimal solutions to the problem are found by an equivalent problem formulation and integer programming. The methodology is demonstrated on a well-known test problem with interesting results. The optimal system design is distinctly different from the corresponding design obtained with only active redundancy. The availability of this tool can result in more reliable and cost-effective engineering designs.     

Use of risk assessment in the nuclear industry with specific reference to the Australian situation

The use of risk assessment in the nuclear industry began in the 1970s as a complementary approach to the deterministic methods used to assess the safety of nuclear facilities. As experience with the theory and application of probabilistic methods has grown, so too has its application. In the last decade, the use of probabilistic safety assessment has become commonplace for all phases of the life of a plant, including siting, design, construction, operation and decommissioning. In the particular case of operation of plant, the use of a ‘living’ safety case or probabilistic safety assessment, building upon operational experience, is becoming more widespread, both as an operational tool and as a basis for communication with the regulator. In the case of deciding upon a site for a proposed reactor, use is also being made of probabilistic methods in defining the effect of design parameters. Going hand in hand with this increased use of risk based methods has been the development of assessment criteria against which to judge the results being obtained from the risk analyses. This paper reviews the use of risk assessment in the light of the need for acceptability criteria and shows how these tools are applied in the Australian nuclear industry, with specific reference to the probabilistic safety assessment (PSA) performed of HIFAR.     

Application of genetic algorithm for reliability allocation in nuclear power plants

Reliability allocation is an optimization process of minimizing the total plant costs subject to the overall plant safety goal constraints. Reliability allocation was applied to determine the reliability characteristics of reactor systems, subsystems, major components and plant procedures that are consistent with a set of top-level performance goals; the core melt frequency, acute fatalities and latent fatalities. Reliability allocation can be performed to improve the design, operation and safety of new and/or existing nuclear power plants. Reliability allocation is a kind of a difficult multi-objective optimization problem as well as a global optimization problem. The genetic algorithm, known as one of the most powerful tools for most optimization problems, is applied to the reliability allocation problem of a typical pressurized water reactor in this article. One of the main problems of reliability allocation is defining realistic objective functions. Hence, in order to optimize the reliability of the system, the cost for improving and/or degrading the reliability of the system should be included in the reliability allocation process. We used techniques derived from the value impact analysis to define the realistic objective function in this article.     

Tabu search for the redundancy allocation problem of homogenous series–parallel multi-state systems

This paper develops an efficient tabu search (TS) heuristic to solve the redundancy allocation problem for multi-state series–parallel systems. The system has a range of performance levels from perfect functioning to complete failure. Identical redundant elements are included in order to achieve a desirable level of availability. The elements of the system are characterized by their cost, performance and availability. These elements are chosen from a list of products available in the market. System availability is defined as the ability to satisfy consumer demand, which is represented as a piecewise cumulative load curve. A universal generating function technique is applied to evaluate system availability. The proposed TS heuristic determines the minimal cost system configuration under availability constraints. An originality of our approach is that it proceeds by dividing the search space into a set of disjoint subsets, and then by applying TS to each subset. The design problem, solved in this study, has been previously analyzed using genetic algorithms (GAs). Numerical results for the test problems from previous research are reported, and larger test problems are randomly generated. Comparisons show that the proposed TS out-performs GA solutions, in terms of both the solution quality and the execution time.     

Bi criteria flexible assembly line design problem with equipment decisions

In this paper, we address the assembly line balancing and design problem of assigning tasks and equipment to work stations where there are several equipment alternatives for each task. We consider minimizing the total equipment cost and the number of work stations criteria. We aim to generate efficient solutions with respect to these criteria and propose a branch and bound algorithm whose efficiency is enhanced with powerful reduction and bounding mechanisms. We find that our algorithm is capable of solving problem instances with up to 25 tasks and five pieces of equipment.     

A SIL quantification approach based on an operating situation model for safety evaluation in complex guided transportation systems

Safety analysis in guided transportation systems is essential to avoid rare but potentially catastrophic accidents. This article presents a quantitative probabilistic model that integrates Safety Integrity Levels (SIL) for evaluating the safety of such systems. The standardized SIL indicator allows the safety requirements of each safety subsystem, function and/or piece of equipment to be specified, making SILs pivotal parameters in safety evaluation. However, different interpretations of SIL exist, and faced with the complexity of guided transportation systems, the current SIL allocation methods are inadequate for the task of safety assessment. To remedy these problems, the model developed in this paper seeks to verify, during the design phase of guided transportation system, whether or not the safety specifications established by the transport authorities allow the overall safety target to be attained (i.e., if the SIL allocated to the different safety functions are sufficient to ensure the required level of safety). To meet this objective, the model is based both on the operating situation concept and on Monte Carlo simulation. The former allows safety systems to be formalized and their dynamics to be analyzed in order to show the evolution of the system in time and space, and the latter make it possible to perform probabilistic calculations based on the scenario structure obtained.     

A heuristic for solving the redundancy allocation problem for multi-state series-parallel systems

The redundancy allocation problem is formulated with the objective of minimizing design cost, when the system exhibits a multi-state reliability behavior, given system-level performance constraints. When the multi-state nature of the system is considered, traditional solution methodologies are no longer valid. This study considers a multi-state series-parallel system (MSPS) with capacitated binary components that can provide different multi-state system performance levels. The different demand levels, which must be supplied during the system-operating period, result in the multi-state nature of the system. The new solution methodology offers several distinct benefits compared to traditional formulations of the MSPS redundancy allocation problem. For some systems, recognizing that different component versions yield different system performance is critical so that the overall system reliability estimation and associated design models the true system reliability behavior more realistically. The MSPS design problem, solved in this study, has been previously analyzed using genetic algorithms (GAs) and the universal generating function. The specific problem being addressed is one where there are multiple component choices, but once a component selection is made, only the same component type can be used to provide redundancy. This is the first time that the MSPS design problem has been addressed without using GAs. The heuristic offers more efficient and straightforward analyses. Solutions to three different problem types are obtained illustrating the simplicity and ease of application of the heuristic without compromising the intended optimization needs.     

Test interval optimization of safety systems of nuclear power plant using fuzzy-genetic approach

Probabilistic safety assessment (PSA) is the most effective and efficient tool for safety and risk management in nuclear power plants (NPP). PSA studies not only evaluate risk/safety of systems but also their results are very useful in safe, economical and effective design and operation of NPPs. The latter application is popularly known as “Risk-Informed Decision Making”. Evaluation of technical specifications is one such important application of Risk-Informed decision making. Deciding test interval (TI), one of the important technical specifications, with the given resources and risk effectiveness is an optimization problem. Uncertainty is inherently present in the availability parameters such as failure rate and repair time due to the limitation in assessing these parameters precisely. This paper presents a solution to test interval optimization problem with uncertain parameters in the model with fuzzy-genetic approach along with a case of application from a safety system of Indian pressurized heavy water reactor (PHWR).     

Probabilistic safety assessment in Canada

The original basis for licensing power reactors in Canada was probabilistic, from which deterministic requirements were derived and developed. The AECB currently regulates in part by imposing unavailability targets on four special safety systems. In recent years, a number of Probabilistic Safety Assessment (PSA) studies have been performed in Canada, although a PSA is not yet formally required yet in licensing. These PSAs are used to derive the fault tree models for special safety systems, to show that their reliability targets are met. A policy and regulatory guides for using PSA in licensing are currently being developed. This article describes the evolution of safety objectives, the current status of PSA, future applications of PSA, and the regulatory aspects of PSA in Canada.     

Design of safety-critical systems using the complementarities of success and failure domains with a case study

A safety-critical system has to qualify the performance-related requirements and the safety-related requirements simultaneously. Conceptually, design processes should consider both of them simultaneously but the practices do not and/or cannot follow such a theoretical approach due to the limitation of design resources. From our experience, we found that safety-related functions must be simultaneously resolved with the development of performance-related functions, particularly, in case of safety-critical systems. Since, success and failure domain analyses are essential for the investigation of performance-related and safety-related requirements, respectively, we articulated our perception to Axiomatic Design (AD), Fault Tree Analysis (FTA), and TRIZ. A design evolution procedure considering feedbacks from AD to identify functional couplings, TRIZ methodology to explore uncoupling solutions and FTA to improve reliability in a systematic way is presented here. A case study regarding design of safety injection tank installed in a nuclear power plant is also included to illustrate the proposed framework. It is expected that several iterations between AD-TRIZ-FTA would result into an optimized design which could be tested against the desired performance and safety criteria.     

A multicriteria approach to timeout collaboration protocol

This paper presents a multicriteria approach to exploring the properties of timeout collaboration protocol with different timeout thresholds in general testing environments. This is formulated as a discrete multiple criteria optimisation problem by choosing five representative timeout thresholds as alternatives with five common performance measures of production systems. The PROMETHEE method is adopted to deal with this multicriteria problem. The divide-and-label algorithm is developed to rank all the alternatives with the overall intensity of their performance, by using multiple valued outranking graphs from the PROMETHEE with multiple replications. It is shown that two extreme timeout thresholds, T ₀ = 0 and ∞, are efficient over multiple criteria in almost all cases. The divide-and-label algorithm is a very efficient approach to overcome the limitations of the PROMETHEE algorithm and Belz and Mertens's procedure with multiple criteria and replications.     

An interactive memetic algorithm for production and manufacturing problems modelled as a multi-objective travelling salesman problem

In this paper, a preference-based, interactive memetic random-key genetic algorithm (PIMRKGA) is developed and used to find (weakly) Pareto optimal solutions to manufacturing and production problems that can be modelled as a symmetric multi-objective travelling salesman problem. Since there are a large number of solutions to these kinds of problems, to reduce the computational effort and to provide more desirable and meaningful solutions to the decision maker, this research focuses on using interactive input from the user to explore the most desirable parts of the efficient frontier instead of trying to reproduce the entire frontier. Here, users define their preferences by selecting among five classes of objective functions and by specifying weighting coefficients, bounds, and optional upper bounds on indifference tradeoffs. This structure is married with the memetic algorithm – a random-key genetic algorithm hybridised by local search. The resulting methodology is an iterative process that continues until the decision maker is satisfied with the solution. The paper concludes with case studies utilising different scenarios to illustrate possible manufacturing and production related implementations of the methodology.     

Tradeoff analysis of non-linear dynamical systems under stochastic excitation

An efficient methodology to carry out multi-objective optimization of non-linear structural systems under stochastic excitation is presented. Specifically, an efficient determination of particular Pareto or non-inferior solutions is implemented. Pareto solutions are obtained by compromise programming which is based on the minimization of the distance between the point that contains the individual optima of each of the objective functions and the Pareto set. The response of the structural system is characterized in terms of the first two statistical moments of the response process, i.e. the mean and variance. An efficient sensitivity analysis of non-inferior solutions with respect to the design variables becomes possible with the proposed formulation. Such information is used for decision making and tradeoff analysis. The compromise programming problem is solved by an efficient procedure that combines a local statistical linearization approach, modal analysis, global approximation concepts, and a sequential optimization scheme. Numerical results show that the total number of stochastic analyses required during the multi-objective optimization process is in general very small. Hence, different compromise solutions including the design that best represents the outcome that the designer considers potentially satisfactory are obtained in an efficient manner. In this way, the analyst can conduct a decision-making analysis through an efficient interactive procedure.     

Multilevel assessment method for reliable design of composite structures

This work aims at demonstrating the interest of a new methodology for the design and optimization of composite materials and structures. Coupling reliability methods and homogenization techniques allow the consideration of probabilistic design variables at different scales. The main advantage of such an original micromechanics-based approach is to extend the scope of solutions for engineering composite materials to reach or to respect a given reliability level. This approach is illustrated on a civil engineering case including reinforced fiber composites. Modifications of microstructural components properties, manufacturing process, and geometry are investigated to provide new alternatives for design and guidelines for quality control.     

A systems analysis approach to solving office work system health and performance problems

This paper describes a systems analysis approach to human performance in office work systems. The approach, integrating both micro- and macroergonomic aspects, provides a process for more comprehensive, systematic solutions. This systems approach is designed to assess office workers' performance and effectiveness problems within technology intensive office work environments and provide realistic solutions for improving performance. The approach incorporates micro- and macroergonomic factors to adequately address the performance and stress and health-related problems associated with modern office work systems. The seven step approach consists of: defining the problems; setting the objectives and developing alternatives; modelling alternatives; evaluating alternatives; selecting an alternative; planning for implementation; and evaluation, feedback and modification. A detailed schematic presentation of these steps is provided. Solutions or alternatives are proposed to minimize the identified problem factors and to improve performance and the quality of work life.     

An integrated fuzzy multi-criteria group decision-making approach for green supplier evaluation

Purchasing is one of the most vital functions within a company and supplier performance evaluation is one of the most important business processes of the purchasing function. Traditionally, companies have considered factors such as price, quality, flexibility, etc. while evaluating suppliers. However, environmental pressures urge them to consider green issues. This study proposes a decision model for supplier performance evaluation by considering various environmental performance criteria. An integrated, fuzzy group decision-making approach is adopted to evaluate green supplier alternatives. More precisely, a fuzzy analytic hierarchy process (AHP) is applied to determine the relative weights of the evaluation criteria and an axiomatic design (AD)-based fuzzy group decision-making approach is applied to rank the green suppliers. Finally, a case study is given to demonstrate the potential of the methodology.