DYNAMIC BEHAVIOR OF OPERATING CREW IN COMPLEX SYSTEMS: AN OBJECT-BASED MODELING & SIMULATION APPROACH

High-risk environments such as the control room of Nuclear Power Plants are extremely stressful for the front line operators; during accidents and under high task load situations, the operators are solely responsible for the ultimate decision-making and control of such complex systems. Individuals working as a team constantly interact with each other and therefore introduce team related issues such as coordination, supervision and conflict resolution. The aggregate impact of multiple human errors inside communication and coordination loops in a team context can give rise to complex human failure modes and failure mechanisms. This research offers a model of operating crew as an interactive social unit and investigates the dynamic behavior of the team under upset situations through a simulation method. The domain of interest in this work is the class of operating crew environments that are subject to structured and regulated guidelines with formal procedures providing the core of their response to accident conditions. In developing the cognitive models for the operators and teams of operators, their behavior and relations, this research integrates findings from multiple disciplines such as cognitive psychology, human factors, organizational factors, and human reliability. An object-based modeling methodology is applied to represent system elements and different roles and behaviors of the members of the operating team. The proposed team model is an extended version of an existing cognitive model of individual operator behavior known as IDAC (Information, Decision, and Action in Crew context). Scenario generation follows DPRA (Dynamic Probabilistic Risk Assessment) methodologies. The method capabilities are demonstrated through building and simulating a simplified model of a steam/power generating plant. Different configurations of team characteristics and influencing factors have been simulated and compared. The effects of team factors and crew dynamics on system risk with main focus on team errors, associated causes and error management processes and their impact on team performance have been studied through a large number of simulation runs. The results are also compared with several theoretical models and empirical studies

A Predictive Model of Nuclear Power Plant Crew Decision-Making and Performance in a Dynamic Simulation Environment

The safe operation of complex systems such as nuclear power plants requires close coordination between the human operators and plant systems. In order to maintain an adequate level of safety following an accident or other off-normal event, the operators often are called upon to perform complex tasks during dynamic situations with incomplete information. The safety of such complex systems can be greatly improved if the conditions that could lead operators to make poor decisions and commit erroneous actions during these situations can be predicted and mitigated. The primary goal of this research project was the development and validation of a cognitive model capable of simulating nuclear plant operator decision-making during accident conditions. Dynamic probabilistic risk assessment methods can improve the prediction of human error events by providing rich contextual information and an explicit consideration of feedback arising from man-machine interactions. The Accident Dynamics Simulator paired with the Information, Decision, and Action in a Crew context cognitive model (ADS-IDAC) shows promise for predicting situational contexts that might lead to human error events, particularly knowledge driven errors of commission. ADS-IDAC generates a discrete dynamic event tree (DDET) by applying simple branching rules that reflect variations in crew responses to plant events and system status changes. Branches can be generated to simulate slow or fast procedure execution speed, skipping of procedure steps, reliance on memorized information, activation of mental beliefs, variations in control inputs, and equipment failures. Complex operator mental models of plant behavior that guide crew actions can be represented within the ADS-IDAC mental belief framework and used to identify situational contexts that may lead to human error events. This research increased the capabilities of ADS-IDAC in several key areas. The ADS-IDAC computer code was improved to support additional branching events and provide a better representation of the IDAC cognitive model. An operator decision-making engine capable of responding to dynamic changes in situational context was implemented. The IDAC human performance model was fully integrated with a detailed nuclear plant model in order to realistically simulate plant accident scenarios. Finally, the improved ADS-IDAC model was calibrated, validated, and updated using actual nuclear plant crew performance data. This research led to the following general conclusions: (1) A relatively small number of branching rules are capable of efficiently capturing a wide spectrum of crew-to-crew variabilities. (2) Compared to traditional static risk assessment methods, ADS-IDAC can provide a more realistic and integrated assessment of human error events by directly determining the effect of operator behaviors on plant thermal hydraulic parameters. (3) The ADS-IDAC approach provides an efficient framework for capturing actual operator performance data such as timing of operator actions, mental models, and decision-making activities

Use of a big data analysis technique for extracting HRA data from event investigation reports based on the Safety-II concept

The safe operation of complex socio-technical systems including NPPs (Nuclear Power Plants) is a determinant for ensuring their sustainability. From this concern, it should be emphasized that a large portion of safety significant events were directly and/or indirectly caused by human errors. This means that the role of an HRA (Human Reliability Analysis) is critical because one of its applications is to systematically distinguish error-prone tasks triggering safety significant events. To this end, it is very important for HRA practitioners to access diverse HRA data which are helpful for understanding how and why human errors have occurred. In this study, a novel approach is suggested based on the Safety-II concept, which allows us to collect HRA data by considering failure and success cases in parallel. In addition, since huge amount of information can be gathered if the failure and success cases are simultaneously involved, a big data analysis technique called the CART (Classification And Regression Tree) is applied to deal with this problem. As a result, it seems that the novel approach proposed by combining the Safety-II concept with the CART technique is useful because HRA practitioners are able to get HRA data with respect to diverse task contexts

Technological advances, human performance, and the operation of nuclear facilities

2017 Spring.Includes bibliographical references.Many unfortunate and unintended adverse industrial incidents occur across the United States each year, and the nuclear industry is no exception. Depending on their severity, these incidents can be problematic for people, the facilities, and surrounding environments. Human error is a contributing factor in many such incidents. This dissertation first explored the hypothesis that technological changes that affect how operators interact within the systems of the nuclear facilities exacerbate the cost of incidents caused by human error. I conducted a review of nuclear incidents in the United States from 1955 through 2010 that reached Level 3 (serious incident) or higher on the International Nuclear Events Scale (INES). The cost of each incident at facilities that had recently undergone technological changes affecting plant operators' jobs was compared to the cost of events at facilities that had not undergone changes. A t-test determined a statistically significant difference between the two groups, confirming the hypothesis. Next, I conducted a follow-on study to determine the impact of the incorporation of new technologies into nuclear facilities. The data indicated that spending more money on upgrades increased the facility's capacity as well as the number of incidents reported, but the incident severity was minor. Finally, I discuss the impact of human error on plant operations and the impact of evolving technology on the 21st-century operator, proposing a methodology to overcome these challenges by applying the systems engineering process

Dynamic HRA in outage from literature and outage personnel interview perspectives

In 2021, the goal of the SAFIR2022 project NAPRA task T3.2 was to provide an overview of an outage of a nuclear power plant from the perspective of human reliability analysis (HRA). The general features of the outage as well as the specific matters related to human reliability and dynamism in the outage context were studied from literature and outage personnel interview perspectives.The safety-critical nature of an outage is well recognized, and there is a wealth of literature on the specifics of outage and the challenges associated with the successful completion of work. HRA methods have mostly been developed for full power conditions where the operator’s actions are well trained and laid down in procedures, in time frames typically less than 60 minutes. In the planned shutdown the work concentrates outside the control room, is less in procedures and less trained and the time frames may be much longer. The environment is continuously changing, there are huge number of workers, large variety of work activities, tight schedule and the requirements are high concerning both safety and productivity. The key issues that should be considered in the HRA are errors of commission (EOCs), dependencies between human actions and the dynamism of the operating environment.One practical objective of this report was to identify a scenario to focus on in further work related to dynamic modelling. Based on interviews, heavy loads were identified as critical but also mentally and physically loaded. They also include features identified safety critical in scientific literature. This scenario will be studied in more detail in 2022. Work analysis will be performed with special emphasis on applying a combination of methods to elicit the key dynamic features from the HRA perspective

Assessment of the Boiling Water Reactor in Indonesia

Indonesia needs to provide a large enough source of energy for development purposes, not only to produce and distribute daily necessities, but also to build industries that improve the nation's competitiveness and the lives of the people. With so many people, Indonesia has the largest energy consumption of any country in the Southeast Asia region and is fifth in the Asia Pacific in primary energy consumption, after the countries of China, India, Japan and South Korea are expected to increasingly encourage Indonesia's energy needs in the future. In Indonesia the main energy source is the fossil fuel, a non-renewable energy source. With the large amount of energy demands in Indonesia, the use of fossil fuels as energy-generating materials is increasing, which results in the depletion of fuel. Besides that, use of fossil fuels in Indonesia produces carbon dioxide in the wild which can endanger the natural environment. From the problems that occur in Indonesia with the limitations and constraints in conventional energy sources, it has been found that the Nuclear Power Plant (NPP) is a viable alternative for providing electricity. The construction of the NPP would spur national industry development because various industries could be involved in the construction of nuclear power plants. One type of nuclear power plant, namely the Boiling Water Reactor (BWR) has already been implemented in several countries with some success and has advantages that could be applied in Indonesia. It is expected that the presence of nuclear power plants in Indonesia would reduce the use of fossil fuels and increase clean and renewable energy in Indonesia. &nbsp

System dynamics modeling for human performance in nuclear power plant operation

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Nuclear Science and Engineering, 2006.Includes bibliographical references (p. 62).Perfect plant operation with high safety and economic performance is based on both good physical design and successful organization. However, in comparison with the affection that has been paid to technology research, the effort that has been exerted to enhance NPP management and organization, namely human performance, seems pale and insufficient. There is a need to identify and assess aspects of human performance that are predictive of plant safety and performance and to develop models and measures of these performance aspects that can be used for operation policy evaluation, problem diagnosis, and risk-informed regulation. The challenge of this research is that: an NPP is a system that is comprised of human and physics subsystems. Every human department includes different functional workers, supervisors, and managers; while every physical component can be in normal status, failure status, or a being-repaired status. Thus, an NPP's situation can be expressed as a time-dependent function of the interactions among a large number of system elements. The interactions between these components are often non-linear and coupled, sometime there are direct or indirect, negative or positive feedbacks, and hence a small interference input either can be suppressed or can be amplified and may result in a severe accident finally. This research expanded ORSIM (Nuclear Power Plant Operations and Risk Simulator) model, which is a quantitative computer model built by system dynamics methodology, on human reliability aspect and used it to predict the dynamic behavior of NPP human performance, analyze the contribution of a single operation activity to the plant performance under different circumstances, diagnose and prevent fault triggers from the operational point of view, and identify good experience and policies in the operation of NPPs.(cont.) Regarding the human reliability analysis function, the partial Standardized Plant Analysis Risk Human Reliability Analysis (SPAR-H) method was applied. Performance Shaping Factors (PSFs) were employed to analyze the influence of human performance indicators already existing in ORSIM. Based on the human performance model, an operation case study was investigated. A series of carefully chosen candidate policies were tested on a computerized model that represents the structure, processes, and interactions of the underlying target NPP systems. These candidates included: (1) New management system application; (2) Personnel population change, (3) Planning delay, and (4) Tolerance to surprise workload.by Xinyuan Chu.S.M