Dynamic Human Reliability Analysis Using the EMRALD Dynamic Risk Assessment Tool
Not Available
Park, Jooyoung↗
Engineering topics
Boring, Ronald L.
Publications and source records attributed to Boring, Ronald L..
The role of AI in detecting and mitigating human errors in safety-critical industries: A review
For safety-critical industries, human error (HE) presents continual risks to system productivity, reliability and safety. Artificial intelligence (AI) and machine learning (ML) methods have emerged as promising approaches to understand, categorize and mitigate the risk of HE in safety-critical industries. Furthermore, this review offers an examination of the current landscape regarding the utilization of AI/ML with regards to HE in safety-critical industries, categorizing literature into descriptive modeling, predictive modeling, prescriptive modeling, and generative modeling techniques. Additionally, the review aims to provide insights regarding themes in literature, challenges, and future research directions. Findings of the review suggest that AI/ML methods can prove useful in addressing the HE problem across safety-critical industries.
42 ENGINEERING↗
A Tale of Two Simulators—A Comparative Human-in-the-Loop Nuclear Power Plant Operations Study on Thermal Power Dispatch for Hydrogen Production
A study was designed for a reconfigurable, full-scale, full-scope nuclear power plant control room simulator to compare two different thermal power dispatch systems, on separate simulator platforms, demonstrating a TPD concept of operation. A TPD system can provide a desirable alternative revenue source for utilities but requires addressing new and unique operational issues. The selection of representative scenarios and the scenario-based experimental design are presented as key elements to capture evidence for validating the developed TPD concept of operations overcome these operational issues.
Ulrich, Thomas A.↗
Human-Centered and Explainable Artificial Intelligence in Nuclear Operations
Nuclear power plants in the United States are critical to the nation’s energy security, accounting for 20% of all electricity produced for the power grid. As energy needs grow, 100 gigawatts of additional nuclear power will be necessary by 2050, more than double the current capacity. Realizing this target requires cutting-edge technology like artificial intelligence (AI) and machine learning (ML) that can bring about significant increases in the level of automation. Human-centered AI (HCAI) is a combination of human-centered design (human factors, human-in-the-loop, etc.) with AI/ML to help produce an efficient and reliable system with full consideration for human engagement. This paper provides a comprehensive and novel discussion of HCAI considerations in nuclear power, introducing unique applications for the existing fleet as well as new advanced reactor designs. We include real-life use cases of AI applications to work management processes at nuclear power sites and highlight lessons learned for HCAI.
Hall, Anna↗
Visual Style Elements in Human–System Interface Design for Nuclear Power Operations: Does Style Affect Performance and Preference?
As the world increasingly adopts renewable and sustainable energy systems, transitionary solutions include nuclear power, which currently provides 20% of the United States’ electricity and is the largest single source of carbon-free electricity generation. Advanced reactors are a critical component of a carbon-free mixed energy portfolio that require careful design of first-of-a-kind control rooms. The application of Human Factors Engineering (HFE) is essential for scientific and iterative testing of novel human–system interface (HSI) concepts to ensure effective, efficient, and safe plant operations. Microworlds are simulators that use simplified physics models and control systems to distill nuclear power operations into essential functions. HFE scientists used the Rancor Microworld Simulator to obtain preference and performance metrics for novel and traditional static HSI design styles. Participants comprised advanced reactor company employees and nuclear industry consultants. A mixture of quantitative and qualitative data was captured. There was a preference for the basic graphical style that included high contrast and traditional color scheme elements. No single HSI design outperformed the others, and the participants did not perform better using their preferred HSI style. We report this experiment is the first in a series of HFE testing for HSIs in advanced reactor control room development. Clear user preferences emerged for elements within static displays. The cutting-edge neumorphic style was the least preferred. Future directions include tests of dynamic displays. HFE is used in evaluating and designing HSI devices that will improve the efficiency and safety of advanced nuclear power operations.
99 GENERAL AND MISCELLANEOUS↗
Levels of Digitization, Digitalization, and Automation for Advanced Reactors
Much has been written about levels of automation (LOA), but comparatively little has been written about levels of digitization (LODi) and levels of digitalization (LODa). Digitization is a digital representation of analog information and is typical of migration from analog to digital control systems, digitalization involves enhancing the functionality of digital information, and automation changes control from humans to machines. Each of these technology implementations has its own scales, and each forms a viable type of functionality that should be considered not as a continuum toward automation but rather as separate categories of solutions that meet the needs of advanced reactors. In this paper we develop separate LODi, LODa, and LOA scales and demonstrate how conflation of these technologies, using the example of computer-based procedures, can lead to confusion in the design process. With the race to develop advanced reactors, the surest metric of success and safety is proper consideration of the right technology requirements for different control systems.
99 GENERAL AND MISCELLANEOUS↗
Application of Artificial Intelligence in Detection and Mitigation of Human Factor Errors in Nuclear Power Plants: A Review
Human factors and ergonomics have played an essential role in increasing the safety and performance of operators in the nuclear energy industry. In this critical review, we examine how artificial intelligence (AI) technologies can be leveraged to mitigate human errors, thereby improving the safety and performance of operators in nuclear power plants (NPPs). First, we discuss the various causes of human errors in NPPs. Next, we examine the ways in which AI has been introduced to and incorporated into different types of operator support systems to mitigate these human errors. We specifically examine (1) operator support systems, including decision support systems, (2) sensor fault detection systems, (3) operation validation systems, (4) operator monitoring systems, (5) autonomous control systems, (6) predictive maintenance systems, (7) automated text analysis systems, and (8) safety assessment systems. Finally, we provide some of the shortcomings of the existing AI technologies and discuss the challenges still ahead for their further adoption and implementation to provide future research directions.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Lessons Learned from Performing a Human Factors Engineering Validation of an Upgraded Digital Control System in a Nuclear Power Plant Control Room
The United States' fleet of nuclear power plants (NPPs) have exceeded their original 40-year licenses and many are seeking license renewals to continue operations into the future. To support continued operation the Department of Energy's (DOE) Light Water Reactor Sustainability Program (LWRS) provides support for utilities to maintain and upgrade their power production capabilities. Control room modernization is one focus area, which aims to modernization the analog control rooms with digital controls and human-system interfaces (HSIs). The Turbine Control System (TCS) is a non-safety related system undergoing modernization efforts at multiple plants and utilities. Considerable human factors research efforts, comprised of expert reviews and operator in the loop studies were performed to develop human factors compliant and usable TCS. This report documents the final phase of the human factors engineering program used to design and validate the new digital TCS. According to the NUREG-0711 review guide, one of the last steps of human factors engineering program prior to implementation is human factors validation. This report details the method, results and conclusions from the human factors validation study of a collaborating utilities TCS slated to be implemented in one of their plants. One control room team consisting of two control room supervisors and two reactor operators participated in the validation. All team members had participated in the BNP TCS training program foregoing the TCS validation. Performance measures included task performance, situation awareness and workload, expert observation of any negative influences of human-system interface features on control room team performance, and interviews with the participating operators. Performance criteria were based on the performance scores and the topics identified by interviews and expert observations. The results showed that the crew using the new TCS clearly could achieve their overall objectives in the test scenarios with sufficient situation awareness. The new TCS compared to the old system provided clear improvements of the control room work regarding shell and chest warming and regarding valve and other system testing. However, a number of Human Engineering Discrepancies (HEDs) were identified. HEDs were prioritized (level 1, 2 or 3) according to potential safety, plant availability, operability or personnel performance consequences. Since no priority 1 HEDs were identified there is no HFE reason not to continue implementation of the TCS. The TCS upgrade is acceptable if the Priority 2 HEDs are resolved and dispositioned in a satisfactory way. Priority 3 HEDs should be noted and considered for future modifications.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Operator-in-the-Loop Study for a Computerized Operator Support System (COSS) – Cross-System and System-Independent Evaluations
The Computerized Operator Support System (COSS) is an operator assistive technology funded by the U.S. Department of Energy Office of Nuclear Energy and developed at Idaho National Laboratory. The project works in collaboration with an advanced prognostics system called PRO-AID developed at Argonne National Laboratory. The COSS provides a human-machine interface to help operators maintain situation awareness and detect faults earlier than would be possible using conventional control room technologies at nuclear power plants. This report represents a status report outlining third-year efforts to develop and validate the COSS. The COSS has now been implemented as a prototype system, with multiple interaction design concepts on multiple plant systems in the simulator from a commercial nuclear power plant. Two studies involving three licensed reactor crews were conducted to evaluate the COSS. This report captures insights into the development of COSS as well as operator feedback and future development guidance derived from the operator-in-the-loop simulator studies.
99 GENERAL AND MISCELLANEOUS↗
HUMAN FAILURE EVENT DEPENDENCE: WHAT ARE THE LIMITS
Abstract Objectives: This paper discusses the differences between classical human reliability analysis (HRA) dependence and the full spectrum of probabilistic dependence. Positive influence suggests an error increases the likelihood of subsequent errors or success increases the likelihood of subsequent success. Currently the typical method for dependence in HRA implements the Technique for Human Error Rate Prediction (THERP) positive dependence equations. This assumes that the dependence between two human failure events varies at discrete levels between zero and complete dependence (as defined by THERP). Dependence in THERP does not consistently span dependence values between 0 and 1. In contrast, probabilistic dependence employs Bayes Law, and addresses a continuous range of dependence. Methods: Using the laws of probability, complete dependence and maximum positive dependence do not always agree. Maximum dependence is when two events overlap to their fullest amount. Maximum negative dependence is the smallest amount that two events can overlap. When the minimum probability of two events overlapping is less than independence, negative dependence occurs. For example, negative dependence is when an operator fails to actuate Pump A, thereby increasing his or her chance of actuating Pump B. The initial error actually increases the chance of subsequent success. Results: Comparing THERP and probability theory yields different results in certain scenarios; with the latter addressing negative dependence. Given that most human failure events are rare, the minimum overlap is typically 0. And when the second event is smaller than the first event the max dependence is less than 1, as defined by Bayes Law. As such alternative dependence equations are provided along with a look-up table defining the maximum and maximum negative dependence given the probability of two events. Conclusions: THERP dependence has been used ubiquitously for decades, and has provided approximations of the dependencies between two events. Since its inception, computational abilities have increased exponentially, and alternative approaches that follow the laws of probability dependence need to be implemented. These new approaches need to consider negative dependence and identify when THERP output is not appropriate.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
SIMULATED HUMAN ERROR PROBABILITY AND ITS APPLICATION TO DYNAMIC HUMAN FAILURE EVENTS
Abstract Objectives: Human reliability analysis (HRA) methods typically analyze human failure events (HFEs) at the overall task level. For dynamic HRA, it is important to model human activities at the subtask level. There exists a disconnect between dynamic subtask level and static task level that presents issues when modeling dynamic scenarios. For example, the SPAR-H method is typically used to calculate the human error probability (HEP) at the task level. As demonstrated in this paper, quantification in SPAR-H does not translate to the subtask level. Methods: Two different discrete distributions were generated for each SPAR-H Performance Shaping Factor (PSF) to define the frequency of PSF levels. The first distribution was a uniform, or uninformed distribution that assumed the frequency of each PSF level was equally likely. The second non-continuous distribution took the frequency of PSF level as identified from an assessment of the HERA database. These two different approaches were created to identify the resulting distribution of the HEP. The resulting HEP that appears closer to the known distribution, a log-normal centered on 1E-3, is the more desirable. Each approach then has median, average and maximum HFE calculations applied. To calculate these three values, three events, A, B and C are generated from the PSF level frequencies comprised of subtasks. The median HFE selects the median PSF level from each PSF and calculates HEP. The average HFE takes the mean PSF level, and the maximum takes the maximum PSF level. The same data set of subtask HEPs yields starkly different HEPs when aggregated to the HFE level in SPAR-H. Results: Assuming that each PSF level in each HFE is equally likely creates an unrealistic distribution of the HEP that is centered at 1. Next the observed frequency of PSF levels was applied with the resulting HEP behaving log-normally with a majority of the values under 2.5% HEP. The median, average and maximum HFE calculations did yield different answers for the HFE. The HFE maximum grossly over estimates the HFE, while the HFE distribution occurs less than HFE median, and greater than HFE average. Conclusions: Dynamic task modeling can be perused through the framework of SPAR-H. Identification of distributions associated with each PSF needs to be defined, and may change depending upon the scenario. However it is very unlikely that each PSF level is equally likely as the resulting HEP distribution is strongly centered at 100%, which is unrealistic. Other distributions may need to be identified for PSFs, to facilitate the transition to dynamic task modeling. Additionally discrete distributions need to be exchanged for continuous so that simulations for the HFE can further advance. This paper provides a method to explore dynamic subtask to task translation and provides examples of the process using the SPAR-H method.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Using the Human Systems Simulation Laboratory at Idaho National Laboratory for Safety Focused Research
Under the United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program, researchers at Idaho National Laboratory (INL) have been using the Human Systems Simulation Laboratory (HSSL) to conduct critical safety focused Human Factors research and development (R&D) for the nuclear industry. The LWRS program has the overall objective to develop the scientific basis to extend existing nuclear power plant (NPP) operating life beyond the current 60-year licensing period and to ensure their long-term reliability, productivity, safety, and security. One focus area for LWRS is the NPP main control room (MCR), because many of the instrumentation and control (I&C) system technologies installed in the MCR, while highly reliable and safe, are now difficult to replace and are therefore limiting the operating life of the NPP. This paper describes how INL researchers use the HSSL to conduct Human Factors R&D on modernizing or upgrading these I&C systems in a step-wise manner, and how the HSSL has addressed a significant gap in how to upgrade systems and technologies that are built to last, and therefore require careful integration of analog and new advanced digital technologies.
99 GENERAL AND MISCELLANEOUS↗
A Tool for Assessing the Text Legibility of Digital Human Machine Interfaces
A tool intended to aid qualified professionals in the assessment of the legibility of text presented on a digital display is described. The assessment of legibility is primarily for the purposes of designing and analyzing human machine interfaces in accordance with NUREG-0700 and MIL-STD 1472G. The tool addresses shortcomings of existing guidelines by providing more accurate metrics of text legibility with greater sensitivity to design alternatives.
human factors↗
Control Board Digital Interface Input Devices – Touchscreen, Trackpad, or Mouse?
The authors collaborated with a power utility to evaluate input devices for use in the human system interface (HSI) for a new digital Turbine Control System (TCS) at a nuclear power plant (NPP) undergoing a TCS upgrade. A standalone dynamic software simulation of the new digital TCS and a mobile kiosk were developed to conduct an input device study to evaluate operator preference and input device effectiveness. The TCS software presented the anticipated HSI for the TCS and mimicked (i.e., simulated) the turbine systems’ responses to operator commands. Twenty-four licensed operators from the two nuclear power units participated in the study. Three input devices were tested: a trackpad, mouse, and touchscreen. The subjective feedback from the survey indicates the operators preferred the touchscreen interface. The operators subjectively rated the touchscreen as the fastest and most comfortable input device given the range of tasks they performed during the study, but also noted a lack of accuracy for selecting small targets. The empirical data suggest the mouse input device provides the most consistent performance for screen navigation and manipulating on screen controls. The trackpad input device was both empirically and subjectively found to be the least effective and least desired input device.
control room modernization↗
The Development of Dynamic Human Reliability Analysis Simulations for Inclusion in Risk Informed Safety Margin Characterization Frameworks
The United States Department of Energy is sponsoring the Light Water Reactor Sustainability program, which has the overall objective of supporting the near-term and the extended operation of commercial nuclear power plants. One key research and development (R&D) area in this program is the Risk-Informed Safety Margin Characterization pathway, which combines probabilistic risk simulation with thermohydraulic simulation codes to define and manage safety margins. The R&D efforts to date, however, have not included robust simulations of human operators, and how the reliability of human performance or lack thereof (i.e., human errors) can affect risk-margins and plant performance. This paper describes current and planned research efforts to address the absence of robust human reliability simulations and thereby increase the fidelity of simulated accident scenarios.
dynamic HRA↗
A COMPUTERIZED OPERATOR SUPPORT SYSTEM PROTOTYPE
A computerized operator support system (COSS) is proposed for use in nuclear power plants to assist control room operators in addressing time-critical plant upsets. A COSS is a collection of technologies to assist operators in monitoring overall plant performance and making timely, informed decisions on appropriate control actions for the projected plant condition. A prototype COSS was developed in order to demonstrate the concept and provide a test bed for further research. The prototype is based on four underlying elements consisting of a digital alarm system, computer-based procedures, piping and instrumentation diagram system representations, and a recommender module for mitigation actions. The initial version of the prototype is now operational at the Idaho National Laboratory using the Human System Simulation Laboratory.
99 GENERAL AND MISCELLANEOUS↗
EARLY-STAGE DESIGN AND EVALUATION FOR NUCLEAR POWER PLANT CONTROL ROOM UPGRADES
As control rooms are modernized with new digital systems at nuclear power plants, it is necessary to evaluate operator performance with these systems as part of a verification and validation process. While there is regulatory and industry guidance for some modernization activities, there are no well defined standard processes or predefined metrics available for assessing what is satisfactory operator interaction with new systems, especially during the early design stages. This paper proposes a framework defining the design process and metrics for evaluating human system interfaces as part of control room modernization. The process and metrics are generalizable to other applications and serve as a guiding template for utilities undertaking their own control room modernization activities.
99 GENERAL AND MISCELLANEOUS↗