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DOE OSTI2024

Simulator Data Analysis to Inform Digitalized Environment Impacts on Human Reliability

The U.S. Nuclear Regulatory Commission (NRC) has developed a human reliability analysis (HRA) method, termed the Integrated Human Event Analysis System for Event and Condition Assessment (IDHEAS-ECA), in order to estimate human error probabilities (HEPs) in risk-informed regulatory applications. To update the quantification part of IDHEAS-ECA, the NRC required human performance and error data from fully digitalized main control rooms (MCRs); therefore, it requested that Idaho National Laboratory (INL) revisit previous data collection studies and investigate how the following three factors impact human reliability: self-checking, peer-checking, and automation. The HRA data collection studies revisited were the Human Reliability Data Extraction (HuREX) project, developed by the Korea Atomic Energy Research Institute (KAERI), and the Simplified Human Error Experimental Program (SHEEP), developed by INL. HuREX is a representative HRA data collection study that collects human reliability data from full-scope simulators staffed by licensed operators. SHEEP, on the other hand, has been proposed to complement such full-scope studies by collecting data via simplified simulators staffed by non-licensed student operators. In the HuREX study, KAERI collected HRA data from fully digitalized MCRs for the Advanced Power Reactor (APR)–1400. The SHEEP data were obtained from simplified simulators that partially mimicked the features of digitalized MCRs. The present report mainly discusses how the impacts of the aforementioned three factors on human errors were derived from these two data collection studies.

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DOE OSTI2022

Characterization of Recovery Human Action Mechanisms in Nuclear Power Plants

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DOE OSTI2025

EVALUATION OF HRA METHODOLOGIES FOR APPLICATION IN SDP WORK

This study critically evaluates human reliability analysis (HRA) methodologies applicable to regulatory probabilistic safety assessment (PSA) model, with a particular focus on their role in supporting the significance determination process (SDP) in nuclear safety assessment. Firstly, three widely utilized HRA methods – IDHEAS-ECA, SPAR-H, and ASEP/THERP – were qualitatively and quantitatively assessed. Qualitative assessments were conducted using attributes from the NEA/CSNI/R(2015)1 report, while quantitative evaluations employed regression and correlation analyses to compare predicted human error probabilities (HEPs) against empirical data. Results reveal distinct strengths, for example, IDHEAS-ECA’s robust predictive accuracy and K-HRA’s alignment with operational practices. In addition, dependency analysis and recovery analysis were critically evaluated. For dependency analysis, the methods’ handling of inter-task dependencies and their impact on HEPs were examined, while recovery analysis highlighted strategies for mitigating failure events. Furthermore, strategies were proposed to evaluate performance-shaping factors under conditions of reduced human performance, such as stress, fatigue, or cognitive overload, addressing specific challenges faced in SDP evaluations. Human errors from KINS’s operational performance information system event reports were evaluated as a case study. This study identifies gaps and provides actionable insights to ensure their validity and applicability in SDP HRA applications. This paper is a part of research conducted by KINS, and it should be noted that this result does not represent the regulatory position of KINS.

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DOE OSTI2022

Human Performance Analysis Depending on Operator Expertise (Student vs. Operator) and Simulator Complexity (Rancor Microworld vs. Compact Nuclear Simulator)

Human reliability analysis (HRA) evaluates human errors and provides human error probabilities (HEPs) for application in probabilistic safety assessment (PSA), which is a comprehensive safety assessment method for nuclear power plants (NPPs). Generally, HRA methods estimate HEPs based on human reliability data collected from actual historical measurement, simulator experiments, or expert judgement. Most recent HRA data collection studies focus on collecting data via full-scope main control room (MCR) simulators with actual licensed reactor operators. Contrary to this, Idaho National Laboratory (INL) has adopted a different approach, which attempts to collect HRA data based on experiment using simplified simulators and student participants by following the Simplified Human Error Experimental Program (SHEEP). This approach has a couple of advantages compared to full-scope data collection. Representatively, it has relatively low entry point for collecting HRA data, and secures large sample sizes with reasonable cost and labor. In the previous studies, we developed the SHEEP framework, then verified whether the data collected through the framework could support a representative full-scope data collection study, i.e., the Human Reliability Data Extraction (HuREX) study. Also, we analyzed human performance measurements depending on participant type (i.e., student vs. operator). In this paper, we analyze human performance data collected from an experiment comparing operator expertise and simulator complexity when using the more simplified simulator developed by INL, i.e., Rancor Microworld and the less simplified simulator, i.e., Compact Nuclear Simulator (CNS) developed by Korea Atomic Energy Research Institute (KAERI). Analysis of variance (ANOVA) tests and correlation analysis are used for analyzing the experimental data.

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NASA NTRS2017

And the Humans Save the Day or Maybe They Ruin It: The Importance of Humans in the Loop

Flying a mission in space requires a massive commitment of resources, and without the talent and commitment of the people involved in this effort we would never leave the atmosphere of Earth. When we use the phrase "humans in the loop", it could apply to almost any endeavor since everything starts with humans developing a concept, completing the design process, building or implementing a product and using the product to achieve a goal or purpose. Narrowing the focus to spaceflights, there are a variety of individuals involved throughout the preparations for flight and the flight itself. All of the humans involved add value and support for program success. The purpose of this paper focuses on how a Probabilistic Risk Assessment (PRA) accounts for the human in the loop for potential missions using a technique called Human Reliability Analysis (HRA). Human actions can increase or decrease the overall risk via initiating events or mitigating them, thus removing the human from the loop doesn't always lower the risk.

DeMott, Diana↗

DOE OSTI2023

Simulation-Based Recovery Action Analysis Using the EMRALD Dynamic Risk Assessment Tool

Recovery human action is defined as the action that prevents deviant conditions from producing unwanted effects. Analyzing recovery actions has been a critical part in human reliability analysis (HRA). However, there are a couple of limitations to treating recovery actions using only the current HRA methods available. Representatively, the existing recovery analysis does not specifically consider recovery actions as they have occurred in actual nuclear power plants (NPPs). To handle the challenges in the existing recovery analyses, this study suggests a way to analyze recovery actions under a dynamic HRA method, the Procedure-based Risk Investigation MEthod-Human Reliability Analysis (PRIME-HRA) method. The PRIME-HRA method suggests a way on how to develop dynamic simulation models using dynamic risk assessment tools such as the Event Modeling Risk Assessment Using Linked Diagram (EMRALD) [1] and the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) [2]. EMRALD and HUNTER are the dynamic probabilistic risk assessment and HRA tools developed at Idaho National Laboratory. In this paper, differences on analyzing recovery actions in the Technique for Human Error-Rate Prediction (THERP), the Cause-Based Decision Tree (CBDT) and the Korean Standard HRA (K-HRA) and challenges of these approaches are introduced. How we have developed the PRIME-HRA is also introduced in this paper. Then, the proposed approach to analyzing recovery human actions in dynamic context is partially discussed with an example.

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DOE OSTI2021

Experimental Analysis of the Effects of Simulator Complexity on Human Performance

Human Reliability Analysis predicts accidents caused by human errors and is an important factor in Probabilistic Safety Assessment that comprehensively evaluates the safety of nuclear power plants. This study compares and analyzes the human performance of nuclear power plant operators according to the simulator complexity as part of the HRA data collection support method development project conducted by Idaho National Laboratory. This experiment was conducted by setting two types of simulators and scenarios as independent variables. The data collected by conducting the experiments in two different simulators were analyzed using an analysis of variance test and a correlation analysis, and four human performance charts were derived.

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NASA NTRS2017

Lost in Translation: the Case for Integrated Testing

The building of a spacecraft is complex and often involves multiple suppliers and companies that have their own designs and processes. Standards have been developed across the industries to reduce the chances for critical flight errors at the system level, but the spacecraft is still vulnerable to the introduction of critical errors during integration of these systems. Critical errors can occur at any time during the process and in many cases, human reliability analysis (HRA) identifies human error as a risk driver. Most programs have a test plan in place that is intended to catch these errors, but it is not uncommon for schedule and cost stress to result in less testing than initially planned. Therefore, integrated testing, or "testing as you fly," is essential as a final check on the design and assembly to catch any errors prior to the mission. This presentation will outline the unique benefits of integrated testing by catching critical flight errors that can otherwise go undetected, discuss HRA methods that are used to identify opportunities for human error, lessons learned and challenges over ownership of testing will be discussed.

Young, Aaron↗

DOE OSTI2021

Modeling FLEX Human Actions Using the EMRALD Dynamic Risk Assessment Tool

Most current efforts have modified and applied existing human reliability analysis (HRA) methods to treat human actions related to beyond-design-basis external events in which diverse and flexible coping strategies (FLEX) equipment would be deployed. However, many questions remain regarding the suitability of legacy HRA methods to address FLEX human actions, sparking the need for a new method of reasonably evaluating them. In this context, Idaho National Laboratory (INL) researched a relatively new approach to treating FLEX human actions via Event Modeling Risk Assessment Using Linked Diagrams (EMRALD) software. EMRALD was developed to support the increasing need for dynamic probabilistic risk assessment (PRA) models that can respond to evolving plant conditions during simulations. A couple benefits were identified when analyzing FLEX human actions through this software. In general, it is especially useful for evaluating a strategy’s feasibility, including FLEX human actions that require a relatively long time to perform. In this paper, we suggest how FLEX human actions can be modeled using the EMRALD software. Two different HRA modeling approaches using EMRALD are introduced: (1) procedure-based modeling and (2) PRA/HRA-based modeling. The former approach was introduced in the authors’ previous paper, whereas this paper mainly discusses how the latter approach works for an extended loss of AC power (ELAP) scenario with relevant procedures and PRA models. A hybrid method combining the two modeling approaches is introduced at the paper’s conclusion.

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NASA NTRS2012

Launch and Assembly Reliability Analysis for Human Space Exploration Missions

NASA's future human space exploration strategy includes single and multi-launch missions to various destinations including cis-lunar space, near Earth objects such as asteroids, and ultimately Mars. Each campaign is being defined by Design Reference Missions (DRMs). Many of these missions are complex, requiring multiple launches and assembly of vehicles in orbit. Certain missions also have constrained departure windows to the destination. These factors raise concerns regarding the reliability of launching and assembling all required elements in time to support planned departure. This paper describes an integrated methodology for analyzing launch and assembly reliability in any single DRM or set of DRMs starting with flight hardware manufacturing and ending with final departure to the destination. A discrete event simulation is built for each DRM that includes the pertinent risk factors including, but not limited to: manufacturing completion; ground transportation; ground processing; launch countdown; ascent; rendezvous and docking, assembly, and orbital operations leading up to trans-destination-injection. Each reliability factor can be selectively activated or deactivated so that the most critical risk factors can be identified. This enables NASA to prioritize mitigation actions so as to improve mission success.

Cates, Grant↗

NASA NTRS2014

Human Reliability and the Cost of Doing Business

Most businesses recognize that people will make mistakes and assume errors are just part of the cost of doing business, but does it need to be? Companies with high risk, or major consequences, should consider the effect of human error. In a variety of industries, Human Errors have caused costly failures and workplace injuries. These have included: airline mishaps, medical malpractice, administration of medication and major oil spills have all been blamed on human error. A technique to mitigate or even eliminate some of these costly human errors is the use of Human Reliability Analysis (HRA). Various methodologies are available to perform Human Reliability Assessments that range from identifying the most likely areas for concern to detailed assessments with human error failure probabilities calculated. Which methodology to use would be based on a variety of factors that would include: 1) how people react and act in different industries, and differing expectations based on industries standards, 2) factors that influence how the human errors could occur such as tasks, tools, environment, workplace, support, training and procedure, 3) type and availability of data and 4) how the industry views risk & reliability influences ( types of emergencies, contingencies and routine tasks versus cost based concerns). The Human Reliability Assessments should be the first step to reduce, mitigate or eliminate the costly mistakes or catastrophic failures. Using Human Reliability techniques to identify and classify human error risks allows a company more opportunities to mitigate or eliminate these risks and prevent costly failures.

DeMott, Diana↗

DOE OSTI2024

Analysis of Human Reliability Program Data

Explore the source record for details and available documents.

Chirayath, Sunil↗

DOE OSTI2025

Rancor-HUNTER: Using a Simulator Engine for Realistic Human Performance Modeling of Nuclear Power Operations

The Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) is a software system to simulate human performance in support of human reliability analysis (HRA) in nuclear power plants. This paper summarizes recent work to integrate HUNTER with a plant simulator, namely the Rancor Microworld Simulator. Rancor is an offshoot of earlier work at Idaho National Laboratory (INL) to support plant modernization. The graphical software tools used to mimic digital human-system interface upgrades at INL’s Human Systems Simulation Laboratory were linked to the Rancor Microworld Simulator, an INL-developed simplified plant model. HUNTER becomes a “virtual operator” coupled to the Rancor simulator, thereby allowing a tight coupling between a digital human twin and a digital twin of the plant. Rancor-HUNTER may be run through Monte Carlo iterations across a dynamic range of performance shaping factors, thereby producing distributions of human performance in terms of procedure paths, errors instantiations, and task durations. This paper overviews the various unique features of Rancor-HUNTER and presents an example run of Rancor-HUNTER for a startup scenario.

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DOE OSTI2022

A Framework to Integrate Human Reliability Data Obtained from Different Sources Based on the Complexity Scores of Proceduralized Tasks

For many decades, PSA (Probabilistic Safety Assessment) or PRA (Probabilistic Risk Assessment) techniques have been used to enhance the operational safety of nuclear power plants (NPPs) based on the consideration of potential hazards that could result in an unexpected consequence. As human error is one of the potential hazards, diverse human reliability analysis (HRA) methods have been proposed to provide a systematic way to estimate the likelihood of human errors (i.e., human error probability, HEP) in specific task contexts. Accordingly, it is evident that the quality of HRA results strongly depends on the credibility of HEP estimations. This implies that, in terms of enhancing this credibility, the collection of raw information (HRA data) that is helpful for understating when and why human errors occur is a crucial issue. In order to address this issue, in this study, the feasibility of a framework to integrate HRA data obtained from different sources is investigated based on the complexity of proceduralized tasks.

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DOE OSTI2024

Estimating the Contributions to Human Error Probability from the Convolution of the Distribution of Time Available and Time Required

As part of their duties, Human Reliability Analysis must often evaluate if crews in nuclear power plants (NPPs) can complete tasks associated with a human-failure event within time limits. For example, the time required in NPP scenarios is determined by systematic and structured walkthroughs, feasibility studies, recorded times from training exercises, and interviews with experienced operators and experts. Typically, a point estimate is derived for the estimate (mean, maximum, or 95th percentile of time required). Using point-estimate values can mask the risk associated with variability among crews, plant conditions and set-up, environmental conditions, and other impact factors under which these actions are executed. While point estimates for time required and time available have served the industry well, without considering the uncertainty they could lead to biased understanding about the risk. The Integrated Human Event Analysis System - General Methodology (IDHEAS-G) model (developed by the US Nuclear Regulatory Commission, NRC) for human error probability calculates human error probability by summing two probabilities: insufficient time and cognitive error. As such, the model takes a more holistic approach by considering the full distributions for time required and time available to calculate the human error probability because the time available to complete the task is insufficient. In this study, we expand on the work of the NRC and discuss methods for estimating these time considerations. For example, for the time required, the impact of Performance Influencing Factors (PIFs) on the distribution was divided into impacts that are aleatory in nature, such as crew-to-crew variability, and those that are epistemic (i.e., the PIFs). Starting with the factors that introduce aleatory uncertainty, a first-order distribution was developed from a large set of time required (i.e., NPP task completion times) data for the range of operator actions that occur in the NPP control room under simulated accident conditions. The first-order distribution can then be adjusted to account for epistemic uncertainty using research associated with the impact of applicable PIFs on the time required. We also develop guidance for analysts to address the probability distributions for the time available. The guidance we developed on how to estimate time required and time available distributions is based on the identification of pertinent research and data, data analyses, and expert knowledge elicitation.

human error probability, human performance, time e↗

DOE OSTI2024

Rancor-HUNTER: A Virtual Plant and Operator Environment for Predicting Human Performance

Advances in simulation capabilities to model physical systems have outpaced the development of simulations for humans using those physical systems. There is an argument that the infinite span of potential human behaviors inherently render human modeling more challenging than physical systems. Despite this challenge, the need for modeling humans interacting with these complex systems is paramount. As technologies have improved, many of the failure modes originating from the physical systems have been solved. This means the overall proportion of human errors has increased, such that it is not uncommon to be the primary driver of system failure in modern complex systems. Moreover, technologies such as automated systems may introduce emerging contexts that can cause new, unanticipated modes of human error. Therefore, it is now more important than ever to develop models of human behavior to realize overall system error reductions and achieve established safety margins. To support new and novel concepts of operations for the anticipated wave of advanced nuclear reactor deployments, human factors and human reliability analysis researchers need to develop advanced simulation-based approaches. This talk presents a simulation environment suitable to both collect data and then perform Monte Carlo simulations to evaluate human performance and develop better models of human behavior. Specifically, the Rancor Microworld Simulator models a complex energy production system in a simplified manner. Rancor includes computer-based procedures, which serve as a framework to automatically classify human behaviors without manual, subjective experimenter coding during scenarios. This method supports a detailed level of analysis at the task level. It is feasible for collecting large sample sizes required to develop quantitative modelling elements that have historically challenged traditional full-scope simulator study approaches. Additionally, the other portion of this experimental platform, the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER), is presented to show how the collected data can be used to evaluate novel scenarios based on the contextual factors, or performance shaping factors, derived from Rancor simulations. Rancor-HUNTER is being used to predict operator performance with new procedures, such as results from control room modernization or new-build situations. Rancor-HUNTER is also proving a useful surrogate platform to model human performance for other complex systems.

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DOE OSTI2021

EXTRACTING HUMAN RELIABILITY FINDINGS FROM HUMAN FACTORS STUDIES IN THE HUMAN SYSTEMS SIMULATION LABORATORY

Modernization of U.S. nuclear power plants (NPPs) is widespread, with most plants currently replacing and transitioning equipment, control systems, and human system interfaces (HSI)s from analog to digital displays. This conversion remedies the obsolescence of analog parts along with needs for increased intuitiveness of design, safety, and capabilities. The Human Factors and Reliability team at Idaho National Laboratory (INL) carried out twelve control room modernization studies in the newly designed Human Systems Simulation Laboratory (HSSL) over nine years. The HSSL was constructed as a testbed for evaluating human factors techniques and performance measures, HSI frameworks, and cutting-edge operational concepts in NPPs. Installing a full-scope training simulator enabled direct design and evaluation work on the same instrumentation and control (I&C) and HSIs located at U.S. plants. The subsequent addition of glass top bays afforded crews opportunities to implement operations via the simulator using full-scale representations of their home NPP. Additionally, functional HSI prototypes were created, providing an environment for operator-in-the-loop benchmark studies. The HSSL has assisted in upgrades of six commercial NPP control rooms and served as an invaluable proving ground for new NPP operations technology. Human reliability analysis (HRA) was not originally the focus of the studies; however, data relating to HRA such as type and frequency of human errors can be extracted from the studies. INL is currently extracting data from the HSSL study reports to apprise how information gathered from simulation, HSI, and other related studies can create a broad look across different data sources to help inform HRA methods.

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DOE OSTI2023

An Experimental Investigation of Students’ Learning Effects When Using a Simplified Nuclear Simulator

Securing enough data has been a main challenge in human reliability analysis (HRA). Many researchers and institutes have made a lot of efforts for collecting HRA data to produce reasonable human error probabilities (HEPs) as well as reduce the uncertainty of HRA quantification. Representatively, U.S. Nuclear Regulatory Commission (U.S. NRC), Korea Atomic Energy Research Institute (KAERI) and Idaho National Laboratory (INL) have led lots of empirical research regarding the HRA data collection. The U.S. NRC and KAERI have mainly carried out full-scope simulator research collecting HRA data through experiments using full-scope simulators with actual operators. In contrast, INL has experimentally collected the data using simplified simulators and student operators. INL has proposed the Simplified Human Error Experimental Program (SHEEP) framework to complement full-scope data collection efforts by suggesting a way to infer full-scope data based on experimental data collected from students operating simplified simulators, specifically the Rancor Microworld Simulator (Rancor) and the Compact Nuclear Simulator (CNS). The aim of the SHEEP framework is to lower the entry point for collecting useful HRA data by securing large sample sizes at a reasonable amount of cost and labor while also guaranteeing a high degree of freedom when designing experiments. The authors’ previous research investigated whether data collected from the SHEEP framework could support a representative full-scope study. Besides, human performance differences between professional and student operators when using Rancor and CNS have been analyzed to understand the lack of fidelity of the simplified simulators and student operators within the SHEEP study. As a follow up research, this study experimentally investigates students’ learning effects and the performance trends over a certain period when using Rancor. This study aims to find out 1) how much training or education is required to collect HRA data from non-experts (i.e., students) when using Rancor and 2) how much differences there are in human performance measures between students and professional operators. In this study, a longitudinal experiment is developed. The four experiment trials with two weeks interval are carried out for sixteen undergraduate students majoring nuclear engineering at Chosun University. Totally four scenarios randomly selected from ten Rancor scenarios are used in each experiment trial. Four human performance measurements (i.e., workload, situation awareness, time and error) are considered in the experiment. Lastly, the trend of students’ performance is compared with operator data having been collected from the previous experiment.

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