PSAM 16 - Paper Overview

PSAM 16 Conference Paper Overview

Welcome to the PSAM 16 Conference paper and speaker overview page.

Lead Author: Tom Ulrich Co-author(s): Ronald L Boring, ronald.boring@inl.gov Jeeyea Ahn, jeeyea.ahn@inl.gov Yunyeong Heo, yunyeong.heo@inl.gov Jooyoung, Park jooyoung.park@inl.gov Roger Lew, rogerlew@uidaho.edu

The HUNTER Dynamic Human Reliability Analysis Tool: Procedurally Driven Operator Simulation
This paper describes the software implementation of the HUNTER dynamic human reliability analysis (HRA) framework. The HUNTER software tool is a dynamic HRA simulation driven by an operator model defined through existing operating procedures. The software attempts to create a simplified approach to dynamic HRA that does not rely on complex cognitive modeling and therefore provide a more accessible tool for analysts. Traditional HRA models human errors by building a static model of the operators’ activities surrounding the predefined human failure event. Retroactive analysis entails gathering information around a known event, modeling the event, and then extrapolating the failure to other aspects of the system in prospective manner to determine human error opportunities in other parts of the system that could be impacted by the type of modelled event. Historically, this is a largely manual task performed by an analyst or team of analysts and, as a result, traditional HRA often suffers from a level of variability across analyses. Dynamic HRA provides an opportunity to more objectively build models in which the analyst defines the system and constraints. The simulation then uses Monte Carlo simulation to identify human failure events, their probabilities, and the time course of the events. To achieve this, the analyst must define the system and the operators’ activities. Nuclear control room operating procedures are highly prescriptive of operator activities and can serve as a model of potential operator actions. At its core, the HUNTER approach relies on a virtual operator model based on the execution of procedures with dynamically defined contexts representing the HRA aspects of the simulation. The simulation supports configuring both static and dynamic SPAR-H performance shaping factors, based on the nature of each of the individual factors. The likelihood of failure is therefore changing over the course of the simulation based on the operator and the plant state. In addition to the operator context, the human reliability module provides a suite of goals, operators, methods, and selection rules (GOMS) primitives that represent basic units of operator activity. Each primitive has a predefined, SHERPA based human error probability and empirically derived time distributions collected from full-scope simulator experiments. Each procedure step is assigned a primitive, which defines the time taken to execute the step and the likelihood of success to execute the step. Procedures are closed loop representations of plant diagnoses and actions, meaning success achieves the desired goal and failure results in deviating and sometimes looping back on the same set of procedures, which therefore defines a closed simulation environment suitable for handling a broad set of simulation outcomes. As the operator model is executing procedures, a coupled plant model, such as RELAP5, runs in tandem to provide updated plant states based on the scenario’s natural progress due to a given induced transient and any changes due to operator actions. The execution of activities results in the desired plant state or failure to execute appropriate activities, resulting in exceeding a safe operating envelope, which is coded as a failure. The procedure implementation and each of the models described briefly above are conveyed in greater detail in the paper.

The HUNTER Dynamic Human Reliability Analysis Tool: Procedurally Driven Operator Simulation

This paper describes the software implementation of the HUNTER dynamic human reliability analysis (HRA) framework. The HUNTER software tool is a dynamic HRA simulation driven by an operator model defined through existing operating procedures. The software attempts to create a simplified approach to dynamic HRA that does not rely on complex cognitive modeling and therefore provide a more accessible tool for analysts. Traditional HRA models human errors by building a static model of the operators’ activities surrounding the predefined human failure event. Retroactive analysis entails gathering information around a known event, modeling the event, and then extrapolating the failure to other aspects of the system in prospective manner to determine human error opportunities in other parts of the system that could be impacted by the type of modelled event. Historically, this is a largely manual task performed by an analyst or team of analysts and, as a result, traditional HRA often suffers from a level of variability across analyses. Dynamic HRA provides an opportunity to more objectively build models in which the analyst defines the system and constraints. The simulation then uses Monte Carlo simulation to identify human failure events, their probabilities, and the time course of the events. To achieve this, the analyst must define the system and the operators’ activities. Nuclear control room operating procedures are highly prescriptive of operator activities and can serve as a model of potential operator actions. At its core, the HUNTER approach relies on a virtual operator model based on the execution of procedures with dynamically defined contexts representing the HRA aspects of the simulation. The simulation supports configuring both static and dynamic SPAR-H performance shaping factors, based on the nature of each of the individual factors. The likelihood of failure is therefore changing over the course of the simulation based on the operator and the plant state. In addition to the operator context, the human reliability module provides a suite of goals, operators, methods, and selection rules (GOMS) primitives that represent basic units of operator activity. Each primitive has a predefined, SHERPA based human error probability and empirically derived time distributions collected from full-scope simulator experiments. Each procedure step is assigned a primitive, which defines the time taken to execute the step and the likelihood of success to execute the step. Procedures are closed loop representations of plant diagnoses and actions, meaning success achieves the desired goal and failure results in deviating and sometimes looping back on the same set of procedures, which therefore defines a closed simulation environment suitable for handling a broad set of simulation outcomes. As the operator model is executing procedures, a coupled plant model, such as RELAP5, runs in tandem to provide updated plant states based on the scenario’s natural progress due to a given induced transient and any changes due to operator actions. The execution of activities results in the desired plant state or failure to execute appropriate activities, resulting in exceeding a safe operating envelope, which is coded as a failure. The procedure implementation and each of the models described briefly above are conveyed in greater detail in the paper.

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Author and Presentation Info

Lead Author Name: Thomas Ulrich (thomas.ulrich@inl.gov)

Bio: Dr. Ulrich is a human factors and reliability research scientist at the Idaho National Laboratory. He has led and participated in several full-scope, full-scale simulator studies using the Human Systems Simulation Laboratory (HSSL) to investigate a range of nuclear control room topics. Dr. Ulrich possesses expertise in human performance assessment methodology with an emphasis on situation awareness and attention assessment via eye-tracking techniques. He is an expert in simulation and interface prototyping and has helped develop the advanced computerized operator support system (COSS), the Rancor Microworld, the ATRC digital control board, and numerous digital Turbine Control Systems. Dr. Ulrich’s active research includes dynamic human reliability analysis methodology development for nuclear power plant FLEX activities and development of prognostic human-machine interfaces (HMIs) for existing main control rooms and advanced reactor HMIs. Dr. Ulrich has also led an interdisciplinary res

Country: United States of America
Company: Idaho National Laboratory
Job Title: Human Factors and Reliability Research Scientist

PSAM 16 Conference Paper Overview

Welcome to the PSAM 16 Conference paper and speaker overview page.

Paper TH196 Preview

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