PSAM 16 - Paper Overview

PSAM 16 Conference Paper Overview

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

Lead Author: Roger Lew Co-author(s): Ronald L. Boring Thomas A. Ulrich

Applications of the Gamified Rancor Microworld Simulator Model for Dynamic Human Reliability Simulation
Significant effort has been put into the development of high-fidelity thermohydraulic modeling for nuclear power and process control generally. Plants utilize full-scope simulators for engineering and training purposes. While not perfect, they tend to represent the physical configuration and control systems of plants with a high degree of fidelity. The disadvantages of such models are that they are complex, difficult to modify, and difficult to couple with other models. Full-scope simulators are also not optimized for speed, and even with modern computers conducting Monte Carlo simulations with 10s of thousands or 100s of thousands of runs is not logistically feasible. Reduced order models (ROMs) are simplified engineering models that validate particular aspects (e.g. steady state performance) against physical systems or higher fidelity models. They can then be utilized within their validated envelopes for gaining insights into engineered systems. ROMs address the complexity and coupling disadvantages of full-scope models due to their simplified nature. In the human factors domain an analogous problem exists with full-scope simulators. The simulators represent all of the sub-systems and components in physical plants and licensed operators go through years of training to operate these plants competently. Licensed operators are a finite and expensive commodity for laboratory human reliability studies to the extent that obtaining statistically useful error probability rates is not feasible. An alternative approach is to utilize microworld simulators such as DURESS/II or the Rancor Nuclear Power Plant Microworld Simulator. The Rancor Microworld Simulator was jointly developed by Idaho National Laboratory and the University of Idaho to investigate attention and situation awareness with novice operator. It has subsequently been used to design and validate the concept of operations, procedures, and interface design of an integrated energy system for nuclear power. A shortcoming of the Rancor Microworld was that the simulation model was only accessible through a graphical user interface. Here we describe how the simulation model of Rancor has been extracted and made accessible to a variety of platforms and applications by modularizing the model to a .NET core library that can be utilized from .NET compatible environments including Visual Studio and Unity3d. The model has also been ported to Python with the ability to load initial conditions, and trigger faults. Integrating it with the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) will enable dynamic human reliability simulations. The fidelity of the Rancor microworld is limited compared to full-scope simulators or even simplified educational/training simulators. However, the tradeoff is performance, with yet-to-be-optimized code running 100x the speed of full-scope simulators. Furthermore, the simplicity and flexibility of Rancor is favorable to proof-of-concept testing for HUNTER. Dynamic human reliability simulation fundamentally requires a model with a deterministic fault tree, the ability to specify the probability of faults and accept human control actions, and the ability to conduct enough simulation runs to capture the fault tree. The Rancor python model meets these specifications. In this manner the Rancor model can capture theoretical principles of dynamic human reliability analysis (DHRA) ahead of more lengthy and complex integration with higher fidelity models that more precisely capture the temporal and failure dynamics of nuclear power systems.

Applications of the Gamified Rancor Microworld Simulator Model for Dynamic Human Reliability Simulation

Significant effort has been put into the development of high-fidelity thermohydraulic modeling for nuclear power and process control generally. Plants utilize full-scope simulators for engineering and training purposes. While not perfect, they tend to represent the physical configuration and control systems of plants with a high degree of fidelity. The disadvantages of such models are that they are complex, difficult to modify, and difficult to couple with other models. Full-scope simulators are also not optimized for speed, and even with modern computers conducting Monte Carlo simulations with 10s of thousands or 100s of thousands of runs is not logistically feasible. Reduced order models (ROMs) are simplified engineering models that validate particular aspects (e.g. steady state performance) against physical systems or higher fidelity models. They can then be utilized within their validated envelopes for gaining insights into engineered systems. ROMs address the complexity and coupling disadvantages of full-scope models due to their simplified nature. In the human factors domain an analogous problem exists with full-scope simulators. The simulators represent all of the sub-systems and components in physical plants and licensed operators go through years of training to operate these plants competently. Licensed operators are a finite and expensive commodity for laboratory human reliability studies to the extent that obtaining statistically useful error probability rates is not feasible. An alternative approach is to utilize microworld simulators such as DURESS/II or the Rancor Nuclear Power Plant Microworld Simulator. The Rancor Microworld Simulator was jointly developed by Idaho National Laboratory and the University of Idaho to investigate attention and situation awareness with novice operator. It has subsequently been used to design and validate the concept of operations, procedures, and interface design of an integrated energy system for nuclear power. A shortcoming of the Rancor Microworld was that the simulation model was only accessible through a graphical user interface. Here we describe how the simulation model of Rancor has been extracted and made accessible to a variety of platforms and applications by modularizing the model to a .NET core library that can be utilized from .NET compatible environments including Visual Studio and Unity3d. The model has also been ported to Python with the ability to load initial conditions, and trigger faults. Integrating it with the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) will enable dynamic human reliability simulations. The fidelity of the Rancor microworld is limited compared to full-scope simulators or even simplified educational/training simulators. However, the tradeoff is performance, with yet-to-be-optimized code running 100x the speed of full-scope simulators. Furthermore, the simplicity and flexibility of Rancor is favorable to proof-of-concept testing for HUNTER. Dynamic human reliability simulation fundamentally requires a model with a deterministic fault tree, the ability to specify the probability of faults and accept human control actions, and the ability to conduct enough simulation runs to capture the fault tree. The Rancor python model meets these specifications. In this manner the Rancor model can capture theoretical principles of dynamic human reliability analysis (DHRA) ahead of more lengthy and complex integration with higher fidelity models that more precisely capture the temporal and failure dynamics of nuclear power systems.

Paper RO162 Preview

Author and Presentation Info

Lead Author Name: Roger Lew (rogerlew@uidaho.edu)

Bio: Roger is a associate research professor at the University of Idaho and holds a Master's in Human Factors Psychology and a Ph.D. in Neuroscience. His research interests include human factors for nuclear power, transportation safety, visual perception, and decision-support for pre- and post-wildfire modeling.

Country: United States of America
Company: University of Idaho
Job Title: Associate Research Professor

PSAM 16 Conference Paper Overview

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

Paper RO162 Preview

Author and Presentation Info

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