Dynamic Reliability Modeling of Digital Instrumentation and Control Systems for Nuclear Reactor Probabilistic Risk Assessments

U.S. Nuclear Regulatory Commission / Ohio State University

This project is in support of the upcoming NASA robotic mission to Mars in 2020.  It has the objective of developing the framework and executing the risk assessment for the launch vehicle and spacecraft segments in support of the nuclear launch approval process for the Mars 2020 mission.  In this project, ASCA interacts with organizations that have responsibility for various aspects of the overall risk assessment process.  This team included the Department of Energy, the Jet Propulsion Laboratory, and other NASA contractors.  Into these activities ASCA brings its expertise and databases in the areas of launch and space vehicle reliability, safety and mission assurance analysis.

Development of Tools for Safety Analysis of Control Software in Advanced Reactors

U.S. Nuclear Regulatory Commission 

This project consisted of an in-depth study of the basic features of embedded system and plant process software, including interactions with the hardware systems that it is intended to control and regualate. The project developed tools to analyze and assure the safety and dependability of software used in digital control and digital embedded systems for nuclear reactors. These tools can identity 1) paths through which certain undesirable postulated events may occur in a system and 2) appropriate testing strategies based on the analyses of system functional behavior.

Advanced methods for modeling team effects on control-room operator performance in real time

U.S. Nuclear Regulatory Commission 

The broad technical objective of this research was to develop and demonstrate the use of an integrated model, as well as a set of software tools, that builds on advances in the areas of human reliability analysis, organizational factors, and team dynamics, in order to model and analyze, group decision-making processes that take place in a nuclear power plant under both normal and abnormal operating conditions. The resulting methodology complements current Probabilistic Risk Assessment (PRA) methodologies in accounting for team effects and utilizes influence diagrams to model the interactions among the teams. The possible failure modes, e.g., miscommunication between two teams, are modeled by decision tables. Multiple fault tress can, then, be automatically constructed for failures of interest. The associated minimal cut sets provide useful insights into the group dynamics and its possible impact on accident sequences. The results of this project can be applied in reliability assurance, safety assurance and risk assessment tasks concerning the operation of nuclear plants and systems, with specific regard to the effects of the actions of human operator and teams. This work and resulting products is also highly relevant to other industries in which the reliable integration of human operator and teams intervention with system hardware and software is important.

Evaluation of Work Processes at Nuclear Power Plants

Institute of Nuclear Energy Research (INER), Taiwan

This project provided INER personnel with training and documentation on the analysis of nuclear power plant work processes. Work process analysis encompasses the qualitative and quantitative evaluation of the influence of a set of relevant factors on both the design and the implementation of a given work process. It includes provisions for sensitivity analyses for risk -management and resource-allocation purposes. The workshop consisted of a training course and demonstration/application of the Work Process Analysis Model (WPAM) analytical tool.

U.S. Department of Energy

This research developed an Accident Management Advisor System (AMAS) for nuclear power plants. The AMAS concept was demonstration by developing a logic structure of models and techniques in order to organize existing accident management knowledge bases in a form suited for computer-executable retrieval and inference, and by showing how this structured knowledge base system may be used to assist the accident management activities of plant operators. A suite of engineering workstation software tools, to develop the AMAS inference and devision model to execute them in real time, was developed using probabilistic logic flowgraph and influence diagram technology. The functionality of the AMAS workstation software was demonstrated on a selection of test cases.

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