Mathematical Models for Performing Safety, Reliability, and Human Error Analysis in Nuclear Power Plants

2017 ◽  
pp. 193-218
Author(s):  
B.S. Dhillon
Keyword(s):  
Error Analysis ◽  
Mathematical Models ◽  
Nuclear Power ◽  
Power Plants ◽  
Human Error ◽  
Nuclear Power Plants

Use of Influence Diagrams and Fuzzy Theory to Develop Assessment Method of Organizational Influences on Component Maintenance

2002 ◽  
Author(s):  
Yoonik Kim ◽  
Kwang-Won Ahn ◽  
Chang-Hyun Chung ◽  
Kil Yoo Kim ◽  
Joon-Eon Yang
Keyword(s):  
Nuclear Power ◽  
Power Plants ◽  
Human Error ◽  
Nuclear Power Plants ◽  
Fuzzy Theory ◽  
Organizational Factors ◽  
Assessment Method ◽  
Influence Diagrams ◽  
Safety Attitudes ◽  
Organizational Influences

Organization can make influences on all the systems. Especially in case of nuclear power plants in which safety is established to be one of the most important operating goals, there have been a lot of research efforts for the hardware advancement. However in recent years, it has been widely recognized that organizational factors in nuclear power plants have an important influence on the safety attitudes and the safe behavior of individuals. Until now, any means to include assessments of organizational structure in probabilistic risk assessments have not been universally accepted. The objective of this work is to develop a method to assess organizational influences on component maintenance. Influence diagrams are introduced in this method as a decision making tool and fuzzy theory is used to reflect the vagueness in considering relevance of human activities in maintenance tasks. Introducing fuzzy theory to assess the organizational factors is deemed to a somewhat new trial, which makes it possible to convert linguistic vague descriptions into mathematical ones. Fuzzy linguistic descriptions offer an alternative and often complementary language to conventional, i.e., analytic approaches to modeling systems. Among the existing methodologies to assess organizational factors, the concept of the ω-factor model is utilized and the mechanism that organizational factors have influences on component maintenance is evaluated through composing influence diagrams. These influences go to failure rates and eventually affect component unavailability. Further study will make it possible that the influences of organizational factors on human error probabilities are incorporated into human reliability analysis and furthermore probabilistic safety assessment.

Dynamical mathematical models of nuclear power plants

Atomic Energy ◽  
2000 ◽  
Vol 88 (6) ◽  
pp. 431-442
Author(s):  
A. A. Abagyan ◽  
A. E. Kroshilin ◽  
V. E. Kroshilin ◽  
V. N. Maidanik ◽  
E. F. Seleznev ◽  
...  
Keyword(s):  
Mathematical Models ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants

Augmenting Probabilistic Risk Assessment With Malevolent Initiators

2011 ◽  
Author(s):  
Curtis Smith ◽  
David Schwieder ◽  
Trond Bjornard
Keyword(s):  
Risk Assessment ◽  
Nuclear Power ◽  
Power Plants ◽  
Human Error ◽  
Nuclear Power Plants ◽  
Probabilistic Risk Assessment ◽  
Vulnerability Assessments ◽  
Equipment Failures ◽  
Random Failures ◽  
Probabilistic Risk

As commonly practiced, the use of probabilistic risk assessment (PRA) in nuclear power plants only considers accident initiators such as natural hazards, equipment failures, and human error. Malevolent initiators are ignored in PRA, but are considered the domain of physical security, which uses vulnerability assessment based on an officially specified threat (design basis threat). This work explores the implications of augmenting and extending existing PRA models by considering new and modified scenarios resulting from malevolent initiators. Teaming the augmented PRA models with conventional vulnerability assessments can cost-effectively enhance security of a nuclear power plant. This methodology is useful for operating plants, as well as in the design of new plants. For the methodology, we have proposed an approach that builds on and extends the practice of PRA for nuclear power plants for security-related issues. Rather than only considering “random” failures, we demonstrated a framework that is able to represent and model malevolent initiating events and associated plant impacts.

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