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South Texas Project uses a large fault tree to produce scenarios (minimal cut sets) used in quantification of plant availability and event frequency predictions. On the other hand, the South Texas Project probabilistic risk assessment model uses a large event tree, small fault tree for quantifying core damage and radioactive release frequency predictions. The South Texas Project is converting its availability and event frequency model to use a large event tree, small fault in an effort to streamline application support and to provide additional detail in results. The availability and event frequency model as well as the applications it supports (maintenance and operational risk management, system engineering health assessment, preventive maintenance optimization, and RIAM) are briefly described. A methodology to perform availability modeling in a large event tree, small fault tree framework is described in detail. How the methodology can be used to support South Texas Project maintenance and operations risk management is described in detail. Differences with other fault tree methods and other recently proposed methods are discussed in detail. While the methods described are novel to the South Texas Project Risk Management program and to large event tree, small fault tree models, concepts in the area of application support and availability modeling have wider applicability to the industry.

The .solution to a Markov chain modeling electric power supply to critical equipment in a typical four-loop pressurized water reactor following a loss of offsite power event is compared with a convolution method. The standard "convolution integral" approach is described, and an alternative methodology based on a Markov model is illustrated.

The solution to a Markov chain modeling electric power supply to critical equipment in a typical 4-loop pressurized water reactor following a Loss of offsite power event is compared with a convolution method. The standard “convolution integral” approach is described, and an alternative methodology based on a Markov model is illustrated.