Probabilistic safety assessment for optimum nuclear power plant life management (PLiM): Theory and application of reliability analysis methods for major power plant components

Woodhead Publishing Ltd, September 2012, Pages: 368

Probabilistic safety assessment methods are used to calculate nuclear power plant durability and resource lifetime. Successful calculation of the reliability and ageing of components is critical for forecasting safety and directing preventative maintenance, and Probabilistic safety assessment for optimum nuclear power plant life management provides a comprehensive review of the theory and application of these methods. Part one of this important book reviews probabilistic methods for predicting the reliability of equipment, whilst the practical applications of probabilistic methods for strength reliability are explored in part two.

Part 1 Probabilistic methods for predicting the reliability of equipment: Terminology, concepts and definitions
- Formal–statistical methods
- Formal–statistical methods
- Physico–statistical approach: Procedures using the defect-free model of structural material
- Physico–statistical approach taking defects into account and using binomial distribution
- Physico-statistical models based on the residual defectiveness of structural materials.
Part 2 Practical application of probabilistic methods for strength reliability: Probabilistic analysis of safety: Increasing the reliability and safety of nuclear power plant components
- Optimisation of non-destructive testing
- Optimisation of hydraulic tests, technical certification and planned-preventative maintenance
- Using probabilistic methods for solving the problem of ensuring leak tightness of heat exchanger pipes of nuclear power plant steam generators.

Dr Gennadij V. Arkadov and Prof. Alexander F. Getman are based at the All-Russia Scientific Research Institute for Nuclear Power Plants Operation (VNIIAES), Russia. Dr Andrei N Rodionov is based at the Institute for Radiological Protection and Nuclear Safety (IRSN), France.

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