During the past 25 years, in the context of probabilistic safety assessment, efforts have been directed towards establishment of comprehensive pipe failure event databases as a foundation for ...exploratory research to better understand how to effectively organize a piping reliability analysis task. The focused pipe failure database development efforts have progressed well with the development of piping reliability analysis frameworks that utilize the full body of service experience data, fracture mechanics analysis insights, expert elicitation results that are rolled into an integrated and risk-informed approach to the estimation of piping reliability parameters with full recognition of the embedded uncertainties. The discussion in this paper builds on a major collection of operating experience data (more than 11,000 pipe failure records) and the associated lessons learned from data analysis and data applications spanning three decades. The piping reliability analysis lessons learned have been obtained from the derivation of pipe leak and rupture frequencies for corrosion resistant piping in a raw water environment, loss-of-coolant-accident frequencies given degradation mitigation, high-energy pipe break analysis, moderate-energy pipe break analysis, and numerous plant-specific applications of a statistical piping reliability model framework. Conclusions are presented regarding the feasibility of determining and incorporating aging effects into probabilistic safety assessment models.
Probabilistic fracture mechanics (PFM) simulates the behavior of cracked structures and propagates uncertainties from input parameters to a failure probability or its uncertain estimate. In nuclear ...technology, this approach supports the assessment of the rupture probability of highly reliable pipes, which is an important parameter for the safety analysis of a nuclear power plant. For the appropriate probabilistic modelling of a structure with consideration of uncertainties, but also for the analysis of PFM application cases, the question arises, which input parameter of a probabilistic model has a higher impact on the estimate of computed failure probability, and which has a minor impact. This question is associated with the sensitivity measures or importance factors of the input parameters and their ranking concerning their influence.
In this paper, six different approaches for the quantification of the sensitivity of parameters PFM evaluations are investigated: the amplification ratio, the direction cosine, the degree of separation, the analysis of the most probable failure point, the separation of uncertainty method, and the simple sample-based sensitivity study. Each method is described, visualized, applied to a common test case, and compared. The application case and the comparison are part of the Coordinated Research Project (CRP), “Methodology for Assessing Pipe Failure Rates in Advanced Water-Cooled Reactors (AWCRs)” by the International Atomic Energy Agency (IAEA), which is dedicated to the development of failure rates of piping in AWCRs. The participants used different PFM computer codes to analyze the test case and individual sensitivity methods to rank the input parameters, which motivated the comprehensive survey.
The predicted parameter ranking of the approaches is consistent between the methods and between different PFM codes, but the approaches differ in the scope and the required effort. A conclusion is drawn and recommendations for the six different approaches are given.
•Sensitivity measures in probabilistic fracture mechanics are compared.•Six parameter ranking methods with different starting points are evaluated.•The proposed sensitivity measures agree in their ranking.•The sensitivity ranking approaches differ in their scope and the required effort.
Estimates of failure rates for nuclear power plant piping systems are important inputs to Probabilistic Risk Assessments (PRA) and risk informed applications of PRA. Such estimates are needed for ...initiating event frequencies for Loss of Coolant Accidents and internal flooding events and for risk informed evaluations of piping system in-service inspection programs. A critical issue in the estimation of these parameters is the treatment of uncertainties, which can exceed an order of magnitude deviation from failure rate point estimates. Sources of uncertainty include failure data reporting issues, scarcity of data, poorly characterized component populations, and uncertainties about the physical characteristics of the failure mechanisms and root causes. A methodology for quantifying these uncertainties using a Bayes' uncertainty analysis method was developed for the EPRI risk informed in-service inspection program and significantly enhanced in subsequent applications. In parallel with these efforts, progress has been made in the development of pipe failure databases that contain the quantity and quality of information needed to support piping system reliability evaluations. Examples are used in this paper to identify technical issues with previous published estimates of pipe failure rates and the numerical impacts of these issues on the pipe failure rates and rupture frequencies are quantified.
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