The Effect of Inspection Uncertainties in Predicting the Probability of Leak of Steam Generator Tubing Due to Pitting

2008 ◽  
Author(s):  
M. D. Pandey ◽  
S. V. Datta ◽  
D. Komljenovic
Keyword(s):  
Steam Generator ◽  
Probability Of Detection ◽  
Likelihood Method ◽  
Probabilistic Framework ◽  
Future Time ◽  
Time Intervals ◽  
Steam Generator Tubing ◽  
Inspection Data ◽  
Biased Estimates ◽  
Incomplete Picture

Pitting corrosion is a serious form of degradation in steam generator (SG) tubing of some nuclear generating stations. The nature and extent of the pitting process is assessed through inspection programs, typically using eddy current probes. Although the data can be used in a probabilistic framework to predict the probability of a tube leak in future time intervals, the consideration of uncertainties associated with inspection tools is extremely important. The paper shows that the defect size data collected through inspections are significantly contaminated by probability of detection and measurement error such that the data present an incomplete picture of the defect population. The use of inspection data without accounting for inspection uncertainties in the analysis results in highly biased estimates of the leak probability. The paper presents a statistically correct likelihood method to account for inspection uncertainties and demonstrates its effectiveness through examples.

A Review of Crack Detection In-Line Inspection Case Studies

2010 ◽  
Author(s):  
Neil Bates ◽  
David Lee ◽  
Clifford Maier
Keyword(s):  
Crack Growth ◽  
Case Studies ◽  
Crack Detection ◽  
Growth Parameters ◽  
Probability Distributions ◽  
Low Frequency ◽  
Probability Of Failure ◽  
Probability Of Detection ◽  
Inspection Data

This paper describes case studies involving crack detection in-line inspections and fitness for service assessments that were performed based on the inspection data. The assessments were used to evaluate the immediate integrity of the pipeline based on the reported features and the long-term integrity of the pipeline based on excavation data and probabilistic SCC and fatigue crack growth simulations. Two different case studies are analyzed, which illustrate how the data from an ultrasonic crack tool inspection was used to assess threats such as low frequency electrical resistance weld seam defects and stress corrosion cracking. Specific issues, such as probability of detection/identification and the length/depth accuracy of the tool, were evaluated to determine the suitability of the tool to accurately classify and size different types of defects. The long term assessment is based on the Monte Carlo method [1], where the material properties, pipeline details, crack growth parameters, and feature dimensions are randomly selected from certain specified probability distributions to determine the probability of failure versus time for the pipeline segment. The distributions of unreported crack-related features from the excavation program are used to distribute unreported features along the pipeline. Simulated crack growth by fatigue, SCC, or a combination of the two is performed until failure by either leak or rupture is predicted. The probability of failure calculation is performed through a number of crack growth simulations for each of the reported and unreported features and tallying their respective remaining lives. The results of the probabilistic analysis were used to determine the most effective and economical means of remediation by identifying areas or crack mechanisms that contribute most to the probability of failure.

scholarly journals Inference of distribution of fitness effects and proportion of adaptive substitutions from polymorphism data

2016 ◽  
Author(s):  
Paula Tataru ◽  
Maéva Mollion ◽  
Sylvain Glemin ◽  
Thomas Bataillon
Keyword(s):  
Molecular Evolution ◽  
Evolutionary Genetics ◽  
Deleterious Mutations ◽  
Probabilistic Framework ◽  
Evolutionary Trajectory ◽  
Beneficial Mutations ◽  
Fitness Effects ◽  
Polymorphism Data ◽  
Inference Methods ◽  
Biased Estimates

ABSTRACTThe distribution of fitness effects (DFE) encompasses deleterious, neutral and beneficial mutations. It conditions the evolutionary trajectory of populations, as well as the rate of adaptive molecular evolution (α). Inference of DFE and α from patterns of polymorphism (SFS) and divergence data has been a longstanding goal of evolutionary genetics. A widespread assumption shared by numerous methods developed so far to infer DFE and α from such data is that beneficial mutations contribute only negligibly to the polymorphism data. Hence, a DFE comprising only deleterious mutations tends to be estimated from SFS data, and α is only predicted by contrasting the SFS with divergence data from an outgroup. Here, we develop a hierarchical probabilistic framework that extends on previous methods and also can infer DFE and α from polymorphism data alone. We use extensive simulations to examine the performance of our method. We show that both a full DFE, comprising both deleterious and beneficial mutations, and α can be inferred without resorting to divergence data. We demonstrate that inference of DFE from polymorphism data alone can in fact provide more reliable estimates, as it does not rely on strong assumptions about a shared DFE between the outgroup and ingroup species used to obtain the SFS and divergence data. We also show that not accounting for the contribution of beneficial mutations to polymorphism data leads to substantially biased estimates of the DFE and α. We illustrate these points using our newly developed framework, while also comparing to one of the most widely used inference methods available.

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