This report documents a literature survey of so-called "Model Assisted Probability of Detection" (MAPOD) approaches that may be useful in determining the probability of detection (POD), a metric for quantifying the performance of nondestructive evaluation (NDE) methods. The objective of this report is to summarize MAPOD concepts that have been proposed to date in order to assess specific approaches that may be appropriate for application to improve estimates of POD for field NDE of nuclear power plant components. The limitations of laboratory-based studies to replicate actual field conditions are well-recognized and not limited to the nuclear power industry. Probability of detection estimates based on laboratory studies generally provide ideal environments for performing examinations as compared field settings, which typically will result in non-conservative estimates of POD for field applications. As a consequence of this effort, the authors conclude that use of MAPOD concepts to improve estimates of field NDE performance may require access to certain field data, or alternatively, may require significant laboratory studies to assess the influence of human and environmental shaping factors. If the necessary field data cannot be made available, it is proposed that laboratory efforts will have greater chance of success by focusing on a specific examination technique and component application. The authors also find that MAPOD concepts may have a more immediate contribution to the nuclear power industry through their use in extending personnel and procedure qualifications beyond established limits. Finally, another basis was identified that may be used to adjust POD curves generated in previous reliability studies and performance demonstrations. Many of the previous studies calculated POD as the average of performance data which may be inappropriate for many applications, as the calculation may be nonconservative. A more conservative approach to estimating POD would include basing the calculation on a lower statistical quantile of the data set. v

No abstract

No abstract

No abstract

No abstract