Probabilistic Collocation Method for NDE Problems with Uncertain Parameters with Arbitrary Distributions (Preprint)
Abstract
In order to quantify the reliability of NDE systems, large amounts of experiments are performed to develop a probability of detection (POD) curve for the system. These POD studies require a substantial amount of experimentation which can sometimes be cost prohibitive. To expedite the process of developing these curves, highly precise numerical models are used in conjunction with NDE sensors to understand the uncertainties associated with the inspections. Numerical models are also used in stochastic inversion methods such as Bayesian inversion, which provide a means of characterizing system properties with uncertainties. A strong basis has been developed in the modeling and simulation community for deterministic forward models in NDE, but to fully incorporate these models in model-assisted probability of detection (MAPOD) studies or stochastic inversion schemes, the models must be treated in a stochastic sense. A method of taking random inputs to a "black box" forward model and developing the full probability distribution function (PDF) of the response has been proposed.
Document Details
- Document Type
- Technical Report
- Publication Date
- Nov 01, 2011
- Accession Number
- ADA553668
Entities
People
- Jeremy S. Knopp
- Mark P. Blodgett
- Matthew Cherry
Organizations
- Air Force Research Laboratory