Optimal fitting and validation of computer simulated probability of detection curves from ultrasonic inspection

Abstract

In order to verify and ensure the structural integrity of industrial components, probability of detection curves (POD) are often used to quantify the reliability of a particular nondestructive testing (NDT) technique. Given their stochastic nature, POD curves are dependent not only on the physical phenomena that governs the NDT technique but also on other factors, known as uncertainty parameters (UP), which leads to a normally requested 95% confidence level. Therefore, to satisfy a 95% confidence level, it is necessary to gather a large number volume of experimental data, besides a sophisticated control of sizing and location of defects in a test piece, which is very costly. It is already well stablished that Model-Assisted POD (MAPOD) have the potential to reduce those costs by generating data through numerical modelling, leading to a prediction of the POD curve using, many times, computer simulation in the process. This study demonstrates how simulations can be optimized, shedding light on the most significant parameters that result in better agreement between simulated and real POD curves. Further, it validates simulated POD curves using the software CIVA by comparing them to industrial ultrasonic inspections on API 5L X-65 pipes. Finally, using a different subset of experimental data, demonstrates the difficulty on transferring optimized fitting.