Partial defect detection using the DCVD and a segmented Region-Of-Interest

Abstract

The Digital Cherenkov Viewing Device (DCVD) is a safeguards instrument available to international nuclear safeguards inspectors. It is frequently used to verify fuel on the gross defect level, and approved for partial defect verification, i.e. to assess that parts of a fuel assembly have not been diverted. The current limit for partial defect verification with the DCVD is on the 50% level. In the verification process, an analysis methodology is used where the inspector places a Region-Of-Interest (ROI) around the fuel assembly and assesses the total Cherenkov light intensity within this region. The intensity is then compared to a predicted value, and deviations from the predicted value are used to flag fuel assemblies for further investigations.In this work, we investigate a slightly different analysis approach, where the ROI is split into two or three segments to more accurately capture changes in light intensity in different regions of the captured image. The purpose is to increase the sensitivity of the DCVD to partial defects below the 50% level. Based on simulations of a Pressurised Water Reactor 17x17 fuel assembly, we conclude that a partial defect on the 30% level decreases the Cherenkov light intensity by at least 15% using one single ROI, by at least 20% using a ROI with two segments, and by at least 22% using a ROI with three segments. The analysis approach using two or three ROI segments instead of one thus appears to be more sensitive to partial defects, and can enable more accurate detection of partial defects on the 50% level as well as partial defect detection below the 50% level.Validation of the approach using a limited set of measurement data of intact fuel assemblies supports that detection of light intensity reductions by 20% and 22% is possible, while ensuring that the false positive rate is kept sufficiently low. However, an optimization of ROI segment splits as well as a more extended validation of the approach is required before the method can be considered reliable and applicable to all fuel assemblies that the DCVD can verify today.

Ice accretion thickness prediction using flash infrared thermal imaging and BP neural networks

“…The infrared thermal imaging technology, however, is not affected by the above problems since it uses thermal radiation instead of visible light. In the industrial field, infrared thermal imaging has been applied to defect detection [10,11] and stress inspection [12]. Infrared thermography identifies targets through discrepancies in thermal conductivity characteristics.…”