The Fingerprints of Resonant Frequency for Atomic Vacancy Defect Identification in Graphene

The identification of atomic vacancy defects in graphene is an important and challenging issue, which involves inhomogeneous spatial randomness and requires high experimental conditions. In this paper, the fingerprints of resonant frequency for atomic vacancy defect identification are provided, based on the database of massive samples. Every possible atomic vacancy defect in the graphene lattice is considered and computed by the finite element model in sequence. Based on the sample database, the histograms of resonant frequency are provided to compare the probability density distributions and interval ranges. Furthermore, the implicit relationship between the locations of the atomic vacancy defects and the resonant frequencies of graphene is established. The fingerprint patterns are depicted by mapping the locations of atomic vacancy defects to the resonant frequency magnitudes. The geometrical characteristics of computed fingerprints are discussed to explore the feasibility of atomic vacancy defects identification. The work in this paper provides meaningful supplementary information for non-destructive defect detection and identification in nanomaterials.

Repeatability Study of Flash-Pulse Thermographic Inspection of CFRP Samples

Thermographic flash-pulse inspection is one of popular methods of non-destructive testing (NDT) of materials. Despite the automation of the NDT methods, most of them are based on visual inspections and results of these inspections are influenced by the skills of operators. The repeatability and reproducibility (R&R) of these inspections are therefore more important compared to exact gauge-type methods. This study was focused on the statistical evaluation of flash pulse inspection. Space hardware representative carbon-fiber composite samples with 50 artificial defects were used as reference samples, which were independently inspected by three operators in two independent runs. A Gage R&R study was performed based on contrast to noise ratio defects identification. It was determined that at certain conditions, a total R&R variability 29% can be achieved, which can be assumed as acceptable for this application.

A Novel Assessment of Delayed Neutron Detector Data in CANDU Reactors

A common opportunity for nuclear power plant operators is ensuring that routinely collected data are fully leveraged. Exploiting data analytics can enable improvements in anomaly detection and condition monitoring by identifying previously unseen data trends and correlations without major financial investment. One such opportunity is in facilitating the detection of fuel defects by augmenting the delayed neutron (DN) monitoring system deployed in the majority of Canada deuterium uranium (CANDU) reactors. In this paper, we demonstrate using archive data that the detection of fuel defects can be accelerated using this system in combination with the use of a deeper historical dataset and the introduction of a smoothing algorithm. The current defect identification process relies on the analysis of data of high variance and is subject to the judgment of a domain expert, resulting in variable defect identification periods. The proposed approaches seek to mitigate this and alleviate the variable identification time. Initial results presented here show that for an initial batch of 30 defects, identification periods can be meaningfully reduced compared to the current process, with defects potentially visible on an average of 11.4 days earlier. By shortening this identification period, fuel containing defects can be scheduled for earlier removal, reducing the risk of statutory shutdown obligations, protecting personnel, and promoting industry best practice. Exploring a historical dataset identifies previously undocumented trends and we discuss the potential to produce correlations with other reactor parameters. The application of this knowledge can lead to opportunities in the use of machine learning algorithms and, ultimately, more accurate predictions.