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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The first crystal structure of a xylobiose-bound xylobiohydrolase with high functional specificity from the bacterial glycoside hydrolase family 30, subfamily 10

Xylobiose is a prebiotic sugar that has applications in functional foods. This report describes the first X-ray crystallographic structure models of apo and xylobiose-bound forms of a xylobiohydrolase (XBH) from Acetivibrio clariflavus. This xylan-active enzyme, a member of the recently described glycoside hydrolase family 30 (GH30), subfamily 10, phylogenetic clade has been shown to strictly release xylobiose as its primary hydrolysis product. Inspection of the apo structure reveals a glycone region X-2-binding slot. When X-2 binds, the non-reducing xylose in the -2 subsite is highly coordinated with numerous hydrogen bond contacts while contacts in the -1 subsite mostly reflect interactions typical for GH30 and enzymes in clan A of the carbohydrate-active enzymes database (CAZy). This structure provides an explanation for the high functional specificity of this new bacterial GH30 XBH subfamily.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Ion Beam Figuring System for Synchrotron X-Ray Mirrors Achieving Sub-0.2-µrad and Sub-0.5-nm Root Mean Square

Abstract Optics with high-precision height and slope are increasingly desired in numerous industrial fields. For instance, Kirkpatrick–Baez (KB) mirrors play an important role in synchrotron X-ray applications. A KB system is composed of two aspherical, grazing-incidence mirrors used to focus an X-ray beam. The fabrication of KB mirrors is challenging due to the aspherical departure of the mirror surfaces from base geometries and the high-quality requirements for slope and height residuals. In this paper, we present the process of manufacturing an elliptical cylinder KB mirror using our in-house-developed ion beam figuring (IBF) and metrology technologies. First, the key aspects of figuring and finishing processes with IBF are illustrated in detail. The effect of positioning error on the convergence of the residual slope error is highlighted and compensated. Finally, inspection and cross-validation using different metrology instruments are performed and used as the final validation of the mirror. Results confirm that relative to the requested off-axis ellipse, the mirror has achieved 0.15-µrad root mean square (RMS) and 0.36-nm RMS residual slope and height errors, respectively, while maintaining the initial 0.3-nm RMS microroughness.

36 MATERIALS SCIENCE↗
DOE OSTI2024

ROADRUNNER MiniFuel Experiment: Irradiation Target Design and Sample Characterization

High-density uranium nitride (UN) is a fuel candidate for several advanced nuclear reactor designs currently under development. Because there are limited UN performance data relative to fuel fabrication impurity and density variation, an irradiation campaign has been developed as part of a collaborative effort among the University of Texas at San Antonio (UTSA), Westinghouse Electric Company, Oak Ridge National Laboratory (ORNL), and Los Alamos National Laboratory (LANL) under the Nuclear Science User Facilities program. This project, entitled ROADRUNNER, or Research On ADvancing the peRformance of UraNium Nitrides in Extreme enviRonments, aimsto support UN fuel qualification for advanced reactors by investigating the impact of density and impurity variations on UN performance as a function of irradiation temperature and burnup. The MiniFuel experiment vehicle developed by ORNL, which leverages the High Flux Isotope Reactor, was selected to perform this accelerated separate-effects irradiation testing. The experiment test matrix consists of six MiniFuel targets containing miniature UN fuel disks, and targets three distinct burnup levels (37.5, 60, and 75 MWd/kg U) and three distinct temperatures (600, 900, and 1200°C). Neutronics and thermal analyses were performed to determine the experimental parameters needed to meet the desired irradiation conditions and to predict the experiment components temperatures. UN pellets were fabricated at LANL with tightly controlled parameters to produce specimens with three distinct densities and three levels of carbon content. The pellets were then thinned down by UTSA to the experiment-required thickness. The pre-characterization of the specimens includes density measurements, carbon and oxygen contents, microstructure analysis, and x-ray computed tomography. The selected specimens will be assembled into the MiniFuel experiment, and the first ROADRUNNER MiniFuel targets are intended for HFIR insertion during the Fall of 2024. After irradiation, the targets will be shipped to ORNL’s hot cell facility for disassembly. The post-irradiation examination on the fuel specimens includes fission gas release measurements, visual inspection, fuel swelling measurements, gamma spectroscopy, and microstructure analysis. The data collected post-irradiation will be used to develop fuel performance models.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Local structure elucidation of tungsten-substituted vanadium dioxide (V$$_{1-x}$$W$$_x$$O$$_2$$)

Abstract Initially, vanadium dioxide seems to be an ideal first-order phase transition case study due to its deceptively simple structure and composition, but upon closer inspection there are nuances to the driving mechanism of the metal-insulator transition (MIT) that are still unexplained. In this study, a local structure analysis across a bulk powder tungsten-substitution series is utilized to tease out the nuances of this first-order phase transition. A comparison of the average structure to the local structure using synchrotron x-ray diffraction and total scattering pair-distribution function methods, respectively, is discussed as well as comparison to bright field transmission electron microscopy imaging through a similar temperature-series as the local structure characterization. Extended x-ray absorption fine structure fitting of thin film data across the substitution-series is also presented and compared to bulk. Machine learning technique, non-negative matrix factorization, is applied to analyze the total scattering data. The bulk MIT is probed through magnetic susceptibility as well as differential scanning calorimetry. The findings indicate the local transition temperature ( $$T_c$$ T c ) is less than the average $$T_c$$ T c supporting the Peierls-Mott MIT mechanism, and demonstrate that in bulk powder and thin-films, increasing tungsten-substitution instigates local V-oxidation through the phase pathway VO $$_2\, \rightarrow$$ 2 → V $$_6$$ 6 O $$_{13} \, \rightarrow$$ 13 → V $$_2$$ 2 O $$_5$$ 5 .

Wilson, Catrina E. (ORCID:0000000173397318)↗
DOE OSTI2024

DETERMINATION OF STRUT QUALITY FACTORS IN ADDITIVELY MANUFACTURED LATTICES USING IN-SITU COMPRESSION TESTING µ-CT

In response to the need for an automated, commercial method to qualify additively manufactured (AM) lattice components, an experiment was conducted to evaluate the effects of defective lattice struts on the structural compression strength. Lattice samples with known defective or missing struts were compressed using a Deben CT5000RT and imaged using xray µ-CT. The compressive force and x-ray computed tomography results were compared to defect free standards to evaluate the impact of each defect type on the overall structure’s compressive strength. This analysis will allow for simplifications to Finite Element Analysis (FEA) on AM parts without sacrificing model fidelity. Understanding the contribution of each defect type and severity will also better inform non-destructive evaluation (NDE) personnel of the inspection parameters necessary to detect the smallest feature of importance.

Dinova, Vincent A.↗
DOE OSTI2022

Defect and satellite characteristics of additive manufacturing metal powders

Metal additive manufacturing (AM) requires high-quality metal powders to three-dimensionally (3D) print metallic components with complex and customizable geometries. The lack of quantification of AM metal powders creates quality control challenges for 3D printed components, increases the uncertainty of printing reliability and net cost of inspected and certified printed components, and reduces the recyclability of used powders. However, critical characteristics of AM metal powders that are decisive factors for the 3D printing process, such as internal porosity, contamination, and satellite feature, remain ambiguous. In this work, we developed a novel approach to 3D quantify key characteristics of AM metal powders down to individual particles by using high-resolution synchrotron x-ray computed tomography. Empowered by the penetrative capability of high-energy x-ray, internal porosity and contamination within as-atomized metal powders from high-entropy alloys to nickel-based superalloys were evaluated. Additionally, the newly-developed dispersion method enables the homogeneous separation of individual particles, and consequently, results in the implementation of 3D particle shape analysis. To resolve a major challenge of identification and quantification of satellite-feature particles in as-atomized AM metal powders, the satellite features were quantitated by modeling and analyzing the shape parameter of local thickness variance. Furthermore, the 3D analytical methods of particle assessment in this study can be applied to other materials systems like rock, food, and pharmaceutical particles, and provide insights for process optimization across powder metallurgy, concrete, food and pharmaceutical manufacturing, and AM industries.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Core-Level Spectroscopy with Hard and Soft X-rays on Phosphorus-Containing Compounds for Energy Conversion and Storage

The electronic properties of nine solid phosphorus (P)-containing compounds with varying oxidation states and chemical environments, including GaP (–III) , InP (–III) , red-P (0) , H 3 P (III) O 3 , Na 2 H 2 P 2 (IV) O 6 , H 3 P (V) O 4 , KH 2 P (V) O 4 , Na 2 HP (V) O 4 , and InP (V) O 4 , are investigated using X-ray absorption near-edge structure (XANES) spectroscopy in the hard (P K-edge) and soft X-ray regime (P L 2,3 - edge). We find shifts in the absorption-edge positions and correlate them with the ligands surrounding the P atom, likely causing a different core–hole interaction screening for different compounds. Complementing the experimental analysis, ab initio many-body calculations of XANES spectra provide insights into the excitonic nature of the observed spectral features and their impact on the electronic structure of the materials. Furthermore, we report on P K-edge XANES measurements on aqueous phosphorus-containing acids, including H 3 PO 3 , H 3 PO 4 , and their mixtures. At first sight, the spectra of the aqueous acids are similar to those of their solid counterparts. However, close inspection reveals a slight red shift of the absorption edge and the presence of fewer spectral features compared with spectra of the respective solids. Mixtures of aqueous acids display spectral features corresponding to the individual components, indicating the potential for speciation and quantification through fingerprinting.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Passive spiral galaxies deeply captured by Subaru Hyper Suprime-Cam

Abstract This paper presents a thousand passive spiral galaxy samples at z = 0.01–0.3 based on a combined analysis of the Third Public Data Release of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP PDR3) and the GALEX–SDSS–WISE Legacy Catalog (GSWLC-2). Among 54871 gri galaxy cutouts taken from the HSC-SSP PDR3 over 1072 deg2, we conducted a search with deep-learning morphological classification for candidates of passive spirals below the star-forming main sequence derived by ultraviolet to mid-infrared spectral energy distribution fitting in the GSWLC-2. We then classified the candidates into 1100 passive spirals and 1141 secondary samples based on visual inspections. Most of the latter cases are considered to be passive ringed S0 or pseudo-ringed galaxies. The remaining secondary samples have ambiguous morphologies, including two peculiar objects with diamond-shaped stellar wings. The selected passive spirals have a similar distribution to the general quiescent galaxies on the EWHδ–Dn4000 diagram and concentration indices. Moreover, we detected an enhanced passive fraction of spiral galaxies in X-ray clusters. Passive spirals in galaxy clusters are preferentially located in the midterm or late infall phase on the phase–space diagram, supporting the ram pressure scenario, which has been widely advocated in previous studies. The source catalog and gri-composite images are available on the HSC-SSP PDR3 website 〈https://hsc.mtk.nao.ac.jp/ssp/data-release/〉. Future updates, including integration with a citizen science project dedicated to the HSC data, will achieve more effective and comprehensive classifications.

Shimakawa, Rhythm (ORCID:0000000344422750)↗
DOE OSTI2022

MatPhase: Material phase prediction for Li-ion Battery Reconstruction using Hierarchical Curriculum Learning

Li-ion Batteries (LIB), one of the most efficient energy storage devices, are used extensively in many industrial applications. These batteries consist of electrodes that are put together with heterogeneous material compositions. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Such reconstructions of material constituents help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. These images often suffer from low image contrast between multiple material constituents, hence making it difficult for humans to distinguish and characterize these constituents through visual inspection. A minor error in detecting distributions of the material constituents can lead to magnified errors in the calculated parameters of material properties (e.g., porosity). We present MatPhase, a novel hierarchical curriculum learning technique to address the complex task of estimating material constituent distribution in battery electrodes. MatPhase comprises three modules: (i) an uncertainty-aware global model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a local model to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator model to appropriately fuse the local and global effects towards obtaining the final distribution. On average, MatPhase improves prediction up to 8.5% relative to other sophisticated modeling pipelines and state-of-the-arts (SOTA) object detection models employed in the performance comparison.

Tabassum, Anika↗
DOE OSTI2022

Segmentation of tomography datasets using 3D convolutional neural networks

Dendritic microstructures are ubiquitous in nature and are the primary solidification morphologies in metallic materials. Techniques such as X-ray computed tomography (XCT) have provided new insights into dendritic phase transformation phenomena. However, manual identification of dendritic morphologies in microscopy data can be both labor intensive and potentially ambiguous. The analysis of 3D datasets is particularly challenging due to their large sizes (terabytes) and the presence of artifacts scattered within the imaged volumes. Here, in this study, we trained 3D convolutional neural networks (CNNs) to segment 3D datasets. Three CNN architectures were investigated, including a new version of FCDenseNet which we extended to 3D. We show that using hyperparameter optimization (HPO) and fine-tuning techniques, both 2D and 3D CNN architectures outperform the previous state of the art. The 3D U-Net architecture trained in this study produced the best segmentations according to quantitative metrics (intersection-over-union of 95.56% and a boundary displacement error of 0.58 pixels), while 3D FCDense produced the smoothest boundaries and best segmentations according to visual inspection. The trained 3D CNNs are able to segment entire 852 × 852 × 250 voxel 3D volumes in only ~60 s, thus hastening the progress towards a deeper understanding of phase transformation phenomena such as dendritic solidification.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Effect of image resolution on automated classification of chest X-rays

Deep learning (DL) models have received much attention lately for their ability to achieve expert-level performance on the accurate automated analysis of chest X-rays (CXRs). Recently available public CXR datasets include high resolution images, but state-of-the-art models are trained on reduced size images due to limitations on graphics processing unit memory and training time. As computing hardware continues to advance, it has become feasible to train deep convolutional neural networks on high-resolution images without sacrificing detail by downscaling. This study examines the effect of increased resolution on CXR classification performance. We used the publicly available MIMIC-CXR-JPG dataset, comprising 377,110 high resolution CXR images for this study. We applied image downscaling from native resolution to 2048 × 2048 pixels, 1024 × 1024 pixels, 512 × 512 pixels, and 256 × 256 pixels and then we used the DenseNet121 and EfficientNet-B4 DL models to evaluate clinical task performance using these four downscaled image resolutions. We find that while some clinical findings are more reliably labeled using high resolutions, many other findings are actually labeled better using downscaled inputs. We qualitatively verify that tasks requiring a large receptive field are better suited to downscaled low resolution input images, by inspecting effective receptive fields and class activation maps of trained models. Lastly, we show that stacking an ensemble across resolutions outperforms each individual learner at all input resolutions while providing interpretable scale weights, indicating that diverse information is extracted across resolutions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Scalable in situ non-destructive evaluation of additively manufactured components using process monitoring, sensor fusion, and machine learning

Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) is among the metal 3D printing technologies most broadly adopted by the manufacturing industry. However, the current industry qualification paradigm for critical-application L-PBF parts relies heavily on expensive non-destructive inspection techniques, which significantly limits the use-cases of L-PBF. In situ monitoring of the process promises a less expensive alternative to ex situ testing, but existing sensor technologies and data analysis techniques struggle to detect sub-surface flaws (e.g., porosity and cracking) on production-scale L-PBF printers. In this work, an in situ NDE (INDE) system was engineered to detect subsurface flaws detected in X-Ray Computed Tomography (XCT) directly from process monitoring data. A multilayer, multimodal data input allowed the INDE system to detect numerous subsurface flaws in the size range of 200–1000µm using a novel human-in-the-loop annotation procedure. Furthermore, a framework was established for generating probability-of-detection (POD) and probability-of-false-alarm (PFA) curves compliant with NDE standards by systematically comparing instances of detected subsurface flaws to post-build XCT data. Here, we also introduce for the first time in the AM in situ sensing literature the a 90/95 – the flaw size corresponding to a 90% detection rate on the lower 95% confidence interval of the POD curve. The INDE system successfully demonstrated POD capabilities commensurate with traditional NDE methods. Traditional ML performance metrics were also shown to be inadequate for assessing the ability of the INDE system’s flaw detection performance. It is the hope of the authors that future studies will adopt the POD and PFA approach outlined here to provide better insight into the utility of process monitoring for AM.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Deep Learning Based Workflow for Accelerated Industrial X-Ray Computed Tomography

X-ray computed tomography (XCT) is an important tool for high-resolution non-destructive characterization of additively-manufactured metal components. XCT reconstructions of metal components may have beam hardening artifacts such as cupping and streaking which makes reliable detection of flaws and defects challenging. Furthermore, traditional workflows based on using analytic reconstruction algorithms require a large number of projections for accurate characterization - leading to longer measurement times and hindering the adoption of XCT for in-line inspections. In this paper, we introduce a new workflow based on the use of two neural networks to obtain high-quality accelerated reconstructions from sparse-view XCT scans of single material metal parts. The first network, implemented using fully-connected layers, helps reduce the impact of BH in the projection data without the need of any calibration or knowledge of the component material. The second network, a convolutional neural network, maps a low-quality analytic 3D reconstruction to a high-quality reconstruction. Using experimental data, we demonstrate that our method robustly generalizes across several alloys, and for a range of sparsity levels without any need for retraining the networks thereby enabling accurate and fast industrial XCT inspections.

Rahman, Obaid↗
DOE OSTI2025

Characterizing the pore-scale impacts of enhanced-solubilization solutions on organic-liquid morphology and fluid-fluid interfacial area with microtomography

Synchrotron X-ray microtomography was used to directly quantify the impact of enhanced-solubilization solutions on mobilization of a non-wetting fluid in a natural porous medium. Polyoxyethylene sorbitan monooleate (tween 80), hydroxypropyl-β-cyclodextrin (HPCD), sodium dodecyl sulfate (SDS), and ethanol were used as the solubilization reagents. Tetrachloroethene (PCE) was used as the entrapped organic immiscible liquid. Microtomography images were collected prior to and after successive floods with each reagent solution. The total trapping number was varied by more than three orders of magnitude by changing the velocity and viscosity of the aqueous phase as well as the interfacial tension between fluid phases. The results were compared to those obtained from equivalent experiments conducted with water flooding. Inspection of images indicated that mobilization caused a change in PCE morphology and distribution for all reagents with the exception of HPCD, for which a lower trapping number was determined compared to the other experiments. Mobilization of organic liquid was observed at total trapping numbers that were consistent with prior work. The enhanced-solubilization solutions had minimal impact on the configuration or magnitude of fluid-fluid interfacial area.

Interfacial area↗
DOE OSTI2022

Revealing a Hidden Intermediate of Rotatory Catalysis with X-ray Crystallography and Molecular Simulations

The mechanism of rotatory catalysis in ATPhydrolyzing molecular motors remains an unresolved puzzle in biological energy transfer. Notwithstanding the wealth of available biochemical and structural information inferred from years of experiments, knowledge on how the coupling between the chemical and mechanical steps within motors enforces directional rotatory movements remains fragmentary. Even more contentious is to pinpoint the rate-limiting step of a multistep rotation process. Here, using vacuolar or V 1 -type hexameric ATPase as an exemplary rotational motor, we present a model of the complete 4-step conformational cycle involved in rotatory catalysis. First, using Xray crystallography, a new intermediate or “dwell” is identified, which enables the release of an inorganic phosphate (or P i ) after ATP hydrolysis. Using molecular dynamics simulations, this new dwell is placed in a sequence with three other crystal structures to derive a putative cyclic rotation path. Free-energy simulations are employed to estimate the rate of the hexameric protein transformations and delineate allosteric effects that allow new reactant ATP entry only after hydrolysis product exit. An analysis of transfer entropy brings to light how the side-chain-level interactions transcend into larger-scale reorganizations, highlighting the role of the ubiquitous arginine-finger residues in coupling chemical and mechanical information. An inspection of all known rates encompassing the 4-step rotation mechanism implicates the overcoming of the ADP interactions with V 1 -ATPase to be the rate-limiting step of motor action.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Pyridine-containing substrate analogs are restricted from accessing the human cytochrome P450 8B1 active site by tryptophan 281

The human oxysterol 12α-hydroxylase cytochrome P450 8B1 (CYP8B1) is a validated drug target for both type 2 diabetes and nonalcoholic fatty liver disease, but effective selective inhibitors are not yet available. Herein, steroidal substrate-mimicking compounds with a pyridine ring appended to the C12 site of metabolism were designed as inhibitors, synthesized, and evaluated in terms of their functional and structural interactions with CYP8B1. While the pyridine nitrogen was intended to coordinate the CYP8B1 active site heme iron, none of these compounds elicited shifts in the CYP8B1 Soret absorbance consistent with this type of interaction. However, when CYP8B1 was cocrystallized with the pyridine-containing compound with the 3-keto-Δ4 steroid backbone most similar to the endogenous substrate, it was apparent that this ligand was bound in a channel leading to the active site, instead of near the heme iron. Inspection of this structure suggested that tryptophan 281 directly above the heme might restrict active site binding of potential inhibitors with this design. This hypothesis was supported when a CYP8B1 W281F mutation did allow all three compounds to coordinate the heme iron as designed. These results indicated that the design of next-generation CYP8B1 inhibitors should be compatible with the low-ceiling tryptophan immediately above the heme iron.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

In situ laser profilometry for material segmentation and digital reconstruction of a multicomponent additively manufactured part

In addition to its ability to produce geometrically complex parts, additive manufacturing offers a unique opportunity to collect data about a component while it is being fabricated. However, there has only been limited effort to characterize parts morphologically and compositionally in situ. In this article, we present a layer-by-layer, laser profilometry-based in situ characterization technique as a method to digitally reconstruct a multi-material part. Data collected by the laser profilometer yields height maps and grayscale images which are voxelized using purpose-built software to volumetrically reconstruct the part. Additionally, the same part was also analyzed using X-ray computed tomography (CT) which was not able to resolve the different compositional regions within the part, but captured the filament morphology. The part was then bisected to compare the digital reconstruction to the actual part morphology and composition. Overall, the digital reconstruction was in good agreement with both the CT and bisected images. Deviations between the digital reconstruction and the CT/bisected images are likely the result of image segmentation settings or material shifts after data was collected. The in situ characterization method demonstrated here sets the stage for real time process monitoring and paves the way for additively manufactured parts that are “born qualified.”

36 MATERIALS SCIENCE↗
DOE OSTI2024

Momentum informed muon scattering tomography for monitoring spent nuclear fuels in dry storage cask

Development of an effective monitoring method for spent nuclear fuel (SNF) in a dry storage cask (DSC) is important to meet the increasing demand for dry storage investigations. The DSC investigation should provide information about the quantity of stored SNF, and quality assurance of materials should be possible without opening the cask. However, traditional nondestructive examination (NDE) methods such as x-rays are difficult to deploy for DSC investigation because a typical DSC is intentionally designed to shield against radiation. To address this challenge, cosmic ray muons (CRMs) are used as an alternative NDE radiation probe because they can easily penetrate an entire DSC system; however, a wide application of muons is often hindered due to the naturally low CRM flux (~10 4 muons/m 2 /min). This paper introduces a newly proposed imaging algorithm, momentum-informed muon scattering tomography (MMST) , and presents how a limitation of the current muon scattering tomography technique has been addressed by measuring muon momentum. To demonstrate its functionality, a commercial DSC with 24 pressurized light water reactor fuel assemblies (FAs) and the MMST system were designed in GEANT4. Three noticeable improvements were observed for MMST system as a DSC investigation tool: (1) a signal stabilization, (2) an enhanced capability to differentiate various materials, and (3) statistically increased precision to identify and locate missing FAs. The results show that MMST improves the investigation accuracy from 79 to 98% when one FA is missing and 51% to 88% when one-half FA is missing. The advancement of the NDE technique using CRM for DSC verification is expected to resolve long-standing problems in increasing demand for DSC inspections and nuclear security.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗