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At least 127 records · Page 7

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DOE OSTI2025

Irradiation of an enriched uranium (NaCl-UCl 3 ) fuel salt capsule, summary of nondestructive post-irradiation examinations, and solidification modeling

Molten salt reactors (MSRs) are gaining attention due to their potential for safe, carbon-free nuclear energy with reduced waste. However, licensing these reactors is hindered by limited experimental data on fueled salts, both pre- and post-irradiation. Here, the novel Molten-salt Research Temperature-controlled Irradiation (MRTI) vehicle was designed to address knowledge gaps in irradiating enriched‑uranium-bearing salts. The MRTI experiment irradiated 13 cm 3 of UCl₃-NaCl (93 % U-235) salt in the Neutron Radiography (NRAD) Reactor, achieving a burnup of 0.196 GWd/MTU over 390 h. Despite a heater failure, the thermocouple data suggested fission heat kept the salt molten. The MRTI assembly was remotely disassembled for nondestructive post-irradiation examination (PIE), which included precision gamma-ray scanning (PGS) and neutron radiography. Radiograph images showed the location of the salt and the solidification pattern. PGS results provided an early indication that activated materials of construction did not increase in relative intensity in the region of the capsule where the salt was in contact with the material of construction. Additionally, PGS data showed the presence of several gamma emitting fission products, such as Nb-95, Zr-95, Ru-103, Ce-141, and La-140, where Ru-103 had the highest counts at the bottom of the capsule. Computational fluid dynamics modeling supported observations of salt solidification patterns and proved to be a valuable tool to inform PIE activities. The MRTI experiment has thus far provided critical data and lessons learned for fuel salt PIE activities, essential for advancing the technical readiness of MSRs.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Nondestructive In Operando Imaging of Thin Film Composite Membrane Compaction Enhanced by AI-Based Segmentation

Reverse osmosis (RO) membranes are essential for desalination and water reuse, yet their permeability declines in high-pressure applications due to membrane compaction. This study investigates the structural and functional responses of commercial brackish, seawater, and high-pressure RO membranes at applied pressures up to 120 bar using a multiscale, nondestructive in operando scanning electron microscopy (iSEM) imaging platform. The iSEM technique reveals progressive densification across the composite membrane structure, which correlates with observed declines in water and solute permeance. To quantify these structural changes with greater fidelity, we combined X-ray computed tomography with AI-based segmentation enabling precise analysis of pore size distribution and thickness of the polysulfone support layer. Compared to traditional thresholding, AI segmentation accurately delineates material phases and void spaces, enhancing the reproducibility and resolution of morphological assessments. The results demonstrate that compaction-induced reductions in porosity and thickness strongly impact membrane transport properties. These findings provide mechanistic insights into the compaction behavior of RO membranes and underscore the potential for advanced imaging and AI-driven data analysis to guide the design of next-generation membranes with improved mechanical resilience and operational longevity.

13 HYDRO ENERGY↗

DOE OSTI2022

Nondestructive microstructural investigation of defects in 4H-SiC epilayers using a multiscale luminescence analysis approach

The development of metal oxide semiconductor field effect transistors (MOSFETs) utilizing epitaxially grown 4H-SiC has accelerated in recent years due to their favorable properties, including a high breakdown field, high saturated electron drift velocity, and good thermal conductivity. However, extended defects in epitaxial 4H-SiC can affect both device yields and operational lifetime. In this work, we demonstrate the importance of a multiscale luminescence characterization approach to studying nondestructively extended defects in epitaxial 4H-SiC semiconducting materials. Multiscale luminescence analysis reveals different aspects of excess charge carrier recombination behavior based on the scale of a particular measurement. Combining measurements of the same extended defect area at different scales tells us more about the essential nature of that defect and its microstructure. Here, we use photoluminescence imaging and cathodoluminescence spectrum imaging to investigate the recombination behavior of several different types of extended defects, including stacking faults, inclusions, and basal plane dislocations. A detailed understanding of the optoelectronic properties of extended defects in epitaxial SiC helps elucidate the microstructure of extended defects and can provide pathways to mitigate detrimental changes during device operation related to their evolution, such as the recombination enhanced dislocation glide effect that affects SiC-based MOSFETs.

3C-inclusion↗

DOE OSTI2024

Skipper-in-CMOS: Nondestructive Readout With Subelectron Noise Performance for Pixel Detectors

The Skipper-in-CMOS image sensor integrates the nondestructive readout capability of skipper charge coupled devices (Skipper-CCDs) with the high conversion gain of a pinned photodiode (PPD) in a CMOS imaging process while taking advantage of in-pixel signal processing. This allows both single photon counting as well as high frame rate readout through highly parallel processing. The first results obtained from a ${15} \times {15}~\mu $ m2 pixel cell of a Skipper-in-CMOS sensor fabricated in Tower Semiconductor’s commercial 180-nm CMOS image sensor process are presented. Measurements confirm the expected reduction of the readout noise with the number of samples down to deep subelectron noise of $0.15\text {e}^ - $ , demonstrating the charge transfer operation from the PPD and the single photon counting operation when the sensor is exposed to light. This article also discusses new testing strategies employed for its operation and characterization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

A new mechanism for void-cascade interaction from nondestructive depth-resolved atomic-scale measurements of ion irradiation–induced defects in Fe

The nondestructive investigation of single vacancies and vacancy clusters in ion-irradiated samples requires a depth-resolved probe with atomic sensitivity to defects. The recent development of short-pulsed positron beams provides such a probe. Here, we combine depth-resolved Doppler broadening and positron annihilation lifetime spectroscopies to identify vacancy clusters in ion-irradiated Fe and measure their density as a function of depth. Despite large concentrations of dislocations and voids in the pristine samples, positron annihilation measurements uncovered the structure of vacancy clusters and the change in their size and density with irradiation dose. When combined with transmission electron microscopy measurements, the study demonstrates an association between the increase in the density of small vacancy clusters with irradiation and a remarkable reduction in the size of large voids. This, previously unknown, mechanism for the interaction of cascade damage with voids in ion-irradiated materials is a consequence of the high porosity of the initial microstructure.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Seeing through nuclear fuel: Three-dimensional, nondestructive X-ray microscopy and volumetric analyses of neutron-irradiated TRISO-coated fuel kernels

The three-dimensional (3D) characterization of nuclear fuel with X-ray microscopy has historically proven difficult, due to uranium’s high attenuation of easily accessible X-rays, both in a laboratory setting and at a synchrotron user facility. However, this imaging modality provides nondestructive information that can be used to investigate morphological changes arising from external stimuli (e.g., neutron irradiation, high-temperature testing). Using an appropriate X-ray energy spectrum and an adequate X-ray filter, suitable transmissions through properly sized nuclear fuel specimens can be achieved. Here, in this study, we present the methods and results of using a commercially available, laboratory-based X-ray microscope (XRM) to examine the extent of 3D morphological changes of tristructural isotropic (TRISO)-coated fuel particles, specifically uranium oxide/uranium carbide fuel kernels, after high-temperature neutron irradiation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Imaging of Calibrated Defects in Additively Manufactured Materials: Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components

Additive manufacturing (AM, or 3D printing) for commercial nuclear energy applications is an emerging method for cost-efficient manufacturing aimed at replacing aging nuclear reactor parts and reducing costs for new construction. Known material flaws in AM include low-density regions consisting of non-sintered powder, which have to be detected to ensure the safety of long-term performance nuclear reactor components. Currently, limited options are available for nondestructive evaluation (NDE), either during or post manufacturing. As a solution to NDE of AM, we are developing pulsed thermal imaging which is non-contact, one-sided, and scalable to arbitrary size and shape of the AM parts. Pulsed thermography (PT) system utilizes a high intensity flash lamp to rapidly heat surface of sample, and a high sensitivity fast-frame megapixel infrared (IR) camera to capture data of surface temperature variations. The acquired data cube consists of a stack of surface temperature images taken at different times. Information about material internal defects is extracted by analyzing the data cube. This report provides results of preliminary performance evaluation of pulsed thermal imaging capability in detection of imprinted flaws in AM metallic structures. The flaws were introduced into AM parts as imprinted hemispherical low density regions, consisting of trapped un-sintered metallic powder. Specimens for developed for this study consisted of AM stainless steel 316 and Inconel 718 plates. The diameters of imprinted defects varied from 1mm to 8mm, and their depths below the plate flat surface varied between 1mm and 6mm. Pulsed thermal tomography (PTT) processes the measured data cube to obtain 3D reconstructions of material effusivity using a unique inversion algorithm developed at Argonne. PTT has been previously used in imaging of similar size flat bottom hole (FBH) simulated defects in stainless steel 316 and Inconel 718 specimens. In the study involving AM specimens, PTT imaging results have shown that 1mm-diameter defects located 1mm and 2mm below the surface of specimens were detectable. Larger size defects were detectable at greater depth. We also explored an alternative approach to detection of material flaws in PT data cube, which is using neural learning-based approach to blind source separation. Detection of small material defects requires finding features in the data cube which have signal contrast levels approaching sensitivity limit of IR camera. In this study, an optimized Neural Learning based Blind Source Separation (NLBSS) algorithm, including Principal Component Analysis (PCA), and Independent Component Analysis (ICA) is demonstrated to automatically extract principal temporal and spatial features of thermography frames to enhance flaw detection. By using the NLBSS algorithm, material internal defects can be automatically detected. Furthermore, this processing approach compensates for experimental thermal imaging artifacts, such as noise and uneven heating. By merging artificial intelligence with phtotothermics, the NDE system detects internal calibrated defects of various sizes and depths in AM nuclear-grade metallic alloys.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Nondestructive Evaluation of Concrete within Nuclear Applications

Structural defects that develop over the lifetime of nuclear power plants (NPPs) could threaten the safety and security of the concrete infrastructure. Developing a reliable nondestructive evaluation (NDE) method to evaluate the damage in NPPs and their associated structures will greatly improve the long-term operation and safety of NPPs. The purpose of the US Department of Energy Office of Nuclear Energy’s Light Water Reactor Sustainability program is to develop technologies and other solutions that can improve the reliability, sustain the safety, and extend the operating lifetimes of NPPs beyond 60 years. This report describes the progress made toward growing NDE capabilities for NPPs. It also documents expanded collaborations with the Electric Power Research Institute (EPRI) and future opportunities to collaborate with industry partners (i.e., vendors and utilities) and university partners. These collaborations will continue to stand as a framework to provide value to stakeholders by reducing uncertainties and potential delayed maintenance costs in NPPs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Model-Based Ultrasonic Signal Processing for the Nondestructive Evaluation of Additive Manufacturing Components

42 ENGINEERING↗

DOE OSTI2021

Time-of-Flight Estimation for Nondestructive Evaluation

Ultrasonic testing (UT) for nondestructive evaluation (NDE) is a critical entity necessary to resolve both the quality and precision questions of complex parts evolving from the classical approaches to the innovative additive manufacturing (AM) process. This modality provides the essential quantitative information for acceptance and potential flaw detectionof apart under investigation. A primary ingredient in UT besides the required precision robotic hardware for theacquisition of high-qualitymeasurement data is the underlying signal processing. It is here that much of the system performance capability resides. In this report,we discuss the basic steps in UT signal processing along with current capabilities that must be achieved in order to satisfy the criticaldemands of the various LLNL programs.In itsbasic foundations, signal processing is essentially the “extraction of critical information (signals) from noisy, uncertain data.” Clearly, in a perfect world the best signal processing is none---just make a reliable, noise-free, uncertainty-free, measurement. Unfortunately, even the best of systems is still straddled within the confines and limitations of physical instrumentation. With this in mind, we discuss the development of signal processing techniques toextract the desired UT information from noisy measurement data. We start with a discussion of a simple homogeneous representation of a “part” under investigation and its insonification by an ultrasonicexcitation from a physics-based perspective. Once developed, we briefly discussthe various choices of excitation signals and their tradeoffs.Next,we discuss a simulation approach employing simple models from the signal processing perspective and then move on to the development of the basic signal processing approach to ultrasonic signal processing all based on estimating the pulse arrival estimation. Starting with simple peak detection techniques, progressing to the processing workhorse so-called “matched-filter” and finally to the more sophisticated “model-based matched filter.”The underlying pre-and post-processing of noisy measurement data along with the application of these techniques are subsequently demonstrated on experimental ultrasonic data.Finally, we present some results for processing “weld” data when the ultrasonic excitation signal is not directly available.

42 ENGINEERING↗

DOE OSTI2021

Nondestructive Evaluation (NDE) of Cable Moisture Exposure using Frequency Domain Reflectometry (FDR)

This Pacific Northwest National Laboratory (PNNL) milestone report assesses the capability of frequency domain reflectometry (FDR) to determine electrical cable submergence using PNNL’s Accelerated and Real-Time Environmental Nodal Assessment (ARENA) cable/motor test bed. This work includes a review of relevant literature as well as experimental tests. Nuclear power facilities have experienced various electrical cable failures related to water exposure. The current industry response involves actions to de-water cable vaults, manholes, and other cable locations. These efforts require considerable expenditure of resources, which makes it desirable for the industry to have information on cable condition and history regarding their submergence and water exposure (Mantey 2012). Two tests that are gaining favor within the nuclear industry are time-domain reflectometry (TDR) and FDR. These are low-voltage nondestructive tests that can be applied at a cable end. Testing from the cable end is important because local inspection along the cable length is very difficult due to cables being routed within trays, conduits, underground, and through walls. Both TDR and FDR techniques have been shown to locate cable insulation damage due to thermal, radiation, and mechanical damage. FDR measurements are also more sensitive than TDR to temperature changes, low-bend radius bends, and cable contact with various materials, including conductive materials like steel and water. This work evaluates the feasibility to extend FDR testing to characterizing whether an electrical cable is submerged or not and where it may be submerged using PNNL’s ARENA cable/motor test bed.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2021

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components. Third Annual Progress Report

Additive manufacturing (AM) of high-strength corrosion resistance alloys for nuclear energy applications, such as stainless steel and Inconel, is currently based on laser powder bed fusion (LPBF) process. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Probability of crack initiation at the pore depends on size, shape, and orientation of the defect. Pulsed Infrared Thermography Imaging (PIT) provides a capability for non-destructive evaluation (NDE) of sub-surface defects in arbitrary size structures. The PIT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PIT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. Following initial qualification of an AM component for deployment in a nuclear reactor, a PIT system can also be used for in-service nondestructive evaluation (NDE) applications. In this report, we describe recent progress in enhancing PIT capabilities in detecting microscopic subsurface defects in metals, and classifying shapes and orientation of pores in thermal images. For detection of microscopic defects in PIT imaging data, we have developed Spatial Temporal Denoised Thermal Source Separation (STDTSS) unsupervised machine learning (ML) image processing algorithm. We show that flat bottom hole (FBH) defects as small as 200µm in SS316 and IN718 specimens, can be detected with STDTSS algorithm. To the best of our knowledge, these are the smallest detected defects which are reported in literature. For classification of defects shapes, we have previously developed thermal tomography (TT) algorithm to obtain depth reconstructions of material defects from data cube of sequentially recorded surface temperatures. However, interpretation of TT images is non-trivial because of blurring with increasing depth. To address this challenge, we have developed a deep learning convolutional neural network (CNN) to classify size and orientation subsurface defects in simulated TT images.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Assessment of Machine Learning for Ultrasonic Nondestructive Evaluation of Alkali–Silica Reaction in Concrete

Alkali–silica reaction (ASR) is a type of material degradation in concrete structures that leads to concrete cracking and rebar corrosion, thereby reducing the material’s structural integrity and the overall structure’s lifetime and raising safety concerns. Ultrasonic nondestructive evaluation (NDE) has been proven to be a valuable technique for assessing concrete properties and monitoring ASR progression in concrete. However, the deployment and analysis of ultrasonic NDE and its data requires specialized expertise, often relying on the engineer’s subjective interpretation. With the surge in computational power, artificial intelligence (AI) and machine learning (ML) algorithms have become popular in automating NDE data analysis. Various industrial sectors are increasingly adopting ML algorithms for NDE data analysis with a growing emphasis on AI–assisted automation. Regulatory agencies are also preparing for this technological shift, anticipating corresponding revisions in standards. Thus, there is an urgent need to identify the capabilities and limitations of current ML technologies for the evaluation of concrete material properties and damage status. Furthermore, the effects of various factors on ML model performance must be thoroughly investigated. The study summarized herein evaluated the effectiveness of two ML models (i.e., support vector regression (SVR) and deep neural network (DNN)) in predicting concrete material damage induced by ASR based on the long-term ultrasonic monitoring data. Four distinct concrete specimens were cast with artificially induced ASR, and over a period exceeding 500 days, ultrasonic signals and expansion data were continuously collected. For the SVR model, wave velocity and 12 other wave features were extracted from the ultrasonic signals, with 6 out of 13 features selected as input for the model. Different combinations of training and testing datasets were designed to explore factors influencing prediction performance, including the range of data within training and testing sets, in addition to various signal preprocessing methodologies. These findings suggest the importance of using a training dataset with a broader data range compared with testing datasets for improved model performance alongside consistent signal preprocessing across datasets.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Performance Comparison of Machine Learning Models for Ultrasonic Nondestructive Evaluation of Alkali-Silica Reaction in Concrete

Alkali-silica reaction (ASR) causes concrete degradation, leading to cracking, rebar corrosion, and reduced structural integrity, which raises safety concerns. Ultrasonic nondestructive evaluation (NDE) effectively assesses concrete properties and monitors ASR progression. However, its deployment and analysis require specialized expertise and subjective interpretation. As computational power increases, artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being used to automate NDE data analysis across various industries for AI-assisted automation. Regulatory agencies are adapting to this technological shift, prompting a need to evaluate current ML technologies’ capabilities and limitations in assessing concrete material properties and damage. This report presents a comparative analysis of four ML regression models for predicting concrete material damage induced by ASR expansion using long-term ultrasonic data monitoring. The models investigated include linear regression (LR), support vector regression (SVR), shallow neural networks (NN), and deep neural networks (DNN). LR, SVR, and shallow NN models use features extracted from ultrasonic signals, whereas the DNN model processes time-domain ultrasonic signals and frequency spectra directly. The study systematically compared the models’ performance from various perspectives, including model input, prediction performance, and generalization ability. The findings indicate significant variability in model performance, with some ML algorithms achieving very high or very low prediction accuracy depending on the preprocessing and feature engineering (extraction and selection) applied. Key insights include the observation that shallow ML models (LR, SVR, and shallow NNs) require meticulous preprocessing and feature extraction to achieve high accuracy. In contrast, the DNN model, although it bypasses the need for feature engineering, necessitates extensive preprocessing to mitigate noise and computational demands. The SVR model emerged as the top performer among the shallow models, and the DNN model exhibited superior performance on specific datasets but struggled with generalization across specimens from different batches. Additionally, the SVR model is sensitive to temperature variations, whereas the DNN model is robust in this regard. Using recurrent neural networks is recommended for future ASR expansion prediction studies. Recurrent neural networks’ inherent ability to capture temporal dependencies and long-term patterns makes them well suited for analyzing sequential ultrasonic monitoring data. Overall, the results and conclusions of this study could provide insights into the capabilities and effectiveness of ML when applied to ultrasonic NDE data and help identify best practices for using ML for ultrasonic NDE of concrete material properties.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Advancing Additive Manufacturing Through Artificial Intelligence–Powered, High-Throughput, Nondestructive Characterization and Process Optimization

This Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and ZEISS Industrial Metrology has demonstrated the transformative potential of artificial intelligence (AI)-enabled x-ray computed tomography (XCT) to accelerate the qualification and certification of additively manufactured (AM) parts. At the core of this effort is Simurgh, an AI-powered XCT reconstruction framework jointly advanced by ORNL and ZEISS that integrates computer-aided design (CAD) models, physics-based simulations, and deep learning to overcome the long-standing challenges of metal artifact correction, long scan durations, and limited flaw detectability in dense and geometrically complex components. Simurgh enables high-throughput, high-quality 3D reconstruction from sparse and fast scans, which reduces XCT acquisition times by more than an order of magnitude and simultaneously improves defect detection limits by up to fourfold compared with industry-standard approaches. This capability reduces scan costs by more than 50%, lowers labor overhead, and makes XCT characterization economically viable for routine industrial use. By enabling reliable flaw detection in minutes rather than hours, Simurgh facilitates real-time feedback loops for process parameter optimization, which was highlighted in a recent npj Computational Materials (a Nature journal) issue. In the published study, more than 100 alloy coupons were characterized within a single day. This work represents a tenfold acceleration in the development of novel AM alloys and processes compared with conventional workflows. The ZEISS collaboration has also demonstrated the scalability of Simurgh to diverse application domains, including aerospace, nuclear, automotive, and biomedical components; in these applications, ensuring structural integrity is paramount. By drastically reducing barriers to XCT adoption, this partnership has laid the foundation for digital twins and data-driven certification pipelines and directly addressed bottlenecks in qualifying new materials and designs. Together, ORNL and ZEISS have shown that Simurgh advances the state of the art in nondestructive evaluation and aligns with the broader mission of enabling Industry 4.0 manufacturing ecosystems, in which intelligent, cost-effective, rapid quality assurance is integral to accelerating innovation and ensuring safety in critical applications.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Advancing Additive Manufacturing Through Artificial Intelligence–Powered, High-Throughput, Nondestructive Characterization and Process Optimization

36 MATERIALS SCIENCE↗

DOE OSTI2025

Pulsed Infrared Thermography Nondestructive Imaging of SiC-SiC f Composite Cladding Architectures; Understanding the Performance of SiC-SiC f Composite Cladding Architectures with Cr Coating in Normal Operating and Accident Conditions in LWRs and Advanced Reactors

SiC-SiC f composites, consisting of silicon carbide fibers embedded in a silicon carbide matrix, are advanced materials with high thermal conductivity, temperature stability, and resistance to radiation damage. Traditional methods for quality control of fabricated SiC-SiC f composites involve nondestructive evaluation (NDE) with X-ray computed tomography (XCT). However, XCT imaging of typical SiC-SiC f structures for cladding applications can involve several hours. In this project, we investigate an alternative approach to NDE of SiC-SiC f composites that involves rapid (on the order of seconds) imaging with Pulsed infrared thermography (PIT). PIT images of a planar SiC-SiC f specimen show the structure of the surface monolith layer and internal SiC f structures. The capability of PIT imaging in visualizing SiC f structures is qualitatively confirmed by observing similarity in the PIT and X-ray transmission images of the same specimen. Computer vision analysis of defects in the PIT image of the monolith was performed with thresholding followed by topological structural analysis that computed geometric descriptors, including major/minor axes of fitted ellipses, area, perimeter, Feret diameter, circularity, roundness, and solidity.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Nondestructive Evaluation of Carbon Fiber Reinforced Polymers

The American Society of Mechanical Engineers Boiler and Pressure Vessel Code requires repair and replacement of safety-related piping materials to meet the original Construction Code; however, no construction criteria currently exist for carbon fiber reinforced polymer (CFRP) materials in the nuclear industry. Although nondestructive examination (NDE) techniques for cast and wrought ferritic and austenitic steels are well established, licensees are increasingly deploying novel materials such as CFRP for which assessment of the use of NDE is required, as the inspectability of such materials and the influence of manufacturing processes on inspectability remain insufficiently understood. To address these gaps, Pacific Northwest National Laboratory (PNNL) evaluated the fabrication of several CFRP repair mockups and the effectiveness of NDE methods to support regulatory review of CFRP repairs in nuclear power plant applications. Representative flat-plate mockups containing realistic fabrication defects were manufactured and inspected using manual and automated tap testing, dynamic response spectroscopy (DRS), and ultrasonic testing (UT). The study identified significant fabrication variability, particularly in controlling defect size and epoxy thickness, which strongly influenced defect detectability by NDE methods. Tap testing was effective for shallow defects in thin epoxy configurations but unreliable for thicker repairs and deeper flaws. DRS demonstrated higher sensitivity to dry spots but produced unconfirmed indications for thicker plates, requiring further validation. Conventional UT, particularly at 1.0 MHz, showed the strongest overall capability for detecting a range of defects, although performance degraded with increasing repair thickness and complexity. The results highlight the need to better define critical defect sizes, develop reliable mockup fabrication methods, and validate NDE methods needed to support CFRP repairs.

36 MATERIALS SCIENCE↗