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DOE OSTI2020

Boron and Lithium in Calcium Sulfate Veins: Tracking Precipitation of Diagenetic Materials in Vera Rubin Ridge, Gale Crater

The NASA Curiosity rover's ChemCam instrument suite has detected boron in calcium-sulfate-filled fractures throughout the sedimentary strata of Gale crater including Vera Rubin ridge. The presence of elevated B concentration provides insights into Martian subsurface aqueous processes. Here, we extend the data set of B in Ca-sulfate veins across Gale crater, comparing the detection frequency and relative abundances with Li. We report 33 new detections of B within veins analyzed between Sols 1548 and 2311 where detections increase in Pettegrove Point and Jura members, which form Vera Rubin ridge. The presence of B and Li in the Ca-sulfate veins is possibly due to dissolution of preexisting B in clays of the bedrock by acids or neutral water and redistribution of the elements into the veins. Elevated frequency of B detection in veins of Gale crater correlates with presence of dehydration features such as desiccation cracks, altered clay minerals and detections of evaporites such as Mg-sulfates and chloride salts in the host rocks. The increased observations of B also coincide with decreased Li concentration in the veins (average Li concentration of veins drops by ~15 ppm). Boron and Li have varying solubilities, and Li does not form salts as readily upon dehydration as B, causing it to remain in the solution. So the weak negative correlation between B and Li may reflect the crystallization sequence during dehydration on Vera Rubin ridge.

58 GEOSCIENCES↗

DOE OSTI2020

Improvement in the spatial resolution for imaging with fast neutrons

In this paper, we report on the realization of an improved concept for the detection of fast neutrons using a specific setup of scintillators with the aim to overcome limitations in spatial resolution. While fast neutron imaging (FNI) is a technique to investigate very thick material layers in transmission mode, currently there is a limit in the spatial resolution at ~1 mm, which hinders the performance for the detection of features like cracks, material damage or pores in large objects ($\gg$1 cm 3 ). The improved concept presented here is based on the separation of the converter material from the scintillation layer in a suitable way, which was successfully tested and referenced to the standard devices at the NECTAR facility (FRM-2, MLZ, Garching) under realistic beamline conditions. The results imply a break-through in enabling detection of structures on the order of 0.2 to 0.5 mm (a more precise estimation is not possible due to other resolution determing factors in a realistic setup). Potential additional improvements are discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

3D Broadband, Noncontact, and Nonlinear Phased Array

Researchers at Los Alamos have developed a 3D broadband, noncontact, and nonlinear ultrasonic phased array technology that enables businesses to characterize and identify cracks and defects using laserbased sensing at ultra-high resolution. It can be applied to a wide range of commercial and industrial markets, e.g., aerospace, aeronautical, nuclear energy, oil and gas, and civil and material engineering and more broadly to those interested in the non-destructive evaluation (NDE) of materials and structures. Traditional phased-array technology for subsurface imaging operate over a narrow frequency range, with a limited number of sensors, using one type of ultrasonic waves which may decrease the likelihood of detecting a structural material crack. This technology fills the information gap by utilizing the potential of laser-based sensing to remove all these limitations. We are seeking a commercialization partner to license the technology or fund us through a Cooperative Research and Development Agreement (CRADA) to further develop a usable product for commercial purposes.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Wintertime Brine Discharge at the Surface of a Cold Polar Glacier and the Unexpected Absence of Associated Seismicity

A subglacial groundwater system beneath Taylor Glacier, Antarctica, discharges hypersaline, iron-rich brine episodically at the glacier surface to create Blood Falls. However, the triggering mechanism for these brine release events is not yet understood. Identifying which fracture processes are observed seismically can help us better characterize the hydrological system at Taylor Glacier, and more generally, provide us with a broader understanding of englacial hydrologic activity in cold glaciers. We document wintertime brine discharge using time-lapse photography. Subfreezing air temperatures during the brine discharge indicate that surface melt-induced hydrofracture is an unlikely trigger for brine release. Further, we analyze local seismic data to test a hypothesis that fracturing generates elevated surface wave energy preceding and/or coinciding with brine release events. Our results show no discernible elevated Rayleigh wave activity prior to or during Blood Falls brine release. Instead, we find a pattern of seismic events dominated by a seasonal signal, with more Rayleigh events occurring in the summer than the winter from the Blood Falls source area. We calculate that the volumetric opening of cracks that would generate Rayleigh waves at our detection limits are of similar size to myriad cracks in glacier ice, lake ice, and frozen sediment in the terminus area. We therefore propose that any fracturing coincident with brine release activity likely consists of a series of smaller opening events that are masked by other seismicity in the local environment.

58 GEOSCIENCES↗

DOE OSTI2024

Rapid Electrochemical Diagnosis of Battery Health and Safety from Cells to Modules

Rapid electrochemical diagnosis of battery health and failure is critical for ensuring reliable battery performance and battery safety. Traditional battery health diagnostics such as capacity measurements and DC pulse tests are reliable and well-understood, however, these measurements of battery capacity and resistance do not capture all aspects of battery degradation. Other aspects of degradation, such as electrolyte decomposition, lithium-plating, and particle cracking are difficult to detect electrochemically but are crucial to measure to get a full picture of battery safety and flag out potential failures. In this work, lab- and field-aged commercial lithium-ion batteries and modules of various chemistries and formats are tested using a variety of traditional electrochemical characterization methods as well as using 2-minute pseudo-random DC pulse sequences at rest and during charge/discharge. The electrochemical measurements are compared to physical cell measurements, cell efficiency, drive cycle performance, physical and thermal heterogeneity, and qualitative safety metrics using statistical and machine-learning methods to discover if a comprehensive "battery health map" can be accurately identified using only rapid DC measurements.

ADVANCED PROPULSION SYSTEMS,ENERGY STORAGE↗

DOE OSTI2022

Embedded acoustic sensing and monitoring techniques for small modular reactors

The goal of this project was to study the applicability of specific innovative instrumentation techniques for assessing parameters needed for safe small modular reactor operation and safeguarding of nuclear material. Small modular reactor core designs are currently being developed to provide energy more effectively and efficiently than in the past because they can be built as modules at fabrication sites and then transported to a power-producing facility. However, these modules and/or the final core will often be sealed and not accessible again until disposal. Instead of instruments that access the core directly, during and after operation, to monitor flux/dose and structural integrity, as current power plants use, new sensors need to be designed that can be built into the reactor initially to determine operating history, structural integrity through operation of the system, and nuclear material accountancy after shutdown and before disposition of the core. Embedded sensors already exist that can provide neutron flux, gamma dose, and temperatures, but techniques to expand upon these for assessing structural health and material inventory in the system over time need to be developed. Structural health assessments include the detection and imaging of cracks in the components that could eventually cause radioactive fission products to be released.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Evaluation of Flaw Detection with SLDV Methods Before/After Mechanical Patch

This report provides a summary of the testing and the experiments to evaluate the utility of laser ultrasonic testing (UT) in detecting and characterizing defects on the surface of steel plates before and after the application of an engineered composite patch for crack repair. Included are the detection and characterization of defects from a section of welded plate material taken from the Sandia National Laboratories’ full-size mockup of a spent nuclear fuel (SNF) dry storage canister which had been pre-flawed and exposed to deliquescent sea salt conditions for 2 years. The information includes a review of previously reported metallurgical characterization, and new work on detailed surface characterization of the corrosion attack using laser UT.

36 MATERIALS SCIENCE↗

DOE OSTI2022

FY22 Progress Report: SRNL Analysis of ICCWR LCM and WAMS Data for Corrosion and Cracking

Algorithms for machine learning and data analysis for the 3013 Surveillance Program are being developed in an ongoing collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). To detect the presence of corrosion and cracking, data is collected from large binary files generated by a Laser Confocal Microscope (LCM) or Wide Area 3D Measurement System (WAMS). Software is being developed to use the physical attributes in the data files (e.g., height, color, and grayscale values; all as functions of a location in a plane projection) to detect the presence of surface corrosion and cracking. A user-friendly Matlab Graphical User Interface (GUI) that reads data from either LCM or WAMS files was developed to integrate input data with software developed for processing and evaluation. The GUI can selectively download binary data, interrogate data attributes, label data for training ML algorithms, flag significant features, execute Machine Learning (ML) algorithms, output parameters from trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. Surface defects can be called out by setting user-specified thresholds, feature based analysis or machine learning algorithms. Enhancements to data labeling capability have been developed to address this essential precursor to application of ML routines. Efficient labeling is particularly important in view of the very large volume of data required to train ML algorithms.

3013 Corrosion↗

DOE OSTI2024

FY24 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking

Algorithms for Machine Learning (ML) and data analysis for the 3013 Surveillance Program have been developed in an ongoing collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). The objective of the algorithms is to automate the identification of corrosion and crack formation in the Inner Container Closure Weld Region (ICCWR) of the canister system used to store Pu-bearing material. Data for corrosion and cracking is collected from large binary files generated by a Laser Confocal Microscope (LCM), the Wide Area 3D Measurement System (WAMS), or,in a recent proposal, by a Scanning Electron Microscope (SEM). The ML software uses the physical attributes in the data files (e.g., one or more of: height, color, and 16-bit grayscale values as functions of position in a plane projection) to detect signs of surface corrosion and cracking after being trained on similar data, with the features to be detected. Although the initial scope included screening for broader indicators of corrosion, e.g., pitting, identification of potential cracks was prioritized for the past several years at the request of program leadership. Labeled training data is essential to developing the ML algorithm, and enhancements to data labeling capability have been developed to address this essential precursor to application of ML routines. Efficient labeling is particularly important in view of the large volume of data required to train ML algorithms and the relative rarity of cracks in the ICCWR data set. The updated program will read binary data from either LCM, WAMS or SEM files, interrogate data attributes, facilitate user labeling of data for training ML algorithms, execute ML algorithms, output parameters from trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. In FY24, hourglass neural networks (HNNs) that were initiated in FY22 were further developed and tested using available LCM data, and their performance was tested against that of the alternative U-Net Neural Network algorithm structure. HNNs along with previously developed Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) comprise a suite of ML tools for identification of cracks in the ICCWR

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

FY25 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking

Algorithms for Machine Learning (ML) and image analysis for the 3013 Surveillance Program have been developed in an ongoing collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). The objective of the algorithms is to automate the identification of corrosion and cracks in the Inner Container Closure Weld Region (ICCWR) of the canister system used to store Pu-bearing material. Data for corrosion and cracking is collected from large binary files generated by a Laser Confocal Microscope (LCM), the Wide Area 3D Measurement System (WAMS), or, in a recent proposal, by a Scanning Electron Microscope (SEM). The ML software uses the physical attributes in the data files (e.g., one or more of: height, color, and 16-bit grayscale values as functions of position in a plane projection) to detect signs of surface corrosion and cracking after being trained on similar data with the features to be detected. Although the initial scope included screening for broader indicators of corrosion, e.g., pitting, the identification of potential cracks was prioritized for the past several years at the request of program leadership.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Characterization of mechanical discontinuities based on data-driven classification of compressional-wave travel times

Wave propagation and diffusive transport phenomena are influenced by the mechanical discontinuities in material. Here this study shows that certain bulk properties of the network of low-velocity mechanical discontinuities (e.g. air-filled cracks) in a material can be characterized by processing compressional-wave travel times using traditional data-driven classification techniques. To that end, we perform three tasks in chronological order: (1) use the discrete fracture network (DFN) method to create two-dimensional (2D) numerical models of crack-bearing material embedded with various types of low-velocity mechanical discontinuities, (2) use the fast marching method (FMM) to simulate the propagation of the wave/diffusion front from a single source through the 2D crack-bearing material to multiple receivers placed on the boundary of the material, and (3) train 9 data-driven classifiers to characterize the crack-bearing materials (i.e. bulk properties of the network of mechanical discontinuities in the crack-bearing material) by learning from the simulations of travel times detected by multiple receivers placed around the crack-bearing material. The classifiers identified the orientation, spatial distribution, and dispersion of the low-velocity mechanical discontinuities. Voting classifier performs the best among the 9 classifiers. For the characterization of bulk dispersion and distribution of discontinuities, the sensors located on the adjacent boundaries are more important; whereas for the characterization of bulk orientation of discontinuities, the sensors located on the opposite side are more important.

42 ENGINEERING↗

DOE OSTI2025

Irradiation Testing of Ultrasonic Transducers

Ultrasonic technologies offer the potential for high accuracy and resolution in-pile measurement of numerous parameters, including geometry changes, temperature, crack initiation and growth, gas pressure and composition, and microstructural changes. Many Department of Energy-Office of Nuclear Energy (DOE-NE) programs are exploring the use of ultrasonic technologies to provide enhanced sensors for in-pile instrumentation during irradiation testing. For example, the ability of single, small diameter ultrasonic thermometers (UTs) to provide a temperature profile in candidate metallic and oxide fuel would provide much needed data for validating new fuel performance models. Other efforts include an ultrasonic technique to detect morphology changes (such as crack initiation and growth) and acoustic techniques to evaluate fission gas composition and pressure. These efforts are limited by the lack of existing knowledge of ultrasonic transducer material survivability under irradiation conditions. To address this need, the Pennsylvania State University (PSU) was awarded an Advanced Test Reactor National Scientific User Facility (ATR NSUF) project to evaluate promising magnetostrictive and piezoelectric transducer performance in the Massachusetts Institute of Technology Research Reactor (MITR) up to a fast fluence of at least 1021 n/cm2 (E> 0.1 MeV). This test will be an instrumented lead test; and real-time transducer performance data will be collected along with temperature and neutron and gamma flux data. By characterizing magnetostrictive and piezoelectric transducer survivability during irradiation, test results will enable the development of novel radiation tolerant ultrasonic sensors for use in Material and Test Reactors (MTRs). The current work bridges the gap between proven out-of-pile ultrasonic techniques and in-pile deployment of ultrasonic sensors by acquiring the data necessary to demonstrate the performance of ultrasonic transducers

Daw, J.↗

DOE OSTI2025

Enhanced Design of Radiation Tolerant High-Temperature Structural Health Monitoring Sensors

Acoustic emission sensors are vital in the nuclear industry for real-time structural health monitoring and early detection of material degradation. By capturing high-frequency stress waves emitted from defects like cracks, corrosion, or fatigue, acoustic emission sensors enable non-invasive monitoring of critical components such as reactor vessels, piping, and containment structures. This technology supports predictive maintenance, enhances safety, and ensures regulatory compliance by providing early warnings of potential failures. It is also instrumental in research, particularly in material testing reactors, where it is used to monitor the behavior of fuels and materials under irradiation, by allowing the detection of cracking or other acoustic signals in real time. This enables the evaluation of performance and accident behavior of advanced fuel concepts.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

DOE OSTI2023

Failure analysis of nuclear transient-tested UN tristructural isotropic fuel particles in a 3D printed SiC matrix

Fully ceramic microencapsulated fuel elements containing UN tristructural isotropic (TRISO) fuel particles within a 3D printed SiC matrix were subjected to transient testing with varying energy depositions. Detailed post-irradiation examinations were performed, including leaching in hot HNO3 and post-leaching X-ray computed tomography, to quantify the percentage of failed TRISO particles and crack propagation within the particles and surrounding fuel matrix. In parallel, detailed finite element analyses were performed for comparison with experimental findings and to better evaluate transient failure modes. The lowest transient energy deposition—which still exceeded bounding values for high-temperature gas-cooled reactor applications—resulted in no detectable TRISO particle failures or matrix cracking, which was consistent with the simulations. Simulations of the higher-energy transients for which significant TRISO particle failure was expected were generally able to reproduce the transient temperatures and matrix cracking. Thus, the TRISO particle failures were explained based on the effects of local SiC matrix thickness and porosity. Results generally confirmed the high strength of the additively manufactured SiC matrix but also affirmed the need for a modified UN TRISO architecture to prevent SiC matrix cracks from propagating through TRISO layers. This unique failure mode has not historically been considered for TRISO fuels contained in weaker graphite matrices.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

FY23 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Beam Hardening Artifact Reduction in X-Ray CT Reconstruction of 3D Printed Metal Parts Leveraging Deep Learning and CAD Models

Nondestructive evaluation (NDE) of additively manufactured (AM) parts is important for understanding the impacts of various process parameters and qualifying the built part. X-ray computed tomography (XCT) has played a critical role in rapid NDE and characterization of AM parts. However, XCT of metal AM parts can be challenging because of artifacts produced by standard reconstruction algorithms as a result of a confounding effect called “beam hardening.” Beam hardening artifacts complicate the analysis of XCT images and adversely impact the process of detecting defects, such as pores and cracks, which is key to ensuring the quality of the parts being printed. In this work, we propose a novel framework based on using available computer-aided design (CAD) models for parts to be manufactured, accurate XCT simulations, and a deep-neural network to produce high-quality XCT reconstructions from data that are affected by noise and beam hardening. Using extensive experiments with simulated data sets, we demonstrate that our method can significantly improve the reconstruction quality, thereby enabling better detection of defects compared with the state of the art. We also present promising preliminary results of applying the deep networks trained using CAD models to experimental data obtained from XCT of an AM jet-engine turbine blade.

Ziabari, Amir↗

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 OSTI2021

Unveiling damage mechanisms of chromium-coated zirconium-based fuel claddings at LWR operating temperature by in-situ digital image correlation

Here, we investigate the coupled thermomechanical fracture mechanisms of coated nuclear fuel claddings at Light Water Reactor (LWR) operating temperatures. These coated claddings are a highly attractive, near-term solution, which addresses the demands for accident-tolerant fuel systems and provide greater oxidation resistance. However, the fracture mechanisms of these coatings, which may create channels for oxidation ingression, must be fully understood prior to implementation. Thus, high-temperature expanding plug experiments were conducted on coated cladding specimens at a temperature of 315 °C, consistent with the operating environment of LWRs. Furthermore, in-situ thermomechanical deformation was measured with stereo digital image correlation during heating and mechanical testing to separately resolve contributions of thermal and mechanical strain. Digital image correlation, supported by acoustic emissions (AE) detection, was also leveraged to track cracking activity during loading. Coating fracture was found to initiate at total hoop strains of 0.34%. The thermal deformation of the coated claddings was investigated via finite element simulations, revealing a bidirectional stress-state within the coating with axial and circumferential strains of 0.026 and 0.031%. This bidirectional stress-state was attributed with the generation of off-axis fracture pattern within the coating as identified via post-experiment scanning electron microscopy. Thus, this study unveiled critical, coupled thermomechanical mechanisms governing the coating fracture of coated claddings at LWR temperatures.

36 MATERIALS SCIENCE↗