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DOE OSTI2022

Homomorphic Encryption for Machine Learning and Artificial Intelligence Applications

Third-party and expert analysis is a cost-effective solution for solving specialized problems or processing large datasets related to reactor structural health monitoring and nondestructive evaluation. However, when handling proprietary information, third-party and expert analysts pose a privacy risk. To address this challenge, Homomorphic Encryption (HE) permits arithmetic operations on encrypted data without exposing the underlying data. Implementations of Machine Learning (ML) and Artificial Intelligence (AI) algorithms using HE greatly enhances the capabilities of third-party analysts while maintaining a low security risk. This paper details current success in applying Principal Component Analysis (PCA) and Fully Connected Neural Networks (NN) using the Microsoft SEAL implementation of the popular CKKS Fully Homomorphic Encryption (FHE) algorithm. The MNIST Handwritten Dataset is analyzed as a proof-of-concept demonstration of the implementations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Facility Safety Plan – Building 225

B225 is operated by the Weapons and Complex Integration (WCI) Directorate and is classified as a Low Hazard facility for radiological hazards and Light Science Industry (LSI) for chemical, radiation generating devices (RGDs) and industrial hazards. Explosives and biological hazards are not authorized for B225. B225 is located in the southwest quadrant of Lawrence Livermore National Laboratory (LLNL) less than 600 meters from the nearest site boundary. B225 is a single-story concrete tilt-up building with a steel deck roof. The primary mission of this facility is to apply nondestructive evaluation (NDE) methods to materials, components, and assemblies. B225 also contains the polymer additive manufacturing laboratories. The floor plans for B225 are provided in Appendix A.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Material Characterization of Grade 91 Steel Welds using, Micro-resolution Ultrasonic Imaging System

Grade 91 (9Cr-1Mo-V) steel is a common structural material for fossil-fuel fired and nuclear power plants because of its creep strength and oxidation resistance at high temperatures. In this research, we studied the feasibility of correlating the high-resolution images obtained from the immersion ultrasonic imaging technique to microstructure regions in Grade 91 steel plate weldments. The high-resolution ultrasonic images presented in this paper were generated using a 20-MHz focused transducer as the transmitter, and a laser vibrometer as the receiver in the through transmission configuration to produce longitudinal and shear waves. This nondestructive evaluation (NDE) method was used to identify the Fusion Zone (FZ), Heat-Affected Zone (HAZ), and Base Metal (BM) regions of Grade 91 cold metal transfer (CMT) and flux core arc welds (FCAW). The ultrasonic imaging results were analyzed using beam propagation amplitudes and homogeneity, which in turn were used to relate the different microstructure regions of the FCAW and CMT welded samples. With this study, we were able to identify highly susceptible microstructures in the weld that could potentially promote creep type failures.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2022

Design Investigation of a Scalable Fast Neutron Radiography Panel

Advances in flat-panel radiography device design have increased the use of radiation imaging as a nondestructive evaluation tool. Neutron radiography offers many advantages compared to photon-based radiography.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Light Water Reactor Sustainability: Program Materials Research Pathway (FY22 Technical Program Plan)

The Materials Research (MR) Pathway within the Light Water Reactor Sustainability (LWRS) program is charged with performing R&D to develop the scientific basis for understanding and predicting the long-term environmental degradation behavior of materials in nuclear reactors. Furthermore, the mechanistic understanding of degradation phenomena in materials must be leveraged to develop mitigation and repair strategies and new material alternatives for existing components. This research will provide data and methods to assess the performance of systems, structures, and components essential to safe and sustained reactor operations. The R&D products developed from the LWRS program will be used by stakeholders—including utilities, industry groups, and regulators—to inform operational and regulatory requirements for materials in reactor systems, structures, and components subjected to long-term operation conditions, providing key inputs to regulators and industry. Therefore, the intent of this research is to provide options to reduce the operating costs, which may be in the form of offset maintenance costs due to better predictive models for component lifetimes, improved analyses of materials through nondestructive evaluation, reduced costs for repairs, or extended performance of plants through the selection of improved replacement materials. To provide the best options, industry experience and guidance are important because of their role in coordinated or collaborative research projects. The objectives of this report are to describe the motivation and organization of the MR Pathway within the LWRS program; provide details on the individual research tasks within the MR Pathway; describe the outcomes and deliverables of the MR Pathway, including recent technical highlights and progress; and describe the requirements for performing this critically important research.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2023

Detection of System Drift for the Health Monitoring of an X-ray CT Scientific Instrument

This SE296 Capstone Project technical report is being submitted as a final requirement of the UCSD Master of Science in Structural Engineering with specialization in Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE). The Capstone provides students the opportunity to apply knowledge in their technology areas towards the solution of an SHM or NDE problem. As an employee of the Lawrence Livermore National Laboratory and NDE/NCI team member, I chose to apply the SHM design paradigm taught at UCSD to improve the health monitoring of the X-ray Micro-Computed Tomography (MCT) system. I would like to thank LLNL’s Dr. Harry Martz for serving as my mentor during this project and the entire LLNL MCT technical team for answering my questions and contributing to my knowledge. I would also like to thank Prof. Michael Todd for recruiting me to the UCSD NDE/SHM program and serving as my graduate advisor.

42 ENGINEERING↗

DOE OSTI2023

Machine Learning for Processing Ultrasonic Data from Long-Term Monitoring of Concrete with Alkali-Silica Reaction (ASR)

The alkali–silica reaction (ASR) is a phenomenon that leads to material degradation in concrete, resulting in the formation of microcracks and cracks. This deterioration causes a loss of mechanical properties, concrete damage, and even corrosion. To address this issue, ultrasonic nondestructive evaluation can be employed as a technique for long-term monitoring of ASR development and condition assessment of concrete subjected to ASR. However, traditional approaches typically utilize only a few wave parameters, such as wave velocity or amplitude, to characterize ASR-induced concrete damage while disregarding the majority of information present in the ultrasonic signals.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Development of an advanced ultrasonic phased array for the characterization of thick, reinforced concrete components (Final Scientific/Technical Report)

There are no nondestructive evaluation (NDE) tools capable of characterizing microscale damage throughout the thickness of concrete components, due to the multiphase, heterogeneous and multiscale nature of concrete. Ultrasound is only scattered by features at the same length scale, or smaller, than the wavelength of a wave’s dominant frequency. Successful imaging of microscale damage using ultrasound requires that the ultrasonic wavelength be on the order of a few millimeters (or smaller), yet the heterogeneous microstructure of concrete, with its fine and coarse aggregates, is on this same (and higher) micrometer/millimeter length scale, causing excessive ultrasonic wave scattering even in “good” concrete. The proposed solution applies non-collinear wave mixing to spatially image microscale damage, while still maintaining penetration through thick concrete components. This microscale imaging is possible by combining nonlinear wave mixing, with advanced phased array hardware and software to develop a breakthrough tool that will bring revolutionary changes in NDE of concrete infrastructure in terms of image resolution and depth of penetration. This work uses non-collinear wave mixing which exploits the physics that material nonlinearities such as microscale damage, cause interactions between two intersecting ultrasonic waves due to cross-mixing, which can lead to the generation of a third wave with a frequency and wave number of the sum or difference of the incident waves. The concrete material volume at this mixing point is characterized/imaged. This project delivered a single-sided nonlinear ultrasonic phased array imaging device, that can image microscale damage (microcracks of 100 micrometers) through a 0.5 m thick concrete component and assessed the commercial feasibility of such arrays for improved crack detection.

42 ENGINEERING↗

DOE OSTI2024

Review of In Situ Sensing for Directed Energy Deposition for Industrial Part Quality Assessment

As the use additive manufacturing (AM) processes continues to grow in critical industries, improved quality assurance methods are becoming increasingly sought after for qualification and certification of AM components. Traditional nondestructive evaluation of printed components is often unable to supply the required confidence in print quality to justify qualification and certification, but the layer-by-layer nature of AM provides unprecedented opportunities for in situ quality inspection. This document summarizes recent developments in process monitoring research specifically related to Directed Energy Deposition (DED). Particular attention is given to three aspects of the highlighted manuscripts: (1) the type of sensors used, (2) features extracted from each sensor modality, and (3) analysis of extracted features for AM quality assessment. Based on the review of the state-of-the-art, several observations have been made. First, none of the reviewed works have applied their trained models to real part geometries, with many of the works relying on single track experiments, thin-walled structures, and cubes. Similarly, there have not been any works demonstrating model generalizability, i.e., a model trained on data from one build allows for fruitful analysis of data from another build. Many works used machine learning techniques to distinguish different process regimes (i.e., normal, keyholing, lack-of-fusion), but very few papers have investigated stochastic variation in an already “optimized” process. Sensor fusion approaches are also limited in the DED sensing literature, but the few works that have employed such techniques have demonstrated the benefits. Finally, registration of in situ data to the build coordinate system is of paramount importance to producing industrially relevant in situ monitoring systems. Data registration allows direct correlations between process anomalies detected in the process monitoring data to localized departures in part quality, but such techniques are generally lacking in the current literature.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Initial Results of PostProcess NDE Using Advanced Techniques

The U.S. Department of Energy, Office of Nuclear Energy Advanced Materials and Manufacturing Technologies Program is developing advanced materials and manufacturing technologies that will enable the safe and economical operation of both the current fleet and the next generation of advanced nuclear reactors. Post-process nondestructive evaluation capabilities are a key enabling technology that will be required to qualify and verify the condition of components produced using advanced materials and manufacturing technologies.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

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 OSTI2022

Initial Refinement and Optimization of the Image Reconstruction (U-MBIR) Algorithm to Optimize its Performance in Detecting Damage and Flaws

Concrete is a critical component of nuclear power plants; thus, its safety and reliability must be thoroughly examined throughout the life cycle of the structural system. During the life cycle of this infrastructure, monitoring of the concrete for signs of degradation should be performed through nondestructive evaluation (NDE). Ultrasonic measurements have been an industry standard for both surface and subsurface inspections. As such, Oak Ridge National Laboratory (ORNL) is developing advanced image reconstruction algorithms to overcome the limitations of traditional ultrasonic NDE methodologies. The results and discussion presented herein summarize the current state of the ultrasonic model–based iterative reconstruction (U-MBIR) algorithm developed at ORNL.

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 OSTI2024

Materials Characterization: A Primer for Solid Phase Processing Applications

The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development (LDRD) Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems produced via advanced manufacturing methods, such as solid phase processing, for use in national security and advanced energy applications (Smith 2021). As a two-year LDRD investment requiring focused research, the MCPC project applied only a subset of the wide range of available destructive and nondestructive characterization methods to provide data to the predictive modeling and data analytics tasks. The purpose of this report is to review a wide range of destructive and nondestructive characterization methods that are relevant in solid-phase processing (SPP) applications, but not necessarily applied in the MCPC Project as a guide to the planning of characterization activities in future research. Particular attention is given to measured characteristics that can correlate to other material characteristics, with a particular interest in nondestructive evaluation (NDE) that can be applied to samples obtained in the MCPC Project. Destructive examinations include tensile tests, optical and electron microscopy, micro-hardness, and residual stress tests. NDE tests include surface visual inspection, eddy current examination for cracks, 4-point potential drop, ultrasound, x-ray, and computed tomography.

36 MATERIALS SCIENCE↗

DOE OSTI2021

AI-Enabled Robotic NDE for Structural Damage Assessment and Repair

The aim of this paper is to develop the concept and a prototype of an intelligent mobile robotic platform that is integrated with nondestructive evaluation (NDE) capabilities for autonomous live inspection and repair. In many industrial environments, such as the application of power plant boiler inspection, human inspectors often have to perform hazardous and challenging tasks. There is a significant chance of injury, considering the confined spaces and limited visibility of the inspection environment and hazards such as pressurization and improper water levels. In order to provide a solution to eliminate these dangers, the concept of a new robotic system was developed and prototyped that is capable of autonomously sweeping the region to be inspected. The robot design contains systematic integration of components from robotics, NDE, and artificial intelligence (AI). A magnetic track system is used to navigate over the vertical steel structures required for examination. While moving across the inspection area, the robot uses an NDE sensor to acquire data for inspection and repair. This paper presents a design of a portable NDE scanning system based on eddy current array probes, which can be customized and installed on various mobile robot platforms. Machine learning methods are applied for semantic segmentation that will simultaneously localize and recognize defects without the need of human intervention. Experiments were conducted that show the NDE and repair capabilities of the system. Improvements in human safety and structural damage prevention, as well as lowering the overall costs of maintenance, are possible through the implementation of this robotic NDE system.

Materials Science↗

DOE OSTI2021

Integrating Electromagnetic Acoustic Transducers in a Modular Robotic Gripper for Inspecting Tubular Components

Tubular structures are critical components in infrastructure such as power plants. Throughout their life, they are subjected to extreme conditions or suffer from defects such as corrosion and cracks. Although regular inspection of these components is necessary, such inspection is limited by safety-related risks and limited access for human inspection. Robots can provide a solution for automatic inspection. The main challenge, however, lies in integrating sensors for nondestructive evaluation with robotic platforms. As part of developing a versatile lizard-inspired tube inspector robot, in this study the authors propose to integrate electromagnetic acoustic transducers into a modular robotic gripper for use in automated ultrasonic inspection. In particular, spiral coils with cylindrical magnets are integrated into a novel friction-based gripper to excite Lamb waves in thin cylindrical structures. To evaluate the performance of the integrated sensors, the gripper was attached to a robotic arm manipulator and tested on pipes of different outer diameters. Two sets of tests were carried out on both defect-free pipes and pipes with simulated defects, including surface partial cracking and corrosion. The inspection results indicated that transmitted and received signals could be acquired with an acceptable signal-to-noise ratio in the time domain. Moreover, the simulated defects could be successfully detected using the integrated robotic sensing system.

Materials Science↗

DOE OSTI2024

Enhancing NDE Reliability for Grade 91 Steel Welds: Ultrasonic Imaging and Microstructural Correlations

Ensuring the integrity of Grade 91 (9Cr-1Mo-V) steel welds is vital for the safe and reliable operation of fossil fuel–fired and nuclear power plants. Here, this study applies an imaging technique for the ultrasonic characterization of two Grade 91 steel welds created with cold metal transfer and flux-cored arc welding processes. Ultrasonic immersion testing in the through-transmission configuration was employed to generate shear waves, which helped identify the weld metal, heat-affected zone, and base metal regions. These weld microstructures were also correlated to their ultrasonic images using metallography, ultrasonic amplitude, hardness measurements, and grain size. The findings from this study can assist practitioners in developing new nondestructive evaluation technologies, improving the inspection reliability of creep strength–enhanced ferritic steel welds by potentially identifying weld microstructure regions susceptible to creep-type failures.

Grade 91 steel↗