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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Robust Spectral Anomaly Detection in EELS Spectral Images via 3D Convolutional Variational Autoencoders

Abstract A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross‐entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect‐free material. In exploring methods for anomaly detection, both the 3D‐CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using FeL‐edge ΔEpeak shifts designed to simulate material defects. These results show that 3D‐CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower‐dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise‐dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS‐SI data, particularly valuable for analyzing complex material systems.

Chemistry↗
DOE OSTI2025

Machine Learning Thermodynamics And Kinetics of Defects For Accelerated Materials Discovery

Atomistic defects play a pivotal role in functional and structural materials’ performance across a myriad of technology applications. Quantitative prediction of the thermodynamics and kinetics of defect formation and migration, respectively, typically requires accurate but expensive first-principles approaches, such as density functional theory (DFT). Their computational expense limits the throughput needed to perform high-throughput materials discovery/screening exercises or to perform materials modeling tasks relying on extensive sampling techniques. Therefore, in this Sandia National Laboratories Laboratory Directed Research and Development (LDRD) project (Project #229366), we developed a variety of machine learning techniques, trained on density functional theory calculations, to accelerate the discovery and modeling of materials in which vacancy and interstitial defects primarily dictate material performance. These include applications such as metal oxides for water-splitting or mixed ionic-electronic conduction, metal hydrides for hydrogen storage, and transition metal dichalcogenides for electronics, and the approaches developed herein can further be applied to many other domains that similarly depend on materials’ thermodynamic and kinetic defect properties for their desired functionality.

36 MATERIALS SCIENCE↗
NASA NTRS1994

Scaling effects of defects in fiber-reinforced composites

Material defects may be introduced willingly or unwillingly during material manufacturing and structural component fabrication stages. Their presence in the material plays a dominant role in determining the material's strength and the associate failure mechanisms. In the sense that the size and the number of defects may increase with the volume of the material, the effect of dimensional scaling may manifest itself in the dependence of material strength on volume. Or, alternatively, there may exist a scaling effect of material defects. In fiber-reinforced composites, manufacturing or fabrication defects may come in several forms: matrix voids, matrix microcracks, fiber misalignment, broken fibers, or interface disbonds, just to mention a few. These are interacting and competing defects in the sense that one type of defect may become dominant under one stress condition and another type of defect may become dominant under a different stress condition. This happens because the fiber reinforcement network, together with the distribution of defects, constitutes the prime microstructure of the composite, and there exist continued interactions between the evolving microstructure and the distribution of defects. In the process, the scaling effects of defects are complicated by this interaction. In this presentation, the scaling effects of defects in fiber-reinforced composites will be briefly discussed with the introduction of the concept of effective defects. It is then shown with the aid of some actual experimental and analysis results that the scaling effects are very much present, but they are regulated by the characteristic dimension of the composite microstructure due to the aforementioned microstructure-defect interaction effect.

Wang, A. S. D.↗
NASA NTRS1992

Fracture mechanics expert system

Attention is given to fracture mechanics, an analytical method used extensively in the National Space Transportation System to conservatively predict the remaining service life of an article when a flaw or a material defect is detected. These analyses are performed on hardware containing material defects that have been detected by various nondestructive inspection techniques. An expert system being developed to streamline the process so that hardware dispositions may be obtained in a timely and consistent manner is discussed. The expert system reduces the potential for errors due to the manual transcription between the various software programs involved in completing a fracture mechanics analysis. NEXPERT Object, the expert system development shell selected for this purpose, allows the various software programs used in fracture mechanics analyses to be accessed and manipulated from the same platform.

Powers, E.↗
NASA NTRS1977

Cameras for semiconductor process control

The application of X-ray topography to semiconductor process control is described, considering the novel features of the high speed camera and the difficulties associated with this technique. The most significant results on the effects of material defects on device performance are presented, including results obtained using wafers processed entirely within this institute. Defects were identified using the X-ray camera and correlations made with probe data. Also included are temperature dependent effects of material defects. Recent applications and improvements of X-ray topographs of silicon-on-sapphire and gallium arsenide are presented with a description of a real time TV system prototype and of the most recent vacuum chuck design. Discussion is included of our promotion of the use of the camera by various semiconductor manufacturers.

Porter, W. A.↗
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↗
NASA NTRS1995

Thermographic imaging for high-temperature composite materials: A defect detection study

The ability of a thermographic imaging technique for detecting flat-bottom hole defects of various diameters and depths was evaluated in four composite systems (two types of ceramic matrix composites, one metal matrix composite, and one polymer matrix composite) of interest as high-temperature structural materials. The holes ranged from 1 to 13 mm in diameter and 0.1 to 2.5 mm in depth in samples approximately 2-3 mm thick. The thermographic imaging system utilized a scanning mirror optical system and infrared (IR) focusing lens in conjunction with a mercury cadmium telluride infrared detector element to obtain high resolution infrared images. High intensity flash lamps located on the same side as the infrared camera were used to heat the samples. After heating, up to 30 images were sequentially acquired at 70-150 msec intervals. Limits of detectability based on depth and diameter of the flat-bottom holes were defined for each composite material. Ultrasonic and radiographic images of the samples were obtained and compared with the thermographic images.

Roth, Don J.↗
DOE OSTI2020

Characterizing Dislocation Defects in Dark Field X-ray Microscopy Images of Bulk Aluminum

Material defects play a large role in material response under shock loading, yet the process of how these defects initiate, propagate, and annihilate is not well understood. Using a newly developed diagnostic, dark field X-ray microscopy (DFXM), we can now visualize the behavior of dislocation defects in materials at the mesoscale under varying conditions. Using data from the European Synchrotron Radiation Facility, we apply a variety of image processing techniques to capture relevant features to locate and characterize size and orientation of dislocation defects in bulk aluminum DFXM images. Beyond simple visualization, this analysis drives statistical characterization of the defects and their dynamic response to further improve the relevant physics models.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

ACT University Collaboration Proposal: Role of manufacturing defects on material failure under dynamic loading for developing enhanced failure models and theories (Final Report)

Research on the tensile behavior of additively manufactured 316L stainless steel coupon specimens at increasing strain rates was conducted over the past year at Penn State. Dynamic loading rates into the 1000 1/s loading rates were performed on specimens with nearly 1.32” gage lengths. Stress-strain plots show ductile and plastic behavior well beyond 12% strain with necked specimens, having smaller effective gage lengths showing up to 56% ultimate strain. Additional tests performed on compact tension specimens helped with simulation work to understand the deformation behavior. Using finite elements, it was possible to determine the feasibility of a comprehensive experimental validation study towards an improved model for AM material failure – specifically, the Bai-Wierzbicki approach which accounts for lode angle and triaxiality. A non-significant number of tests are projected for eight specific failure nodes, with additional replicates to provide strain-rate capabilities to the existing formulation. One such correction factor is explored for quasi-static, notched specimens. Finally, stress intensity factor was explored using a set of compact-tension specimens which would also provide useful validation data for any simulation work.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Classification of computed thermal tomography images with deep learning convolutional neural network

Thermal tomography (TT) is a computational method for the reconstruction of depth profile of the internal material defects from Pulsed Infrared Thermography (PIT) nondestructive evaluation. Here, the PIT method consists of recording material surface temperature transients with a fast frame infrared camera, following thermal pulse deposition on the material surface with a flashlamp and heat diffusion into material bulk. TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of the subsurface internal defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to the heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify the size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. The performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shapes imported from scanning electron microscopy images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on the TT images of elliptical defects is capable of classifying the shape and orientation of irregular defects.

42 ENGINEERING↗
DOE OSTI2022

Defect identification in simulated Bragg coherent diffraction imaging by automated AI

X-ray Bragg coherent diffraction imaging is a powerful technique for operando and in situ materials characterization and provides a unique means of quantifying the influence of one-dimensional (1D) and two-dimensional (2D) material defects on material response. However, obtaining full images from raw x-ray diffraction data is nontrivial and computationally intensive, precluding real-time experimental feedback. Here, we present a machine learning approach to identify the presence of crystalline line defects (edge and screw) in samples from the raw, 2D, coherent diffraction data without the need for image reconstruction through iterative phase retrieval. Further, we compare different approaches to designing neural networks for this application and demonstrate the potential of automated ML (autoML) approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Photoluminescence mapping of laser-damaged β-Ga 2 O 3

Here, photoluminescence (PL) mapping was utilized to investigate damage in β-Ga 2 O 3 epilayers induced by 1064 nm laser pulses. The intensity and position of the intrinsic UV band were determined and plotted as a false-color image. Two types of damage were identified: circular damage and damage cracks. Circular damage shows lower UV PL intensity than the surrounding material with color centers in a “halo” around the damaged region. Damage cracks are aligned with the a and c axes and show higher PL intensity than undamaged material. Defects in the as-grown material were revealed by shifts in the UV band energy.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Investigation of Defect Physics for Efficient, Durable and Ubiquitous Perovskite Solar Modules (Final Technical Report)

Organometal halide perovskite solar cells have experienced eye-catching improvements in its recent few years. It serves as one of the most promising candidates to replace the currently widely used silicon-based solar modules. To implement its final step to the real application, functional longevity becomes the dernier continent to conquer. As a polycrystalline material, defect plays critical role in the efficiency and stability of the perovskite solar cells. Thus, the investigation of the defect physics of the perovskite layer is indispensable in this research field. However, hard evidence and a consensus are still lacking in terms of the specific nature of the defects and their effects on performance and hysteresis, and perhaps even more importantly, there is absence of fundamental understanding of the correlations between the defects and long-term operational stability of the device. A more fundamental understanding of the nature of defects in perovskite materials is of paramount importance to progress their efficiency and durability. In this work we propose in-depth studies of correlations of defects with performance and stability of perovskite solar cells. Our project aimed 1) to investigate the defects physics in perovskite solar cells, and 2) to develop a comprehensive understanding and physical model of defects and its influence on performance and stability of perovskite solar cells. With the support from program manager, Peter Lobaccaro, and the Solar Energy Technologies Office of U.S. Department of Energy, the project ends with impact achievements. Our research results have systematically provided strategies to analyze the influences of constructive molecular configurations to the charged defects in the perovskite lattices and developed in-depth understanding of chemical additive approach to improve the perovskite solar cell performance and stability. As history has shown us, control over defect properties of semiconductor materials is the key to achieving high performance and low cost devices. Therefore, the potential impact of unlocking the understanding and manipulation of defects in perovskites is great, enabling this technology to realize SETO goals. The research project is highly productive with 18 published papers in three years in top-level journals such as Science, Nature, Nature Materials, Nature Communications, Journal of American Chemistry Society, Joule, Advanced Materials, and Nano Letters. These works have drawn great attention nationwide with notable total citations over 700 times from 2020 to 2022.

14 SOLAR ENERGY↗
DOE OSTI2023

NanoMechanics: Elasticity and Friction in Nano‐Objects

A large scientific and technological effort is underway to investigate the properties of two‐dimensional (2D) materials to become building blocks in integrated nano‐electronic and photonic circuits, composites, coatings, energy harvesting nano‐ systems, nano‐sensors, and nano‐electro‐mechanical systems (NEMS).While several experiments and calculations have revealed exciting novel phenomena in these nanostructures, many scientific and technological questions remain open. In particular, understanding and controlling the structure and mechanical properties of 2D materials at the interface with a solid surface is of key importance to enable the aforementioned nanotechnologies, as well as to fulfill the potential of strain engineering to tailor their properties. The vision of this DoE research program was to investigate novel mechanical properties and phenomena in 1D and 2D materials with the overarching goal of defining a new basic understanding of mechanical behavior in nano and quantum systems. The group of the PI has developed in the last years several studies on the mechanical properties of Carbon nanotubes and oxide nanobelts, more recently the PI has focused her attention on the properties of two‐dimensional materials, such as graphene and MoS 2 , which are a few‐atomic‐layer thick films and hold a great potential for technological and energy applications. The most studied 2D material is graphene, existing as a single layer of graphite or a few‐layer thick epitaxial graphene film. Graphene possesses a large in‐plane Young’s modulus as well as high intrinsic carrier mobility, and high in‐plane thermal conductivity. Besides graphene, also 2D films of graphene oxide (GO), hexagonal Boron Nitride (h‐BN), and transition metal dichalcogenides such as MoS 2 exhibit unique and excellent properties and hold great promise for nanotechnology applications. This research program was aimed at developing a new basic understanding of the mechanical properties of 1D and 2D materials at the interface with solid substrates, with a focus on the role of defects, materials structure, and substrate interaction. Ultimately, we aimed at developing the basic knowledge and experimental tools for manipulating 1D and 2D materials’ structure, stacking, substrate interaction, defects, and number of layers to realize an entirely new class of ultra‐hard, ultra‐thin, and ultra‐light active materials with ad‐ hoc mechanical properties for a variety of applications.

2D Materials↗
NASA NTRS1972

Fatigue failure of metal components as a factor in civil aircraft accidents

A review of records maintained by the National Transportation Safety Board showed that 16,054 civil aviation accidents occurred in the United States during the 3-year period ending December 31, 1969. Material failure was an important factor in the cause of 942 of these accidents. Fatigue was identified as the mode of the material failures associated with the cause of 155 accidents and in many other accidents the records indicated that fatigue failures might have been involved. There were 27 fatal accidents and 157 fatalities in accidents in which fatigue failures of metal components were definitely identified. Fatigue failures associated with accidents occurred most frequently in landing-gear components, followed in order by powerplant, propeller, and structural components in fixed-wing aircraft and tail-rotor and main-rotor components in rotorcraft. In a study of 230 laboratory reports on failed components associated with the cause of accidents, fatigue was identified as the mode of failure in more than 60 percent of the failed components. The most frequently identified cause of fatigue, as well as most other types of material failures, was improper maintenance (including inadequate inspection). Fabrication defects, design deficiencies, defective material, and abnormal service damage also caused many fatigue failures. Four case histories of major accidents are included in the paper as illustrations of some of the factors invovled in fatigue failures of aircraft components.

Holshouser, W. L.↗
NASA NTRS1999

Method and Apparatus for the Portable Identification of Material Thickness and Defects Using Spatially Controlled Heat Application

A method and a portable apparatus for the nondestructive identification of defects in structures. The apparatus comprises a heat source and a thermal imager that move at a constant speed past a test surface of a structure. The thermal imager is off set at a predetermined distance from the heat source. The heat source induces a constant surface temperature. The imager follows the heat source and produces a video image of the thermal characteristics of the test surface. Material defects produce deviations from the constant surface temperature that move at the inverse of the constant speed. Thermal noise produces deviations that move at random speed. Computer averaging of the digitized thermal image data with respect to the constant speed minimizes noise and improves the signal of valid defects. The motion of thermographic equipment coupled with the high signal to noise ratio render it suitable for portable, on site analysis.

Cramer, K. Elliott↗