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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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Outputs of defective sensors simulated for studies of reliability of control systems. Real-time sensor-failure simulator (SFS) designed and built for use with Advance Detection, Isolation, and Accommodation (ADIA) program. Equipment consists of IBM PC/XT computer and associated analog circuitry. User defines failure scenarios to determine which sensor signals fail and method(s) used to simulate failure.
Papers on optical pattern recognition are presented, covering topics such as the estimation of satellite pose and motion parameters using a neural net tracker, associative memory, optical implmentation of programmable neural networks, optoelectronic neural networks, dynamic autoassociative neural memory, heteroassociative memory, bilinear pattern recognition processors, optical processing of optical correlation plane data, and a synthetic discriminant function-based nonlinear optical correlator. Other topics include an interactive optical-digital image processor, geometric transformations for video compression and human teleoperator display, quasiconformal remapping for compensation of human visual field defects, hybrid vision for automated spacecraft landing, advanced symbolic and inference optical correlation filters, and a rotationally invariant holographic tracking system. Additional topics include the detection of rotational and scale-varying objects with a programmable joint transform correlator, a single spatial light modulator binary nonlinear optical correlator, optical joint transform correlation, linear phase coefficient composite filters, and binary phase-only filters.
Static Code Analysis (SCA) is a vital component of NASA IV&V’s mission assurance for safety-critical software as it reduces the likelihood of software-induced hazards impacting mission success. Static Code Analysis achieves this by identifying hazards that may not have been otherwise detectable by typical code reviews or other testing. Using SCA tools, however, can be intimidating due to steep learning curves, especially considering tool performance and defect coverage varies greatly. Because of this variation amongst SCA tools, understanding which tools support certain defects and which do not, as well as understanding how to run an analysis based on steps that are unique to each tool, can be difficult to both new and experienced analysts alike. To mitigate this, the SCAWG or the IV&V Static Code Analysis Working Group, created the SCA Checker Taxonomy and Starting Point Profiles. The Checker Selection Tool (CST) incorporates these two SCAWG products into an interactive tool which allows the user to: select organized categories of defects they would like the SCA tools to discover, select default checkers depending on their mission type (e.g. flight), and configure multiple SCA tools at once. C/C++, Java, and Python defect checkers from four common SCA tools were utilized in this iteration of the CST. This iteration also includes the addition of training, SCA tool specific help, and taxonomy guide links, into its design to help users new to Static Code Analysis learn how to perform SCA more efficiently. The CST has been subject to beta testing by experienced static code analysts from the SCAWG to ensure a usable and accurate final product. The implications of the CST in the mission assurance of NASA safety-critical software are profound, as the CST can help identify and reduce false positives and false negatives, fundamentally improving overall SCA efficiency and accuracy.
New rotorcraft structural composite designs incorporate lower structural weight, reduced manufacturing complexity, and improved threat protection. These new structural concepts require nondestructive evaluation inspection technologies that can potentially be field-portable and able to inspect complex geometries for damage or structural defects. Two candidate technologies were considered: Thermography and Laser-Based Ultrasound (Laser UT). Thermography and Laser UT have the advantage of being non-contact inspection methods, with Thermography being a full-field imaging method and Laser UT a point scanning technique. These techniques were used to inspect composite samples that contained both embedded flaws and impact damage of various size and shape. Results showed that the inspection techniques were able to detect both embedded and impact damage with varying degrees of success.
The sensing of stress under harsh environmental conditions with high resolution has critical importance for a range of applications including earth’s subsurface scanning, geological CO2 storage monitoring, and mineral and resource recovery. Using a first-principles density functional theory (DFT) approach combined with the theoretical modelling of the low-energy Hamiltonian, here, we investigate a novel approach to detect unprecedented levels of pressure by taking advantage of the solid-state electronic spin of nitrogen-vacancy (NV) centers in diamond. We computationally explore the effect of strain on the defect band edges and band gaps by varying the lattice parameters of a diamond supercell hosting a single NV center. A low-energy Hamiltonian is developed that includes the effect of stress on the energy level of a ±1 spin manifold at the ground state. By quantifying the energy level shift and split, we predict pressure sensing of up to 0.3 MPa/Hz using the experimentally measured spin dephasing time. We show the superiority of the quantum sensing approach over traditional optical sensing techniques by discussing our results from DFT and theoretical modelling for the frequency shift per unit pressure. Importantly, we propose a quantum manometer that could be useful to measure earth’s subsurface vibrations as well as for pressure detection and monitoring in high-temperature superconductivity studies and in material sciences. Our results open avenues for the development of a sensing technology with high sensitivity and resolution under extreme pressure limits that potentially has a wider applicability than the existing pressure sensing technologies.
This study introduces the integration of dynamic computer vision–enabled imaging with electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). This approach involves real-time discovery and analysis of atomic structures as they form, allowing us to observe the evolution of material properties at the atomic level, capturing transient states traditional techniques often miss. Rapid object detection and action system enhances the efficiency and accuracy of STEM-EELS by autonomously identifying and targeting only areas of interest. This machine learning (ML)–based approach differs from classical ML in that it must be executed on the fly, not using static data. We apply this technology to V-doped MoS 2 , uncovering insights into defect formation and evolution under electron beam exposure. This approach opens uncharted avenues for exploring and characterizing materials in dynamic states, offering a pathway to increase our understanding of dynamic phenomena in materials under thermal, chemical, and beam stimuli.
The demand for sub-ppm hydrogen (H 2 ) sensing is growing across emerging applications such as environmental monitoring, breath-based disease diagnostics, and early-stage battery failure detection. However, achieving reliable ppb-level detection with chemiresistive metal oxide sensors remains challenging. At trace gas concentrations, resistance modulation is often insufficient, particularly in the absence of noble metal catalysts. Here, we report samarium-doped tin dioxide (Sm-SnO 2 ) nanofibers in which electronic trap-state modulation is exploited to enable ultrasensitive hydrogen sensing. The 2 at% Sm-doped SnO 2 nanofibers exhibited markedly enhanced H 2 sensitivity, achieving clear detection down to 25 ppb H 2 at 200 °C, with a theoretical limit of detection of 4.5 ppb, placing this material among the most sensitive noble-metal-free SnO 2 -based H 2 sensors reported to date. Mechanistic investigations through X-ray photoelectron spectroscopy and electron energy loss spectroscopy revealed that Sm 3+ doping introduces deep trap states associated with charge-compensating defect complexes. These states reduce free carrier density, increase baseline resistance, and enable trap-assisted charge release during H 2 exposure, thereby amplifying the sensing response. Trap-state engineering via rare-earth doping, exemplified by Sm-SnO 2 , provides an effective pathway for achieving ppb-level hydrogen detection in noble-metal-free chemiresistive sensors.
We report the effect of EH 6/7 electron trap centers alone on the performance of high-resolution radiation detectors fabricated on n-type 4H–SiC epitaxial layers. A Schottky barrier detector (SBD) and a metal-oxide-semiconductor (MOS) capacitor detector fabricated using two sister samples derived from the same 50 μm 4H–SiC parent wafer exhibited widely different energy resolutions of 0.4% and 0.9% for 5486 keV alpha particles. An equivalent noise charge model analysis ruled out the effect of the detector capacitance and the leakage current on the resolution of the detectors. Deep level transient spectroscopic studies revealed the presence of two trapping centers in each detector within the temperature scan range 240–800 K. The Z 1/2 center, a potential electron trap, was detected in both the detectors in equal concentration, which suggested that the observed difference in the energy resolution is due to the presence of the other defect, the EH 6/7 center, in the SBD. The capture cross section of the EH 6/7 center was calculated to be three orders of magnitude higher than the second defect [a carbon antisite vacancy (CAV) center] observed in the MOS detector with an activation energy of 1.10 eV, which accounted for the enhanced electronic trapping in the SBD leading to its poor energy resolution. It has been proposed that the EH 6/7 centers in the SBD have likely been reconfigured to CAV pairs during the thermal growth of the silicon dioxide layer in the MOS detector. The proposed formation mechanism of CAV, a stable qubit state for quantum information processing, addresses the outstanding questions related to the role of defect dynamics in their formation.
Neutral silicon-vacancy (Si-V 0 ) centers in diamond are promising candidates for quantum network applications because of their exceptional optical properties and spin coherence. However, the stabilization of Si-V 0 centers requires careful Fermi-level engineering of the diamond host material, making further technological development challenging. Here, we show that Si-V 0 centers can be efficiently stabilized by photoactivated itinerant carriers. Even in this nonequilibrium configuration, the resulting Si-V 0 centers are stable enough to allow for resonant optical excitation and optically detected magnetic resonance. Furthermore, our results pave the way for on-demand generation of Si-V 0 centers as well as other emerging quantum defects in diamond.
We present a study of the Rydberg spectrum in 166 Er for series connected to the 4 f 12 ( 3 H 6 )6s, J c = 13/2 and J c = 11/2 ionic core states using an all-optical detection based on electromagnetically induced transparency in an effusive atomic beam. Identifying approximately 550 individual states, we find good agreement with a multi-channel quantum defect theory (MQDT) which allows assignment of most states to ns or nd Rydberg series. We provide an improved accuracy for the lowest two ionization thresholds to E IP, J c =13/2 = 49260:750(1) cm -1 and E IP, J c =11/2 = 49701:184(1) cm -1 as well as the corresponding quantum defects for all observed series. We identify Rydberg states in five different isotopes, and states between the two lowest ionization thresholds. Our results open the way for future applications of Rydberg states for quantum simulation using erbium and exploiting its special open-shell structure.
Flat panels simulating internally cooled regenerative thrust chamber walls were fabricated by electroforming, brazing and diffusion bonding to evaluate the feasibility of nondestructive evaluation techniques to detect bonds of various strength integrities. Ultrasonics, holography, and acoustic emission were investigated and found to yield useful and informative data regarding the presence of bond defects in these structures.
Scanning laser acoustic microscopy finds flaws in ceramic specimens. Laser light used to detect distortions, on angstrom scale, produced on surface of specimen by ultrasonic waves transmitted through specimen. From distortion, SLAM creates picture on video monitor of such defects as voids, inclusions, and cracks.
In this project, the emission of electrons during laser powder bed fusion additive manufacturing was identified and investigated to resolve the underlying dynamics driven by laser-material interactions. Laser powder bed fusion involves the selective laser melting of metal powder particles layer by layer which fuse together forming a larger 3D structure. Laser-based additive manufacturing approaches such as laser powder bed fusion hold the potential to revolutionize manufacturing of complex metal components in the aerospace, medical, and automotive industries. However, the difficulty in producing defect-free components by metal 3D printing is a major hurdle for widespread commercial adoption. This project resolved important dynamics in laser powder bed fusion including the identification of plasma formation mechanisms, transitions between laser-induced melting modes and stochastic precursors to defect formation. These findings were realized through implementation of electronic sensing diagnostics into an existing testbed system and harnessing capabilities developed for the time dependent analysis of laser powder bed fusion datasets. Importantly, the results advance our understanding of laser-material interactions and reveal that detection of thermionic emission can also resolve information critical to optimization of the laser powder bed fusion fabrication process. The findings are critical for advancing our fundamental understanding of the laser powder bed fusion process and can help assure the requisite fidelity of fabricated components.
A two-dimensional model for in-plane vibrations of a cantilever plate with a non-symmetrical damage is used in the context of defect identification in materials with piezoelectric ceramic patches bonded to their surface. These patches can act both as actuators and sensors in a self-analyzing fashion, which is a characteristic of smart materials. A Galerkin method is used to approximate the dynamic response of these structures. The natural frequency shifts due to the damage are estimated numerically and compared to experimental data obtained from tests on cantilever aluminum plate-like structures damaged at different locations with defects of different depths. The damage location and extent are determined by an enhanced least square identification method. Efficacy of the frequency shift based algorithms is demonstrated using experimental data.
Uncontained engine failures, although rare in occurrence, can have a catastrophic effect on aircraft performance and safety. Engine disk cracks can eventually lead to these type of failures. A number of techniques to detect engine disk cracks have been developed in recent years. However, these technologies have only been validated by disk spin pit tests, not actual engine tests. Due to this, a project was established to perform seeded fault engine tests on a TF41 engine disk fan. A defect was machined in the first stage fan disk of a TF41 engine. The disk was run in a spin pit to initiate a crack. Once initiated, the disk was run in an actual engine test facility. The engine was cycled by a number of start and stops with the goal of propagating the crack to disk burst through low cycle fatigue. Various crack detection techniques were installed on the engine and run real-time during the test to validate their abilities to detect disk cracks. These techniques were based on methods such as change in mass imbalance using vibration or shaft displacement, change in blade position, acoustic emission, and torsional resonance. At the completion of 4474 test cycles, the crack in the TF41 disk was determined to have grown approximately 0.025 inches. This was far less the predicted crack growth based on a fracture mechanics analysis and finite element stress analysis.
Imaging nanoscale radiation-induced defects using the transmission electron microscope (TEM) is a key factor in the successful implementation of materials for nuclear energy structural applications. Analyzing each defect in a TEM micrograph is currently a manual task. To identify the defects in a single image can take anywhere from 15 min to an hour and a project can require the analysis of anywhere from tens to ≥ 100 images. Here, we use artificial intelligence (AI) models to automate this task. For simplification, we evaluated images with only a single type of defect; helium bubbles. Additionally, we performed semantic segmentation of these helium bubble defects in electron microscopy images of irradiated FeCrAl alloys using a deep learning DefectSegNet model. This model, which was previously used to classify crystal defects, is inspired by the classic DenseNet and U-Net image segmentation models. It claims high spatial resolution, but has poor performance at object boundaries. Our paper improves the DefectSegNet model’s application by adding two new features. First, the DefectSegNet model is applied not only to perform calculation pixel-wise but also object (or feature) wise. Because object-wise metrics are directly relevant to our final goal of detecting bubbles, whereas pixel-wise classification is only an intermediate step, it is an important part of our study. Second, a distance map loss (DML) function has been added to increase its performance at object boundaries. It is crucial to accurately represent defects boundaries, especially bubbles, in order to track the bubble-induced swelling caused by irradiation. The boundary-focused DML function is also compared to other loss functions like Cross-entropy, Weighted Binary Cross Entropy (WBCE), Dice and Intersection over Union (IOU). Finally, by incorporating new features, we found a marked improvement on segmentation quality and better shape preservation at the boundaries and areas of the bubbles.
High-performance microphotodetectors require materials that combine strong light–matter interaction, fast charge transport, and ambient stability. Here, we demonstrate single-nanowire devices based on the 2D perovskite (TPA3) 2 PbBr 4 , synthesized via a controlled slow-cooling self-assembly process that yields defect-minimized, anisotropic nanowires with smooth facets. These microphotodetectors exhibit ultralow dark currents (∼10 –15 A), high responsivity (up to 156 mA W –1 ), and exceptional specific detectivity (∼10 11 Jones) under near-UV (405 nm) illumination, with rise and fall times in the millisecond regime. The superior detectivity is primarily driven by the suppression of thermal noise through the material’s ultralow dark current, while the millisecond temporal response is governed by high-intensity trap-filling dynamics. The devices maintain stable operation over 4000 s of continuous on/off cycling and show remarkable ambient stability over weeks, attributed to dense crystal packing and robust organic cation layers. Furthermore, the influence of nanowire thickness on the charge collection efficiency is systematically elucidated through optical penetration depth analysis, highlighting design principles for optimizing low-dimensional perovskite photodetectors. This study introduces single 2D perovskite nanowires as a versatile platform for miniaturized, high-performance optoelectronic devices.
Acoustic emission technique is described for use as quality control tool in nondestructive inspection of welds. Stress mounts around weld defect until it exceeds yield strength of material. Pressure wave relieving the stress is emitted and followed by oscillations caused by multiple reflections. Acoustic emissions are then detected by sensor similar to ultrasonic sensor.