SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
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.
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.
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.
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.
Rotating disks undergo rigorous mechanical loading conditions that make them subject to a variety of failure mechanisms leading to structural deformities and cracking. During operation, periodic loading fluctuations and other related factors cause fractures and hidden internal cracks that can only be detected via noninvasive types of health monitoring and/or nondestructive evaluation. These evaluations go further to inspect material discontinuities and other irregularities that have grown to become critical defects that can lead to failure. Hence, the objectives of this work is to conduct a collective analytical and experimental study to present a well-rounded structural assessment of a rotating disk by means of a health monitoring approach and to appraise the capabilities of an in-house rotor spin system. The analyses utilized the finite element method to analyze the disk with and without an induced crack at different loading levels, such as rotational speeds starting at 3000 up to 10 000 rpm. A parallel experiment was conducted to spin the disk at the desired speeds in an attempt to correlate the experimental findings with the analytical results. The testing involved conducting spin experiments which, covered the rotor in both damaged and undamaged (i.e., notched and unnotched) states. Damaged disks had artificially induced through-thickness flaws represented in the web region ranging from 2.54 to 5.08 cm (1 to 2 in.) in length. This study aims to identify defects that are greater than 1.27 cm (0.5 in.), applying available means of structural health monitoring and nondestructive evaluation, and documenting failure mechanisms experienced by the rotor system under typical turbine engine operating conditions.
NASA's BioSentinel mission is one of thirteen secondary payloads to be deployed on the Space Launch System Exploration Mission-1 (SLS EM-1). The BioSentinel nanosatellite will be sent into a heliocentric orbit beyond Low Earth Orbit (LEO), to study the effects of deep space radiation on the budding yeast, Saccharomyces cerevisiae. Ionizing radiation encountered in deep space can create damaging lesions in DNA, including double strand breaks (DSBs). Budding yeast is suitable as a biological model to study these effects, as it is eukaryotic, and can be desiccated for prolonged periods while retaining viability, thus serving as a robust analog for human cells. On the ground, yeast cells are grown in liquid medium, then loaded into the wells of microfluidic cards and air dried prior to integration into the payload. Once the spacecraft reaches its target heliocentric orbit, a mixture of growth medium and metabolic indicator dye will be pumped into the microwells at specific time points to rehydrate the cells and allow them to grow. A 3-color LED detection system will measure changes in growth and metabolism resulting from ionizing radiation exposure. BioSentinel contains a wild type control strain and a rad51 mutant that is defective for DNA damage repair. In this study, we will determine the optimal amount of time to grow diploid yeast cells in liquid culture before they are desiccated for space flight. After an extended time in stationary phase, they become more tolerant to desiccation due to stress caused by nitrogen starvation. However, excessive exposure can lead to loss of viability and to a heterogeneous cell population due to sporulation. Since viability loss during desiccation poses a risk to mission success, a stress preconditioning process during initial growth may increase long-term cell viability. To determine the growth period that improves desiccation tolerance but allows for retention of uniform radiation sensitivity, we will grow both strains in liquid medium for a varying number of days (4 to 7), desiccate the cells, and then observe changes to cell viability and ionizing radiation sensitivity over time. Supported by the Space Life Sciences Training Program at NASA Ames Research Center.
Deep level defect introduction and carrier removal were characterized using steady-state photocapacitance (SSPC), deep level transient spectroscopy (DLTS) and lighted capacitance–voltage for proton irradiated n-type Al 0.70 Ga 0.30 N Schottky diodes grown by metal-organic vapor phase epitaxy on AlN-on-sapphire templates. SSPC observed deep levels in the as-grown diode with zero-phonon transition energies of 2.20, 2.65, 3.10, 3.40, and 4.65 eV relative to the conduction band minimum (E c ), and an additional deep level emerged at 1.20 eV with irradiation. Lighted capacitance–voltage measurements quantified the deep level concentration (N t ) of states detected by SSPC, and it was observed that N t increased with proton fluence only for the 1.2 and 4.65 eV levels. Carrier removal was much larger than the increase in N t of the 1.2 and 4.65 eV deep levels, suggesting that radiation-induced deep level compensators existed beyond what was detected with SSPC. DLTS detected additional, proton-induced deep acceptors at E c —0.55, 0.82, and 1.16 eV, the latter of which is likely the same 1.20 eV deep state observed by SSPC. The concentration of the E c —0.82 eV defect state was large enough to reconcile carrier removal with total deep level introduction. Comparing the E c —0.82 and 1.16 eV deep acceptor levels to previous experimental and theoretical reports suggests that their atomistic origins could be the nitrogen vacancy (V N ) and oxygen substituting on the nitrogen sub-lattice (O N ), respectively. This defect behavior contrasts starkly with GaN, where V N and O N are shallow donors, and demonstrates that the electronic properties of defects can evolve drastically within the AlGaN alloy system ranging from wide bandgap GaN to ultra-wide bandgap AlN.
This report provides a description and assessment of recent efforts to couple acoustic-based experimental measurements and characterization with machine learning models in order to enhance structural health monitoring capabilities for nuclear microreactors. With resilient embedded sensors in development by others supported by programs funded by the US Department of Energy’s Office of Nuclear Energy, the work described herein builds upon ongoing efforts to improve non-destructive testing technology that relates measured acoustic signatures to component stresses and/or structural defects, using a combination of new experimental measurements and machine learning architectures. The experimental procedure remained similar to that developed for the previous year’s demonstration of damage detection by the authors, with the same damaged sample tested under similar applied stress conditions. Notably, a new mounting fixture was designed and implemented to improve measurement consistency and a more sophisticated laser Doppler vibrometer was employed to make high-fidelity vibration measurements. Two nominally identical sets of training data were collected for each experimental setup to better understand the repeatability of the experiment and to better test the generality of trained neural network models. Additionally, we obtained new high-quality 3D mode shapes of the damaged test article at various stress and excitation levels, providing greater insights into the physical response of the sample during testing. Previously, we demonstrated that a machine learning model based on a convolutional neural network can predict structural details of an artificially introduced interface (intact, rough cut, smooth cut), and the applied torque level. In this study, we have transitioned to graph-based neural network architectures to better develop and test a flexible framework that is more suitable to being transferred away from controlled benchtop experiments and into more applied settings where less-structured data inputs may be expected. In general, performance testing of a graph neural network on frequency-domain representations of the data indicates strong and consistent identification of test conditions for datasets recorded on damaged components. With goals of predicting damage location and other changing experimental conditions using limited datasets, predictive models using a graph neural network architecture correctly predicted the applied torque level with an accuracy of 85% using only a single measurement point and predicted within one torque level in 95% of test windows. Predictions of damage location had limited success due to the symmetry and minimal number of the damage scenarios presented during model training. Results were ambiguous as to whether the model could detect the location of the artificial damage, or if it was instead learning the location of a given measurement point on the part and subsequently detecting which points were closest to the location of the damage. This finding will be factored into upcoming planned work on damaged graphite components, where new experimental tests with a larger number and variety of damage scenarios are expected to provide improved validation of recent developments in monitoring methodology.
The nondestructive tests applied to Scout rocket motors were reviewed and appraised. Analytical techniques were developed to evaluate the capabilities of the radiographic and ultrasonic procedures used. Major problem areas found were the inadequacy of high voltage radiography for detecting unbonds and propellant cracks having narrow widths, the inability to relate the ultrasonic signals received from flat-bottomed holes in standards to those received from real defects and in the general area of the specification of acceptance criteria and how these were to be met. To counter the deficiencies noted, analyses were conducted to the potential utility of radiometric, acoustic, holographic and thermographic techniques for motor and nozzle bond inspection, a new approach to qualifying magnetic particle inspection and the application of acoustic emission analysis to the evaluation of proof and leak test data.