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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Dynamic Characterization of Galfenol

A novel and precise characterization of the constitutive behavior of solid and laminated research-grade, polycrystalline Galfenol (Fe81:6Ga18:4) under under quasi-static (1 Hz) and dynamic (4 to 1000 Hz) stress loadings was recently conducted by the authors. This paper summarizes the characterization by focusing on the experimental design and the dynamic sensing response of the solid Galfenol specimen. Mechanical loads are applied using a high frequency load frame. The dynamic stress amplitude for minor and major loops is 2.88 and 31.4 MPa, respectively. Dynamic minor and major loops are measured for the bias condition resulting in maximum, quasi-static sensitivity. Three key sources of error in the dynamic measurements are accounted for: (1) electromagnetic noise in strain signals due to Galfenol's magnetic response, (2) error in load signals due to the inertial force of fixturing, and (3) time delays imposed by conditioning electronics. For dynamic characterization, strain error is kept below 1.2 % of full scale by wiring two collocated gauges in series (noise cancellation) and through lead wire weaving. Inertial force error is kept below 0.41 % by measuring the dynamic force in the specimen using a nearly collocated piezoelectric load washer. The phase response of all conditioning electronics is explicitly measured and corrected for. In general, as frequency increases, the sensing response becomes more linear due to an increase in eddy currents. The location of positive and negative saturation is the same at all frequencies. As frequency increases above about 100 Hz, the elbow in the strain versus stress response disappears as the active (soft) regime stiffens toward the passive (hard) regime.

Dynamic sensing↗
NASA NTRS1994

A statistical analysis of the broadband 0.1 to 3.5 keV spectral properties of X-ray-selected active galactic nuclei

We survey the broadband spectral properties of approximately 500 X-ray-selected active galactic nuclei (AGNs) observed with the Einstein Observatory. Included in this survey are the approximately 450 AGNs in the Extended Medium Sensitivity Survey (EMSS) of Gioia et al. (1990) and the approximately 50 AGNs in the Ultrasoft Survey of Cordova et al. (1992). We present a revised version of the latter sample, based on the post publication discovery of a software error in the Einstein Rev-1b processing. We find that the mean spectral index of the AGNs between 0.1 and 0.6 keV is softer, and the distribution of indices wider, than previous estimates based on analyses of the X-ray spectra of optically selected AGNs. A subset of these AGNs exhibit flux variabiulity, some on timescales as short as 0.05 days. A correlation between radio and hard X-ray luminosity is confirmed, but the data do not support a correlation between the radio and soft X-ray luminosities, or between radio loudness and soft X-ray spectral slope. Evidence for physically distinct soft and hard X-ray components is found, along with the possibility of a bias in previous optically selected samples toward selection of AGNs with flatter X-ray spectra.

Thompson, R. J.↗
NASA NTRSDate not supplied

Preliminary Application of Formal Verification to An Autonomy Architecture for Unmanned Aircraft

There is a desire to design autonomous systems in such a way that capabilities can be easily added or re- combined to produce new behaviors while preserving their safety properties. ICAROUS, a prototype software architecture for building safety-centric autonomous unmanned aircraft applications, is designed to support this type of extensibility and re-configurability. In ICAROUS, core capabilities are implemented as individual soft- ware services, so that enabling access to new capabilities simply requires adding new services. To make use of these capabilities, ICAROUS includes a specialized service that provides a general framework for config- uring the relative priorities, conditions, and rules that govern how different modules should be engaged and disengaged during flight. The inherent complexity of coordinating multiple modules under changing conditions makes it difficult to determine whether a particular configuration could have erroneous behaviors in certain circumstances. A robust set of integration tests can help discover errors, but testing can only realistically cover a relatively small proportion of total system behaviors. Developing good tests and interpreting the results to pinpoint the cause of errors when they arise can also be very time-consuming. To supplement testing, formal methods can be used to model and analyze complex systems, achieving better coverage and simplifying the process of finding, understanding, and fixing errors. To demonstrate these benefits, this paper explores the ap- plication of formal methods to ICAROUS. In particular, the Spin model checker is used to specify requirements for and model portions of the system, then verify whether the model satisfies the requirements and find and fix errors when it does not.

Formal Methods↗
NASA NTRSDate not supplied

Preliminary Application of Formal Verification to An Autonomy Architecture for Unmanned Aircraft

There is a desire to design autonomous systems in such a way that capabilities can be easily added or re-combined to produce new behaviors while preserving their safety properties. ICAROUS, a prototype software architecture for building safety-centric autonomous unmanned aircraft applications, is designed to support this type of extensibility and re-configurability. In ICAROUS, core capabilities are implemented as individual soft- ware services, so that enabling access to new capabilities simply requires adding new services. To make use of these capabilities, ICAROUS includes a specialized service that provides a general framework for config- uring the relative priorities, conditions, and rules that govern how different modules should be engaged and disengaged during flight. The inherent complexity of coordinating multiple modules under changing conditions makes it difficult to determine whether a particular configuration could have erroneous behaviors in certain circumstances. A robust set of integration tests can help discover errors, but testing can only realistically cover a relatively small proportion of total system behaviors. Developing good tests and interpreting the results to pinpoint the cause of errors when they arise can also be very time-consuming. To supplement testing, formal methods can be used to model and analyze complex systems, achieving better coverage and simplifying the process of finding, understanding, and fixing errors. To demonstrate these benefits, this paper explores the ap- plication of formal methods to ICAROUS. In particular, the Spin model checker is used to specify requirements for and model portions of the system, then verify whether the model satisfies the requirements and find and fix errors when it does not.

Formal Methods↗
NASA NTRS2011

Radiation-Tolerant Intelligent Memory Stack - RTIMS

This innovation provides reconfigurable circuitry and 2-Gb of error-corrected or 1-Gb of triple-redundant digital memory in a small package. RTIMS uses circuit stacking of heterogeneous components and radiation shielding technologies. A reprogrammable field-programmable gate array (FPGA), six synchronous dynamic random access memories, linear regulator, and the radiation mitigation circuits are stacked into a module of 42.7 42.7 13 mm. Triple module redundancy, current limiting, configuration scrubbing, and single- event function interrupt detection are employed to mitigate radiation effects. The novel self-scrubbing and single event functional interrupt (SEFI) detection allows a relatively soft FPGA to become radiation tolerant without external scrubbing and monitoring hardware

Ng, Tak-kwong↗
NASA NTRS2017

A Comparison of the SOCIT and DebriSat Experiments

This paper explores the differences between, and shares the lessons learned from, two hypervelocity impact experiments critical to the update of orbital debris environment models. The procedures and processes of the fourth Satellite Orbital Debris Characterization Impact Test (SOCIT) were analyzed and related to the ongoing DebriSat experiment. SOCIT was the first hypervelocity impact test designed specifically for satellites in Low Earth Orbit (LEO). It targeted a 1960's U.S. Navy satellite, from which data was obtained to update pre-existing NASA and DOD breakup models. DebriSat is a comprehensive update to these satellite breakup models- necessary since the material composition and design of satellites have evolved from the time of SOCIT. Specifically, DebriSat utilized carbon fiber, a composite not commonly used in satellites during the construction of the US Navy Transit satellite used in SOCIT. Although DebriSat is an ongoing activity, multiple points of difference are drawn between the two projects. Significantly, the hypervelocity tests were conducted with two distinct satellite models and test configurations, including projectile and chamber layout. While both hypervelocity tests utilized soft catch systems to minimize fragment damage to its post-impact shape, SOCIT only covered 65% of the projected area surrounding the satellite, whereas, DebriSat was completely surrounded cross-range and downrange by the foam panels to more completely collect fragments. Furthermore, utilizing lessons learned from SOCIT, DebriSat's post-impact processing varies in methodology (i.e., fragment collection, measurement, and characterization). For example, fragment sizes were manually determined during the SOCIT experiment, while DebriSat utilizes automated imaging systems for measuring fragments, maximizing repeatability while minimizing the potential for human error. In addition to exploring these variations in methodologies and processes, this paper also presents the challenges DebriSat has encountered thus far and how they were addressed. Accomplishing DebriSat's goal of collecting 90% of the debris, which constitutes well over 100,000 fragments, required addressing many challenges stemming from the very large number of fragments. One of these challenges arose in identifying the foam-embedded fragments. DebriSat addressed this by X-raying all of the panels once the loose debris were removed, and applying a detection algorithm developed in-house to automate the embedded fragment identification process. It is easy to see how the amount of data being compiled would be outstanding. Creating an efficient way to catalog each fragment, as well as archiving the data for reproducibility also posed a great challenge for DebriSat. Barcodes to label each fragment were introduced with the foresight that once the characterization process began, the datasheet for each fragment would have to be accessed again quickly and efficiently. The DebriSat experiment has benefited significantly by leveraging lessons learned from the SOCIT experiment along with the technological advancements that have occurred during the time between the experiments. The two experiments represent two ages of satellite technology and, together, demonstrate the continuous efforts to improve the experimental techniques for fragmentation debris characterization.

Ausay, Erick↗
DOE OSTI2020

Nucleation, growth, and superlattice formation of nanocrystals observed in liquid cell transmission electron microscopy

This work reviews the advancements and prospects of liquid cell transmission electron microscopy (TEM) imaging and analysis methods in understanding the nucleation, growth, etching, and assembly dynamics of nanocrystals. The bonding of atoms into nanoscale crystallites produces materials with nonadditive properties unique to their size and geometry. The recent application of in situ liquid cell TEM to nanocrystal development has initiated a paradigm shift, (1) from trial-and-error synthesis to a mechanistic understanding of the “synthetic reactions” responsible for the emergence of crystallites from a disordered soup of reactive species (e.g., ions, atoms, molecules) and shape-defined growth or etching; and (2) from post-processing characterization of the nanocrystals’ superlattice assemblies to in situ imaging and mapping of the fundamental interactions and energy landscape governing their collective phase behaviors. Imaging nanocrystal formation and assembly processes on the single-particle level in solution immediately impacts many existing fields, including materials science, nanochemistry, colloidal science, biology, environmental science, electrochemistry, mineralization, soft condensed-matter physics, and device fabrication.

36 MATERIALS SCIENCE↗
NASA NTRS1993

Ultra-high resolution water window x ray microscope optics design and analysis

This project has been focused on the design and analysis of an ultra-high resolution water window soft-x-ray microscope. These activities have been accomplished by completing two tasks contained in the statement of work of this contract. The new results from this work confirm: (1) that in order to achieve resolutions greater than three times the wavelength of the incident radiation, it will be necessary to use spherical mirror surfaces and to use graded multilayer coatings on the secondary in order to accommodate the large variations of the angle of incidence over the secondary when operating the microscope at numerical apertures of 0.35 or greater; (2) that surface contour errors will have a significant effect on the optical performance of the microscope and must be controlled to a peak-to-valley variation of 50-100 A and a frequency of 8 periods over the surface of a mirror; and (3) that tolerance analysis of the spherical Schwarzschild microscope has been shown that the water window operations will require 2-3 times tighter tolerances to achieve a similar performance of operations with 130 A radiation. These results have been included in a manuscript included in the appendix.

Shealy, David L.↗
NASA NTRS1980

Improving the performance of soft decision Viterbi decoding in a non-Gaussian environment through non-linear quantization

The performance of Viterbi decoding in a non-Gaussian environment is investigated using a nonlinear quantization strategy. The channel model consists of a convolutionally encoded BPSK signal transmitted to a satellite where it is corrupted with additive white Gaussian noise and pulsed radio frequency interference (RFI). The resultant signal is then passed through a satellite nonlinearity and transmitted to a ground station where it is coherently detected. Interleaving is assumed in order to make the channel memoryless. The presence of RFI makes the channel statistics non-Gaussian, leading to a nonlinear log-likelihood function. A near optimum quantization scheme is found by maximizing a channel parameter, or by matching the quantizer to the log-likelihood function in a mean square error sense. Bit error rate performance improvement is achieved by using such nonlinear quantization.

Mcgregor, D. N.↗
NASA NTRS1999

Main Geomagnetic Field Models from Oersted and Magsat Data Via a Rigorous General Inverse Theory with Error Bounds

The purpose of the grant was to study how prior information about the geomagnetic field can be used to interpret surface and satellite magnetic measurements, to generate quantitative descriptions of prior information that might be so used, and to use this prior information to obtain from satellite data a model of the core field with statistically justifiable error estimates. The need for prior information in geophysical inversion has long been recognized. Data sets are finite, and faithful descriptions of aspects of the earth almost always require infinite-dimensional model spaces. By themselves, the data can confine the correct earth model only to an infinite-dimensional subset of the model space. Earth properties other than direct functions of the observed data cannot be estimated from those data without prior information about the earth. Prior information is based on what the observer already knows before the data become available. Such information can be "hard" or "soft". Hard information is a belief that the real earth must lie in some known region of model space. For example, the total ohmic dissipation in the core is probably less that the total observed geothermal heat flow out of the earth's surface. (In principle, ohmic heat in the core can be recaptured to help drive the dynamo, but this effect is probably small.) "Soft" information is a probability distribution on the model space, a distribution that the observer accepts as a quantitative description of her/his beliefs about the earth. The probability distribution can be a subjective prior in the sense of Bayes or the objective result of a statistical study of previous data or relevant theories.

Backus, George E.↗
DOE OSTI2025

Fluid Mechanics Challenges in Direct-Ink-Writing Additive Manufacturing

Direct-ink writing (DIW) has rapidly become a versatile 3D fabrication method due to its ability to deposit a wide range of complex fluids into customizable 3D geometries. This review highlights key fundamental fluid mechanics and soft matter challenges across the different stages of the DIW printing process. The rheology of fluids and suspensions governs the flow behavior through narrow nozzles, posing questions about extrudability, confined flow dynamics, and clogging mechanisms. Downstream, the formation and deposition of extruded filaments involve extensional flows and potential instabilities, while postdeposition dynamics introduces complexities related to yield stress and structural stability. These stages are inherently interdependent, as optimizing material composition without considering filament stability risks compromising the final structure. As DIW applications expand through advanced ink formulations, developing fundamental fluid mechanics frameworks is essential to replace trial-and-error approaches with predictive design methodologies to enable more precise control and improved reliability of the printing process.

3D printing↗
NASA NTRS1981

A general analysis of anti-jam communication systems

A general error bound is derived for a general anti-jam communication system which will serve as the basis for evaluating the performance of all such complex communication systems. The two most common spread spectrum techniques, coherent DS/BPSK and noncoherent FH/MFSK, are analyzed. Pulse jamming represents the worst type of jammer for DS/BPSK systems, and several receiver structures against such a jammer are examined. It is found that for low values of chip energy-to-noise ratios of O dB or less there is little difference between having or not having jammer state knowledge with a hard decision receiver. Soft decision receivers are shown to be useless against very narrow pulses without jammer state knowledge. Partial band jammers are close to the worst case jammer for FH/MFSK systems. The conclusions found for these systems are similar to those for the DS/BPSK systems.

Omura, J. K.↗
DOE OSTI2022

Measurement Uncertainty of Highly Asymmetrically Curved Elliptical Mirrors Using Multi-Pitch Slope Stitching Technique

Soft X-ray off-axis elliptical mirrors bring new challenges for X-ray mirror metrology. These highly asymmetrically curved elliptical cylindrical mirrors with a total slope range >10 mrad are extremely challenging to measure. Their total slope range exceeds the measuring range of most angular sensors used for X-ray mirror inspection. To overcome this problem, it is possible to stitch partial slope data by measuring the mirror at different pitch angles (multi-pitch angles). By revisiting the theory of the multi-pitch Nano-accuracy Surface Profiler (NSP), we derive the sampling position error on the mirror surface as a function of the mirror height profile and the measurement error of the pitch rotation center. When measuring “extreme”, highly asymmetrically curved, elliptical mirrors, the calculation of the mirror height profile with iterative reconstruction outperforms the classical “flat assumption” (i.e., assuming that the mirror sag is negligible). As demonstrated by our simulations, a proper tolerance evaluation on the measurement of pitch rotation center is needed to assess the measurement accuracy (systematic error) for these strongly aspherical mirrors using the multi-pitch NSP technique. Taking a real design of an “extreme” elliptical mirror as a case study, we conduct a Monte Carlo simulation to mimic the measurement and characterization process to analyze the impact of several error sources. With the measurement uncertainty of the pitch rotation center, the multi-pitch NSP measurement can estimate the grazing angle θ and the chief ray location x o with their uncertainties, as well as the slope residuals.

36 MATERIALS SCIENCE↗
NASA NTRS2000

XTE J1906+09 Observations with RXTE

The 89 s pulsar XTE J1906+09 was serendipitously discovered in a 1996 observation of the soft gamma repeater SGR 1900+14. Two outbursts, in 1996 September and 1998 September have been reported to date. Since this system appears to be transient, it is likely in a Be/X-ray binary, accreting material from a circumstellar disk around its companion. Preliminary pulse timing analyses of RXTE data from the 1998 September outburst revealed an orbital signature consistent with either (i) a low-eccentricity, short period orbit, similar to OAO 1657-415 or (ii) periastron passage in a longer orbital period, higher eccentricity Be/X-ray binary system. No companion has been identified to date due to the large 2 feet error radius of the current best position. SGR 1900+14 has been regularly monitored with RXTE since 1998. Because XTE J1906+09 is only 33 feet away, it often shares the RXTE/PCA field of view with SGR 1900+14, giving us a large number of archival PCA observations. We will present results of a search of RXTE data for outbursts of XTE 1906+09, including histories of pulse frequency, 2-30 keV intensity, pulse profiles, and energy spectra. Results of a pulse timing analysis of the detected outbursts will also be presented.

Wilson, Colleen A.↗
DOE OSTI2026

Explainable machine learning reveals that local structural motifs encode the thermodynamic state across the CuZr metallic glass-forming range

Metallic glasses derive their properties from the statistics of local atomic motifs rather than from long-range order, yet a quantitative, chemistry-specific link between motif populations and the underlying glassy state has remained elusive. In this work we combine large-scale molecular dynamics, Voronoi tessellation, deep neural networks, and SHapley Additive exPlanations (SHAP) to identify which local structural motifs define the glassy state of Cu—Zr metallic glasses. A dataset of 17,180 atomistic configurations spanning ten compositions (Cu 20 Zr 80 –Cu 80 Zr 20 ) and four quench rates (10 9 –10 12 K/s) is used to train a feed-forward neural network that regresses temperature across the 50–2000 K liquid–supercooled–glass range, achieving a mean absolute error of 19.89 K and R 2 = 0.9974, confirming that the local structural state is faithfully encoded in motif-level structure. SHAP analysis then reveals that a tightly coupled near-icosahedral family of motifs (coordination numbers (CN) 11–13, including the full icosahedron 001200 and its single-atom-perturbation sibling 10930) collectively encodes the thermodynamic state of the system across the full glass-forming range. The CN = 11–13 ordered members carry negative SHAP values at high populations, tracking the most deeply-quenched configurations, while 10930 shows the reversed signature consistent with its role as a soft-spot host whose population shrinks as the icosahedral network deepens. The analysis demonstrates that explainable machine learning can isolate the minimal motif vocabulary defining the glassy state and recovers the near-icosahedral building blocks previously identified by data-driven analyses of Cu—Zr. The approach provides a general, chemistry-specific route for characterizing the structural state of disordered materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Demonstration of reconstruction-free static magnetic control of DIII-D plasma with deep reinforcement learning

This paper presents the development and experimental validation of a reinforcement learning (RL)-based magnetic controller on the DIII-D tokamak. The controller directly maps raw magnetic diagnostic signals to actuator commands, replacing the traditional isoflux control algorithm based on equilibrium reconstruction. Four RL controllers are trained using the Soft Actor–Critic algorithm with an asymmetric Actor–Critic architecture in the NSFsim simulator. All controllers are deployed in the DIII-D Plasma Control System and operated with a 4 kHz feedback loop. Two randomization strategies are evaluated during training: evolving kinetic profiles and fixed kinetic profiles within each episode. The latter approach is found to better capture experimental deviations in the current density profile and to provide overall improved control performance. Robust operation is demonstrated across heating power scans in both L- and H-mode plasmas, as well as during transient events such as L–H transitions and pellet injections. Control errors in plasma shape and radial position remained within 1.5–2.0 cm and 1 cm, respectively. A notable discrepancy was observed in the vertical X-point position, with errors of up to approximately 4 cm, attributed to the current density distribution mismatches between simulations and experiments.

DIII-D↗
DOE OSTI2024

Applying machine learning and quantum chemistry to predict the glass transition temperatures of polymers

Glass transition temperature (T g ) is important for understanding the physical and mechanical properties of a polymer material because it relates to the thermal energy required to transition between a hard glassy state and a soft rubbery one. Over the years, various models have been developed for predicting this thermal property from molecular structure to aid in designing novel polymers in selected classes. This work builds on those efforts by utilizing both machine learning (ML) and quantum chemistry (QC) techniques to develop models that can predict T g values from the molecular structure under different data availability scenarios and for a wide variety of polymer types. For the ML model, a graph convolutional network (GCN) was used to map topological polymer features; this model was trained against a dataset of more than 7500 T g values and resulted in a root mean square error (RMSE) of 38.1 °C. The QC-based regression model was trained on 83 T g values and produced an RMSE of 34.5 °C. In conclusion, this work demonstrated that while both model techniques produce accurate predictions and are suitable for different data availability scenarios, the QC-based regression model offered a more interpretable model framework with significantly less training data.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Laboratory Data on the EUV/Soft-X-Ray Flux Problem of Fe XVIII and Fe XIX

Observations by the spacecraft Extreme Ultraviolet Explorer (EUVE), Far Ultraviolet Spectroscopic Explorer (FUSE), Chandra, and XMM-Newton of Capella (α Aurigae) have encountered problems with the relative intensities of the Fe XVIII and Fe XIX line emission in the soft-X-ray (XUV) and extreme-ultraviolet spectral ranges versus various model predictions based on theoretical atomic data. The reason may either lie in astrophysical phenomena, e.g., at emission or because of absorption by the interstellar medium, in the theoretical atomic data, in one or more spectrometer calibrations, or in the spectral modeling. Here, by measurements using an electron beam ion trap we provide laboratory data obtained under conditions that are reasonably close to stellar emission regions. The laboratory data on Fe XVIII and Fe XIX show line ratios that are rather similar to the observations of Capella. The measurements, therefore, rule out astrophysical phenomena and calibration errors and point to issues with the modeling of the observed Capella emission.

79 ASTRONOMY AND ASTROPHYSICS↗