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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Entropic order parameters in weakly coupled gauge theories

The entropic order parameters measure in a universal geometric way the statistics of non-local operators responsible for generalized symmetries. In this article, we compute entropic order parameters in weakly coupled gauge theories. To perform this computation, the natural route of evaluating expectation values of physical (smeared) non-local operators is prevented by known difficulties in constructing suitable smeared Wilson loops. We circumvent this problem by studying the smeared non-local class operators in the enlarged non-gauge invariant Hilbert space. This provides a generic approach for smeared operators in gauge theories and explicit formulas at weak coupling. In this approach, the Wilson and ’t Hooft loops are labeled by the full weight and co-weight lattices respectively. We study generic Lie groups and discuss couplings with matter fields. Smeared magnetic operators, as opposed to the usual infinitely thin ones, have expectation values that approach one at weak coupling. The corresponding entropic order parameter saturates to its maximum topological value, except for an exponentially small correction, which we compute. On the other hand, smeared ’t Hooft loops and their entropic disorder parameter are exponentially small. We verify that both behaviors match the certainty relation for the relative entropies. In particular, we find upper and lower bounds (that differ by a factor of 2) for the exact coefficient of the linear perimeter law for thin loops at weak coupling. This coefficient is unphysical/non-universal for line operators. We end with some comments regarding the RG flows of entropic parameters through perturbative beta functions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Trajectory Optimization via Unsupervised Probabilistic Learning On Manifolds

This report investigates the use of unsupervised probabilistic learning techniques for the analysis of hypersonic trajectories. The algorithm first extracts the intrinsic structure in the data via a diffusion map approach. Using the diffusion coordinates on the graph of training samples, the probabilistic framework augments the original data with samples that are statistically consistent with the original set. The augmented samples are then used to construct conditional statistics that are ultimately assembled in a path-planing algorithm. In this framework the controls are determined stage by stage during the flight to adapt to changing mission objectives in real-time. A 3DOF model was employed to generate optimal hypersonic trajectories that comprise the training datasets. The diffusion map algorithm identfied that data resides on manifolds of much lower dimensionality compared to the high-dimensional state space that describes each trajectory. In addition to the path-planing worflow we also propose an algorithm that utilizes the diffusion map coordinates along the manifold to label and possibly remove outlier samples from the training data. This algorithm can be used to both identify edge cases for further analysis as well as to remove them from the training set to create a more robust set of samples to be used for the path-planing process.

42 ENGINEERING↗
NASA NTRS2010

Automated, Miniaturized Instrument for Measuring Gene Expression in Space

To facilitate astrobiological studies on the survival and adaptation of microorganisms and mixed microbial cultures to space environment, we have been developing a fully automated, miniaturized system for measuring their gene expression on small spacecraft. This low-cost, multi-purpose instrument represents a major scientific and technological advancement in our ability to study the impact of the space environment on biological systems by providing data on cellular metabolism and regulation orders of magnitude richer than what is currently available. The system supports growth of the organism, lyse it to release the expressed RNA, label the RNA, read the expression levels of a large number of genes by microarray analysis of labeled RNA and transmit the measurements to Earth. To measure gene expression we use microarray technology developed by CombiMatrix, which is based on electrochemical reactions on arrays of electrodes on a semiconductor substrate. Since the electrical integrity of the microarray remains intact after probe synthesis, the circuitry can be employed to sense nucleic acid binding at each electrode. CombiMatrix arrays can be sectored to allow multiple samples per chip. In addition, a single array can be used for several assays. The array has been integrated into an automated microfluidic cartridge that uses flexible reagent blisters and pinch pumping to move liquid reagents between chambers. The proposed instrument will help to understand adaptation of terrestrial life to conditions beyond the planet of origin, identify deleterious effects of the space environment, develop effective countermeasures against these effects, and test our ability to sustain and grow in space organisms that can be used for life support and in situ resource utilization during long-duration space exploration. The instrument is suitable for small satellite platforms, which provide frequent, low cost access to space. It can be also used on any other platform in space, including the ISS. It can be replicated and used with only small modifications in multiple biological experiments with a broad range of goals in mind.

Pohorille, A.↗
DOE OSTI2022

AutoPhaseNN: unsupervised physics-aware deep learning of 3D nanoscale Bragg coherent diffraction imaging

Abstract The problem of phase retrieval underlies various imaging methods from astronomy to nanoscale imaging. Traditional phase retrieval methods are iterative and are therefore computationally expensive. Deep learning (DL) models have been developed to either provide learned priors or completely replace phase retrieval. However, such models require vast amounts of labeled data, which can only be obtained through simulation or performing computationally prohibitive phase retrieval on experimental datasets. Using 3D X-ray Bragg coherent diffraction imaging (BCDI) as a representative technique, we demonstrate AutoPhaseNN, a DL-based approach which learns to solve the phase problem without labeled data. By incorporating the imaging physics into the DL model during training, AutoPhaseNN learns to invert 3D BCDI data in a single shot without ever being shown real space images. Once trained, AutoPhaseNN can be effectively used in the 3D BCDI data inversion about 100× faster than iterative phase retrieval methods while providing comparable image quality.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Translation and rotation equivariant normalizing flow (TRENF) for optimal cosmological analysis

ABSTRACT Our Universe is homogeneous and isotropic, and its perturbations obey translation and rotation symmetry. In this work, we develop translation and rotation equivariant normalizing flow (TRENF), a generative normalizing flow (NF) model which explicitly incorporates these symmetries, defining the data likelihood via a sequence of Fourier space-based convolutions and pixel-wise non-linear transforms. TRENF gives direct access to the high dimensional data likelihood p(x|y) as a function of the labels y, such as cosmological parameters. In contrast to traditional analyses based on summary statistics, the NF approach has no loss of information since it preserves the full dimensionality of the data. On Gaussian random fields, the TRENF likelihood agrees well with the analytical expression and saturates the Fisher information content in the labels y. On non-linear cosmological overdensity fields from N-body simulations, TRENF leads to significant improvements in constraining power over the standard power spectrum summary statistic. TRENF is also a generative model of the data, and we show that TRENF samples agree well with the N-body simulations it trained on, and that the inverse mapping of the data agrees well with a Gaussian white noise both visually and on various summary statistics: when this is perfectly achieved the resulting p(x|y) likelihood analysis becomes optimal. Finally, we develop a generalization of this model that can handle effects that break the symmetry of the data, such as the survey mask, which enables likelihood analysis on data without periodic boundaries.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2015

Compendium of Current Single Event Effects for Candidate Spacecraft Electronics for NASA

NASA spacecraft are subjected to a harsh space environment that includes exposure to various types of ionizing radiation. The performance of electronic devices in a space radiation environment are often limited by their susceptibility to single event effects (SEE). Ground-based testing is used to evaluate candidate spacecraft electronics to determine risk to spaceflight applications. Interpreting the results of radiation testing of complex devices is and adequate understanding of the test condition is critical. Studies discussed herein were undertaken to establish the application-specific sensitivities of candidate spacecraft and emerging electronic devices to single-event upset (SEU), single-event latchup (SEL), single-event gate rupture (SEGR), single-event burnout (SEB), and single-event transient (SET). For total ionizing dose (TID) and displacement damage dose (DDD) results, see a companion paper submitted to the 2015 Institute of Electrical and Electronics Engineers (IEEE) Nuclear and Space Radiation Effects Conference (NSREC) Radiation Effects Data Workshop (REDW) entitled "compendium of Current Total Ionizing Dose and Displacement Damage for Candidate Spacecraft Electronics for NASA by M. Campola, et al.

spacecraft electronics↗
DOE OSTI2024

Topological Interpretability for Deep Learning

With the growing adoption of AI-based systems across everyday life, the need to understand their decision-making mechanisms is correspondingly increasing. The level at which we can trust the statistical inferences made from AI-based decision systems is an increasing concern, especially in high-risk systems such as criminal justice or medical diagnosis, where incorrect inferences may have tragic consequences. Despite their successes in providing solutions to problems involving real-world data, deep learning (DL) models cannot quantify the certainty of their predictions. These models are frequently quite confident, even when their solutions are incorrect. This work presents a method to infer prominent features in two DL classification models trained on clinical and non-clinical text by employing techniques from topological and geometric data analysis. We create a graph of a model's feature space and cluster the inputs into the graph's vertices by the similarity of features and prediction statistics. We then extract subgraphs demonstrating high-predictive accuracy for a given label. These subgraphs contain a wealth of information about features that the DL model has recognized as relevant to its decisions. We infer these features for a given label using a distance metric between probability measures, and demonstrate the stability of our method compared to the LIME and SHAP interpretability methods. This work establishes that we may gain insights into the decision mechanism of a DL model. This method allows us to ascertain if the model is making its decisions based on information germane to the problem or identifies extraneous patterns within the data.

Spannaus, Adam↗
DOE OSTI2026

Deep-ultraviolet ptychographic pocket-scope (DART): mesoscale lensless molecular imaging with label-free spectroscopic contrast

The mesoscale characterization of biological specimens has traditionally required compromises between resolution, field-of-view, depth-of-field, and molecular specificity, with most approaches relying on external labels. Here we present the Deep-ultrAviolet ptychogRaphic pockeT-scope (DART), a handheld platform that transforms label-free molecular imaging through intrinsic deep-ultraviolet spectroscopic contrast. By leveraging biomolecules’ natural absorption fingerprints and combining them with lensless ptychographic microscopy, DART resolves down to 308-nm linewidths across centimeter-scale areas while maintaining millimeter-scale depth-of-field. The system’s virtual error-bin methodology effectively eliminates artifacts from limited temporal coherence and other optical imperfections, enabling high-fidelity molecular imaging without lenses. Through differential spectroscopic imaging at deep-ultraviolet wavelengths, DART quantitatively maps nucleic acid and protein distributions with femtogram sensitivity, providing an intrinsic basis for explainable virtual staining. We demonstrate DART’s capabilities through imaging of tissue sections, cytopathology specimens, blood cells, and neural populations, revealing detailed molecular contrast without external labels. The combination of high-resolution molecular mapping and broad mesoscale imaging in a portable platform opens new possibilities from rapid clinical diagnostics, tissue analysis, to biological characterization in space exploration.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2003

Foliage discrimination using a rotating ladar

We present a real time algorithm that detects foliage using range from a rotating laser. Objects not classified as foliage are conservatively labeled as non-driving obstacles. In contrast to related work that uses range statistics to classify objects, we exploit the expected localities and continuities of an obstacle, in both space and time. Also, instead of attempting to find a single accurate discriminating factor for every ladar return, we hypothesize the class of some few returns and then spread the confidence (and classification) to other returns using the locality constraints. The Urbie robot is presently using this algorithm to descriminate drivable grass from obstacles during outdoor autonomous navigation tasks.

Autonomous navigation range robots↗
NASA NTRS2007

Radiation Test Challenges for Scaled Commerical Memories

As sub-100nm CMOS technologies gather interest, the radiation effects performance of these technologies provide a significant challenge. In this talk, we shall discuss the radiation testing challenges as related to commercial memory devices. The focus will be on complex test and failure modes emerging in state-of-the-art Flash non-volatile memories (NVMs) and synchronous dynamic random access memories (SDRAMs), which are volatile. Due to their very high bit density, these device types are highly desirable for use in the natural space environment. In this presentation, we shall discuss these devices with emphasis on considerations for test and qualification methods required.

LaBel, Kenneth A.↗
NASA NTRS2010

Memories and NASA Spacecraft: A Description of Memories, Radiation Failure Modes, and System Design Considerations

As NASA has evolved it's usage of spaceflight computing, memory applications have followed as well. In this slide presentation, the history of NASA's memories from magnetic core and tape recorders to current semiconductor approaches is discussed. There is a brief description of current functional memory usage in NASA space systems followed by a description of potential radiation-induced failure modes along with considerations for reliable system design.

LaBel, Kenneth A.↗
NASA NTRS2010

Memory Overview - Technologies and Needs

As NASA has evolved it's usage of spaceflight computing, memory applications have followed as well. In this talk, we will discuss the history of NASA's memories from magnetic core and tape recorders to current semiconductor approaches. We will briefly describe current functional memory usage in NASA space systems followed by a description of potential radiation-induced failure modes along with considerations for reliable system design.

LaBel, Kenneth A.↗
NASA NTRS2023

The Living With a Star Space Environment Testbed Payload

The objectives, instrumentation, methods and data leading up to launch of the NASA Living With a Star (LWS) Space Environment Testbed (SET) payload onboard the Air Force Research Laboratory Demonstration and Science Experiments (DSX) spacecraft are described. The experiments characterize the space radiation environment and how it affects hardware performance. The payload consists of a compact space weather instrument and a carrier containing four board experiments.

Demonstration and Science Experiments (DSX)↗
NASA NTRS1984

The temporal response of bone to unloading

Rats were suspended by their tails with the forelimbs bearing the weight load to simulate the weightlessness of space flight. Growth in bone mass ceased by 1 week in the hindlimbs and lumbar vertebrae in growing rats, while growth in the forelimbs and cervical vertebrae remained unaffected. The effects of selective skeletal unloading on bone formation during 2 weeks of suspension was investigated using radio iostope incorporation (with Ca-45 and H-3 proline) and histomorphometry (with tetracycline labeling). The results of these studies were confirmed by histomorphometric measurements of bone formation using triple tetracycline labeling. This model of simulated weightlessness results in an initial inhibition of bone formation in the unloaded bones. This temporary cessation of bone formation is followed in the accretion of bone mass, which then resumes at a normal rate by 14 days, despite continued skeletal unloading. This cycle of inhibition and resumption of bone formation has profound implication for understanding bone dynamics durng space flight, immobilization, or bed rest and offers an opportunity to study the hormonal and mechanical factors that regulate bone formation.

Globus, R. K.↗