- API data.nasa.gov | Last Updated 2020-01-29T05:06:12.000Z
This NASA Phase I SBIR program would develop and demonstrate radiation hardened nanobridge based non-volatile memory (NVM) for space applications. Specifically, we would combine advances in the resistive memory materials, including solid electrolytes, metal oxides, and metal oxide composites, with atomic layer deposition (ALD) and interference lithography patterning (ILP) techniques, to realize the radiation hardened NVM devices and arrays with high reliability.
- API data.nasa.gov | Last Updated 2020-01-29T01:49:08.000Z
This SBIR Phase II proposal overcomes technology barriers for developing highly efficient all electric aircraft systems for the future, with limited impact to the environment. Turboelectric propulsion for aircraft applications is envisioned, and cryogenic and superconducting components are sought. In particular, low AC loss superconducting wires for the stator windings and superconducting wires with filaments less than 10 micrometers in diameter are of interest. There is an intense push in the aircraft industry to ultimately develop an all-electric aircraft, with liquid hydrogen and fuel cells being considered as the prime generation source for aircraft propulsion. The U.S. is in competition with Europe for the development the next generation all-electric aircraft. Superconductivity especially magnesium diboride (MgB2) superconductors are considered an enabling technology that is being investigated by NASA, Air Force, Rolls-Royce, Airbus and EADS. This means the need for a low cost, low AC loss (fine filament superconductor) that can operate in the 10-25K temperature range in 0-2 tesla fields for superconducting stators for motors and generators. This wire is need by 2016-2017 time frame so all cryogenic motors and generators can fabricated and tested in the NASA test bed. In the Phase I Hyper Tech has shown that fine filament MgB2 wires can be fabricated and there is potential for low AC losses in the 60-400 Hz range for stators. In the Phase II Hyper Tech will continue to work on developing, manufacturing, and testing fine filament MgB2 wire. The wires will also be twisted to reduce coupling losses. The wires will be tested for their superconductor and engineering current density and AC losses. The result of this work will be a low AC loss MgB2 superconductor wire for enabling all-electric aircraft development and allow the U.S. industry to lead the world in this needed and rapid developing technology.
- API data.nasa.gov | Last Updated 2020-01-29T04:05:20.000Z
Damage characterization through wave propagation and scattering is of considerable interest to many non-destructive evaluation techniques. For fiber-reinforced composites, complex waves can be generated during the tests due to the non-homogeneous and anisotropic nature of the material when compared to isotropic materials. Additional complexities are introduced due to the presence of the damage and thus results in difficulty to characterize these defects. The inability to detect damage in composite structures limits their use in practice. A major task of structural health monitoring is to identify and characterize the existing defects or defect evolution through the interactions between structural features and multidisciplinary physical phenomena. In a wave-based approach to addressing this problem, the presence of damage is characterized by the changes in the signature of the resultant wave that propagates through the structure. In order to measure and characterize the wave propagation, we use the response of the surface-mounted piezoelectric transducers as input to an advanced machine-learning based classifier known as a support vector machine.
- API data.nasa.gov | Last Updated 2020-01-29T01:46:30.000Z
Anomaly detection has recently become an important problem in many industrial and financial applications. In several instances, the data to be analyzed for possible anomalies is located at multiple sites and cannot be merged due to practical constraints such as bandwidth limitations and proprietary concerns. At the same time, the size of data sets affects prediction quality in almost all data mining applications. In such circumstances, distributed data mining algorithms may be used to extract information from multiple data sites in order to make better predictions. In the absence of theoretical guarantees, however, the degree to which data decentralization affects the performance of these algorithms is not known, which reduces the data providing participants' incentive to cooperate.This creates a metaphorical 'prisoners' dilemma' in the context of data mining. In this work, we propose a novel general framework for distributed anomaly detection with theoretical performance guarantees. Our algorithmic approach combines existing anomaly detection procedures with a novel method for computing global statistics using local sufficient statistics. We show that the performance of such a distributed approach is indistinguishable from that of a centralized instantiation of the same anomaly detection algorithm, a condition that we call zero information loss. We further report experimental results on synthetic as well as real-world data to demonstrate the viability of our approach. The remaining content of this presentation is presented in Fig. 1.
- API data.nasa.gov | Last Updated 2020-01-29T04:36:17.000Z
UNDERSTANDING SEVERE WEATHER PROCESSES THROUGH SPATIOTEMPORAL RELATIONAL RANDOM FORESTS AMY MCGOVERN, TIMOTHY SUPINIE, DAVID JOHN GAGNE II, NATHANIEL TROUTMAN, MATTHEW COLLIER, RODGER A. BROWN, JEFFREY BASARA, AND JOHN K. WILLIAMS Abstract. Major severe weather events can cause a significant loss of life and property. We seek to revolutionize our understanding of and ability to predict such events through the mining of severe weather data. Because weather is inherently a spatiotemporal phenomenon, mining such data requires a model capable of representing and reasoning about complex spatiotemporal dynamics, including temporally and spatially varying attributes and relationships. We introduce an augmented version of the Spatiotemporal Relational Random Forest, which is a Random Forest that learns with spatiotemporally varying relational data. Our algorithm maintains the strength and performance of Random Forests but extends their applicability, including the estimation of variable importance, to complex spatiotemporal relational domains. We apply the augmented Spatiotemporal Relational Random Forest to three severe weather data sets. These are: predicting atmospheric turbulence across the continental United States, examining the formation of tornadoes near strong frontal boundaries, and understanding the translation of drought across the southern plains of the United States. The results on such a wide variety of real-world domains demonstrate the extensive applicability of the Spatiotemporal Relational Random Forest. Our long-term goal is to significantly improve the ability to predict and warn about severe weather events.
Voluntary Consensus Organization Standards for Nondestructive Evaluation of Aerospace Materials (including Additive Manufactured Parts)data.nasa.gov | Last Updated 2020-01-29T03:14:48.000Z
<p>This NASA-industry effort accomplishes the following:</p><p>1) Lead collaboration between NASA Centers, other government agencies, industry, academia, and voluntary census organizations (ASTM Committees E07 on Nondestructive Testing, F42 on Additive Manufacturing (AM) Technologies, and ISO Technical Committee (TC) 261) to develop national standards for NDE of aerospace materials used in NASA/aerospace applications.</p><p>2) Lead a leveraged interlaboratory study (ILS) to develop NDE for qualification and certification of AM parts.</p><p>3) Lead ASTM E07 development and periodic revision of flat panel polymer matrix composite (PMC) standards: ASTM E2533 (Guide) , E2580 (ultrasonic testing (UT) , E2581 (shearography) , E2582 (flash thermography) , E2661 (acoustic emission) , E2662 (radiographic testing (RT)) , and draft work item WK40707 (active thermography).</p><p>4) Lead periodic revision of composite overwrapped pressure vessel (COPV) standards: E2981 (overwrap)  and ASTM E2982 (liner) .</p><p>5) Develop a new NDE of AM Guide (ASTM WK47031) .</p><p>6) Develop a new eddy current test (ECT)-UT-profilometer standard practice or test method for fracture control of metal parts using 90/95 Probability of Detection (POD) of critical initial flaws sizes in metal parts (TBD).</p><p>7) Respond to NASA Office of Safety and Mission Assurance (OSMA) and NASA Space Technology Mission Directorate (STMD) requests as needed (e.g., AM, advanced manufacturing, counterfeit parts and ESA/JAXA collaboration).</p><p>The historical standards development time line (Items 3 through 6) is shown in <strong>Figure 1</strong>. The WK47031 effort (Item 5) constitutes the bulk of the present focus and capitalizes on momentum created by the release of the FY14 <em>Nondestructive Evaluation of Additive Manufacturing</em> <em>State-of-the-Discipline Report </em>(NASA-TM-218560) . The ultimate goal vis-à-vis WK47031 is to determine the effect-of-defect of specific seeded flaw types while determining detection thresholds using controlled embedded features. A portion of this effort also dovetails with the NASA Engineering and Safety Center (NESC) Universal ECT-UT-Profilometer Scanner project.</p> <p><strong>Background:</strong> One of the main obstacles slowing the acceptance and use of advanced materials (e.g., PMCs, COPVs and AM parts) in NASA and commercial space applications is the lack of a broadly accepted materials and process quality systems, including sensitive NDE procedures with well-defined accept-reject criteria. Matching VCO standards are also needed to ensure process and equipment control, finished part quality and consistent inspection methodologies for finished parts after manufacturing and after installation of parts in service. In AM, the possibility to ‘design to constraint’ offers a paradigm shift opening the door to make parts with shorter production lead times, less waste, improved cost, maximized properties, and reduced weight. However, to fully realize the merits of this and other advanced processing technologies, and to ensure parts of the highest quality end up in NASA/aerospace applications, new approaches to for in-situ monitoring NDE used during manufacturing, post-process NDE used on as-build and finished parts are needed. In AM, for example, NDE procedures must be able to detect flaw types (<strong>Figure 2</strong>), many of which are not found in cast, wrought or conventionally welded parts (<strong>Figure 3</strong>). Deeply embedded porosity, complex part geometry, and intricate internal features (e.g., lattice structures) impose additional challenges on conventional NDE.</p><p> </p><p><strong>Technical Approach: </strong> In the WK47031 effort (<strong>Figure 4</strong>), a NASA-led interlaboratory study (ILS) is currently being conducted to identify and refine NDE for inspection of AM aerospace parts. This effort is spread across g
- API data.nasa.gov | Last Updated 2020-01-29T03:13:47.000Z
An integrated fatigue damage diagnosis and prognosis framework is proposed in this paper. The proposed methodology integrates a Lamb wave-based damage detection technique and a Bayesian updating method for remaining useful life (RUL) prediction. First, a piezoelectric sensor network is used to detect the fatigue crack size near the rivet holes in fuselage lap joints. Advanced signal processing and feature fusion is then used to quantitatively estimate the crack size. Following this, a small time scale model is introduced and used as the mechanism model to predict the crack propagation for a given future loading and an estimate of initial crack length. Next, a Bayesian updating algorithm is implemented incorporating the damage diagnostic result for the fatigue crack growth prediction. Probability distributions of model parameters and final RUL are updated considering various uncertainties in the damage prognosis process. Finally, the proposed methodology is demonstrated using data from fatigue testing of realistic fuselage lap joints and the model predictions are validated using prognostics metrics.
- API data.nasa.gov | Last Updated 2020-01-29T03:49:41.000Z
The proposed research will develop long-range terrain characterization technologies for autonomous excavation in planetary environments. This work will develop a machine learning framework for long-range prediction of both surface and subsurface terrain characteristics that: (1) indicate the excavation-value of the material and (2) describe how hazardous terrain is to a robotic excavator. Factors influencing importance include the mineral composition of the material and the presence and concentration of volatiles. Terrain hazards will include loose terrain that could cause wheels to sink or slip as well as the presence of surface and subsurface rocks that would inhibit excavation. This work will develop technologies for long-range terrain mechanical characterization and volatile prediction with high spatial coverage. Ground penetrating radars and neutron spectrometers provide reasonable accurate estimates of subsurface composition and volatile accumulation; however, they are limited in sampling range and area. Cameras and LIDAR will instead be used to measure reflected radiation, temperature, and geometry at long range with a wide field of view. From these measurements, the thermal properties and spectral reflectance curves of the terrain will be estimated, since both are correlated to terrain composition and traversability. These properties, along with geometry, will be fed into a machine learning framework for prediction of terrain characteristics. Priors will be generated based on data from orbital satellites. Measurements of material composition, volatile accumulation, and traversability will be generated from expert labeling, neutron spectrometers, and wheel slip measurements, respectively. These measurements will be used to train machine learning algorithms for long-range prediction of terrain mechanical characteristics and resource concentration.
- API data.nasa.gov | Last Updated 2020-01-29T04:57:20.000Z
This dataset is comprised of asteroid flux data measured in 26 filters using the McCord dual beam photometer, and covering the range 0.32 - 1.08 microns for 285 numbered asteroids, as published in Chapman & Gaffey (1979b) and McFadden, et al. (1984).
- API data.nasa.gov | Last Updated 2020-01-29T04:30:37.000Z
The photons of the cosmic microwave background (CMB) stream toward us from the boundary of the observable universe and arrive with information about both their point of origin and the contents of the space in between. Fully extracting this information requires measuring not only the energy and arrival direction of these photons, but also their polarization. My proposal is to develop fast, robust, and extremely sensitive polarization detectors (and arrays of such detectors) for future ground-based and satellite missions to accurately map the celestial polarization. The knowledge we will thereby gain extends from the first moments of the universe (and the theory of its exponential expansion) to the statistical distribution of galaxies and galaxy clusters (from which we can derive information about dark matter and dark energy). The two complementary designs I am pursuing share the goal of collecting more photons while maintaining low detector noise and stable equilibrium temperatures under the cryogenic conditions in which they are operated. Achieving these goals requires characterizing the responses of the detectors and arrays and understanding the results through a model of the behavior. I will model thermal energy transfer between the detector or array elements, and use these models to predict and understand their response to electrical and optical input signals. Successful models can then be used to optimize detector and array properties in tandem with measurements of the optical coupling efficiencies of the detectors. I will also contribute to the eventual implementation of these devices in the field, and to the analysis of the data they produce. The science goals of these CMB missions align with those outlined in the 2010 National Research Council decadal survey, while elements of the detector design would themselves prove useful in other space technology applications requiring sensitive photoreceptors.