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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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National Geodetic Survey activities towards the development of operational geodetic survey systems based on radio interferometry are reviewed. Information about the field procedures, data reduction and analysis, and the results obtained to date is presented.
As new radiometer technologies provide the possibility of greatly improved spatial resolution, their performance must also be evaluated in terms of expected sensitivity and absolute accuracy. As aperture size increases, the sensitivity of a Dicke mode radiometer can be maintained or improved by application of any or all of three digital averaging techniques: antenna data averaging with a greater than 50% antenna duty cycle, reference data averaging, and gain averaging. An experimental, noise-injection, benchtop radiometer at C-band showed a 68.5% reduction in Delta-T after all three averaging methods had been applied simultaneously. For any one antenna integration time, the optimum 34.8% reduction in Delta-T was realized by using an 83.3% antenna/reference duty cycle.
Scientists conduct large-scale simulations to compute derived quantities from primary data. Thus, it is crucial that data compression techniques maintain bounded errors on these derived quantities or quantities of interest (QOI). For many spatiotemporal applications, these QOIs are binary in nature and represent presence or absence of a physical phenomenon. In this work, we propose to use a hybrid approah for differential compression for such applications. We use a neural network (NN) approach to determine regions-of-interest (ROIs) where the binary QOIs are going to be prevalent. This is then used with traditional approaches that compress at a lower level (and higher accuracy) for these ROIs as compared to other regions.
Advances in electrocatalysis research rely heavily on building a thorough mechanistic understanding of catalyst active sites under realistic operating conditions. Only recently have techniques emerged that enable sensitive spectroscopic data collection to distinguish catalytically relevant surface sites from the underlying bulk material under applied potential in the presence of an electrolyte layer. Here, we demonstrate that operando high-energy-resolution fluorescence detected X-ray absorption spectroscopy (HERFD-XAS) is a powerful spectroscopic method which offers critical surface chemistry insights in CO 2 electroreduction with sub-electronvolt energy resolution using hard X-rays. Combined with the high surface area-to-volume ratio of 5 nm copper nanoparticles, operando HERFD-XAS allows us to observe with clear evidence the breaking of chemical bonds between the ligands and the Cu surface as part of the ligand desorption process occurring under electrochemical potentials relevant for the CO 2 reduction reaction (CO 2 RR). In addition, the dynamic evolution of oxidation state and coordination number throughout the operation of the nanocatalyst was continuously tracked. With these results in hand, undercoordinated metallic copper nanograins are proposed to be the real active sites in the CO 2 RR. In conclusion, this work emphasizes the importance of HERFD-XAS compared to routine XAS in catalyst characterization and mechanism exploration, especially in the complicated electrochemical CO 2 RR.
Processing and data reduction of holographic images from the International Microgravity Laboratory 1 (IML-1) presents some interesting challenges in determining the effects of microgravity on crystal growth processes. Use of several processing techniques, including the Computerized Holographic Image Processing System and the Software Development Package (SDP-151) will provide fundamental information for holographic and schlieren analysis of the space flight data.
A systematic experimental program was conducted to develop a data base for the noise and related flow characteristics of upper surface blown configurations. The effect of various geometric and flow parameters was investigated experimentally. The dominant noise was identified from the measured flow and noise characteristics to be generated downstream of the trailing edge. The possibilities of noise reduction techniques were explored. An upper surface blown (USB) noise prediction program was developed to calculate noise levels at any point and noise contours (footprints). Using this noise prediction program and a cruise performance data base, aircraft design studies were conducted to integrate low noise and good performance characteristics. A theory was developed for the noise from the highly sheared layer of a trailing edge wake. Theoretical results compare favorably with the measured noise of the USB model.
Seismic sensors deployed near roadways effectively capture ground vibrations generated by passing vehicles. Although both traditional and machine‐learning algorithms have been utilized for analyzing such signals, independent validation of detected vehicle events remains limited. We applied two unsupervised machine‐learning algorithms, uniform manifold approximation and projection for dimension reduction, and hierarchical density‐based spatial clustering of applications with noise, to continuous seismic data collected along a road on the main campus of Oak Ridge National Laboratory. The algorithms identified seven distinct cluster labels across the entire dataset. By comparing these cluster labels with precipitation records from a nearby weather station and image‐derived labels from a local camera system, we identified one cluster associated with rainfall and another with vehicle activity. Our algorithms identified a greater number of vehicle‐related labels compared to the camera‐derived labels because seismic data are unaffected by poor lighting conditions. The arrival times of the newly detected vehicle signals corresponded well with the road’s speed limit, supporting our findings. Our algorithm outperformed the short‐term average/long‐term average method and k‐means clustering. Our results suggest that seismic data, when analyzed with machine‐learning algorithms, can complement existing vehicle monitoring systems, particularly under challenging environmental conditions.
We report on the use of novel mathematical methods in hypergraph analytice over a large quantity of DNS data. Hypergraphs generalize graphs, as used in network science, to better model complex multiway relations in cyber data. Specifically, casting DNS data from Georgia Tech's ActiveDNS repository as hypergraphs allows us to fully represent the interactions between {\em collections} of domains and IP addresses. To facilitate large-scale analytics, we fielded an analytical pipeline of two capabilities. HyperNetX (HNX) is a Python package for the exploration and visualization of hypergraphs, acting as a frontend. For the backend, the Chapel HyperGraph Library (CHGL) is a library for high performance hypergraph analytics written in the exascale programming language Chapel. CHGL was used to process gigascale DNS data, performing compute-intensive calculations for data reduction and segmentation. Identified portions are then sent to HNX for both exploratory analysis and knowledge discovery targeting known tactics, techniques, and procedures.
The present work concerns the transferability of coarse-grained (CG) modeling in reproducing the dynamic properties of the reference atomistic systems across a range of parameters. In particular, we focus on implicit-solvent CG modeling of polymer solutions. The CG model is based on the generalized Langevin equation, where the memory kernel plays the critical role in determining the dynamics in all time scales. Thus, we propose methods for transfer learning of memory kernels. The key ingredient of our methods is Gaussian process regression. By integration with the model order reduction via proper orthogonal decomposition and the active learning technique, the transfer learning can be practically efficient and requires minimum training data. Through two example polymer solution systems, we demonstrate the accuracy and efficiency of the proposed transfer learning methods in the construction of transferable memory kernels. The transferability allows for out-of-sample predictions, even in the extrapolated domain of parameters. Built on the transferable memory kernels, the CG models can reproduce the dynamic properties of polymers in all time scales at different thermodynamic conditions (such as temperature and solvent viscosity) and for different systems with varying concentrations and lengths of polymers.
Equipment and techniques were evaluated operationally, by the Department of Defense, during the National Geodetic Satellite Program (NGSP). The theory, instrumentation, and data reduction methods used are described. Results obtained during the NGSP are given.
Results are presented for an investigation of cosmic-ray isotopic composition in which a balloon-borne instrument was used to perform an isotopic analysis of the abundant elements from B through Si in the energy range from 1.0 to 1.6 GeV/amu. The technique employed, which depends on the effect of earth's magnetic field on incoming cosmic rays, and reduction of the observational data are described in detail. Mean masses of the elements B, C, N, O, Ne, Mg, and Si are extracted from normalized Cerenkov spectra and evaluated in terms of the 'leaky box' model of galactic propagation and confinement. The implications of the results for cosmic-ray sources are shown to be consistent with the nucleosynthesis of certain isotopes in the cosmic rays in explosive carbon burning under the conditions of peak temperature, density, and neutronization expected in an explosion of a massive highly evolved star. The possibility of alternate interpretations is noted.
Laboratory investigation of sound transmission through panels and the use of modern data analysis techniques applied to actual aircraft is used to determine methods to reduce general aviation interior noise. The experimental noise reduction characteristics of stiffened flat and curved panels with damping treatment are discussed. The experimental results of double-wall panels used in the general aviation industry are given. The effects of skin panel material, fiberglass insulation and trim panel material on the noise reduction characteristics of double-wall panels are investigated. With few modifications, the classical sound transmission theory can be used to design the interior noise control treatment of aircraft. Acoustic intensity and analysis procedures are included.
A suboptimal Kalman filter system which evolves error covariances in terms of a truncated set of wavelet coefficients has been developed for the assimilation of chemical tracer observations of CH4. This scheme projects the discretized covariance propagation equations and covariance matrix onto an orthogonal set of compactly supported wavelets. Wavelet representation is localized in both location and scale, which allows for efficient representation of the inherently anisotropic structure of the error covariances. The truncation is carried out in such a way that the resolution of the error covariance is reduced only in the zonal direction, where gradients are smaller. Assimilation experiments which last 24 days, and used different degrees of truncation were carried out. These reduced the covariance size by 90, 97 and 99 % and the computational cost of covariance propagation by 80, 93 and 96 % respectively. The difference in both error covariance and the tracer field between the truncated and full systems over this period were found to be not growing in the first case, and growing relatively slowly in the later two cases. The largest errors in the tracer fields were found to occur in regions of largest zonal gradients in the constituent field. This results indicate that propagation of error covariances for a global two-dimensional data assimilation system are currently feasible. Recommendations for further reduction in computational cost are made with the goal of extending this technique to three-dimensional global assimilation systems.
Results are presented from an experimental investigation into facesheet core disbonding in carbon fiber reinforced plastic/Nomex honeycomb sandwich structures using a Single Cantilever Beam test. Specimens with three, six and twelve-ply facesheets were tested. Specimens with different honeycomb cores consisting of four different cell sizes were also tested, in addition to specimens with three different widths. Three different data reduction methods were employed for computing apparent fracture toughness values from the test data, namely an area method, a compliance calibration technique and a modified beam theory method. The compliance calibration and modified beam theory approaches yielded comparable apparent fracture toughness values, which were generally lower than those computed using the area method. Disbonding in the three-ply facesheet specimens took place at the facesheet/core interface and yielded the lowest apparent fracture toughness values. Disbonding in the six and twelve-ply facesheet specimens took place within the core, near to the facesheet/core interface. Specimen width was not found to have a significant effect on apparent fracture toughness. The amount of scatter in the apparent fracture toughness data was found to increase with honeycomb core cell size.
Introduction: Given the long time intervals required for data transmission to and from ocean worlds targets, low bandwidth for data transmission, time required for data processing and analysis, and potentially extreme radiation environments (e.g., Europa), it is clear that ocean worlds missions will need more autonomous flight instruments and software in order to achieve established science goals. Protracted time intervals for data analysis (e.g., Europa Lander) strongly motivates the development of rapid, consistent and streamlined methods for interpreting data from flight mass spectrometers to e.g., determine how mass spectra from a plume or surface liquid/ice relates to the surface/subsurface. Since mass spectrometry also has the potential to correctly identify biosignatures[1], it is imperative that such methods for interpreting data are consistent and accurate. We used 848 isotope ratio mass spectra from laboratory analyses of CO2 that interacted with ocean worlds-relevant seawaters as a ‘training’ dataset for ‘unsupervised’ machine learning. In unsupervised learning, characteristics of the data are not labeled or linked, and any similarities found only result from the neural network. CO2 isotopologues analyzed for this dataset mimic the remote measurements of CO2 by a flight mass spectrometer, and are detailed in Theiling [2]. From this dataset, we used measured features of the spectra, such as retention time, intensity, and (isotopologue) mass ratios as inputs for our autoencoder neural network. Our neural network was trained to find similarities in these and other spectral features for seawaters of a particular composition and amount of initial CO2. Successful training then created an output of these similarities for various seawaters, which included MgSO4, Na2SO4, NaCl, MgCl2, KCl, and NaHCO3, and combinations of these salts. We then applied dimensionality reduction techniques such as Principal Component Analysis (PCA), T-Distributed Stochastic Neighbor Embedding (TSNE), and Uniform Manifold Approximation and Projection (UMAP) to demonstrate latent data features as a two-dimensional projection in a unitless, high-dimensional space. In this projection, a data point represents the combined effect of spectral features such as intensity, retention time, and isotope ratio. Our initial UMAP demonstrates data clustering (organization of the data by the neural network) based on the amount of CO2 that had initially interacted with each seawater. Further training using more ‘supervised’ learning techniques demonstrate strong clustering of preliminary data based on initial CO2 concentration, seawater chemical composition, and ionic strength (salinity). Our preliminary work therefore suggests that machine learning has the potential to identify compositional variants of an ocean world seawater based on mass spectra from volatile CO2 measurements. Acknowledgments: This work was funded through a Strategic Task Group at NASA Goddard Space Flight Center. The training dataset was collected through funding from the Oklahoma Space Grant Consortium. References: [1] Pappalardo, R. et al. (2013) Astrobiology, 13, 740–773. [2] Theiling (2020) Icarus, 114216.
A long-range laser velocimeter (LV) developed for remote operation from within the flow fields of the large wind tunnels of the National Full-Scale Aerodynamics Complex is described. Emphasis is placed on recent improvements in optical hardware as well as recent additions to data acquisition and processing techniques. The system has been upgraded from a dual-beam, single-color LV with focal range to 10 m, to a dual-beam, two-color LV with focal range to 20 m. At the new extended measurement range (between 10 and 20 m), signals are photon-resolved, and a photon correlation technique is applied to acquire and process the LV signals. This technique permits recovery of the velocity probability distributions at a particular measurement location from which the mean components of velocity and the corresponding normal stress components of turbulence are obtained. The method used for data reduction is outlined in detail, and a discussion of measurement accuracy is made. To study the performance of the LV and verify the measurement accuracy, laboratory measurements were made in the flow field of a 10 cm-diameter, 30-m/sec axisymmetric jet. A discussion of the requirements and techniques used to seed the flow is made, and boundary-layer surveys of mean velocity and turbulence intensity of the streamwise component and the component normal to the surface are presented.
The Microvax 2 computer, the basic software in VMS, and the Mitsubishi High Speed Disk were received and installed. The digital scanning tunneling microscope is fully installed and operational. A new technique was developed for pseudocolor analysis of the line plot images of a scanning tunneling microscope. Computer studies and mathematical modeling of the empirical data associated with many of the film calibration studies were presented. A gas can follow-up experiment which will be launched in September, on the Space Shuttle STS-50, was prepared and loaded. Papers were presented on the structure of the human hair strand using scanning electron microscopy and x ray analysis and updated research on the annual rings produced by the surf clam of the ocean estuaries of Maryland. Scanning electron microscopic work was conducted by the research team for the study of the Mossbauer and Magnetic Susceptibility Studies on NmNi(4.25)Fe(.85) and its Hydride.
Flow accelerated corrosion (FAC) in nuclear power plant pipes is one of the leading causes of accidents, fatalities, damage and outages. Current FAC identification methods employ expensive sensing technology and are “active” methods, where the response of the piping system to an externally-generated thermal, mechanical or optical excitation must be measured. As a result, these techniques require a disruptive and time-consuming setup. Here we propose a method that utilizes pipe surface temperature measurements to passively monitor for FAC-induced pipe wall thinning without the need for expensive equipment or post-installation setup time. This diagnostic method utilizes a simulation data-driven diagnostic model to estimate the amount of thickness reduction in a pipe based on changes in measured steady-state pipe temperatures. In order to reduce the computational burden of generating large, simulation-based datasets, the behavior of the insulation of the pipe was modeled using a suitably calibrated heat transfer boundary parameter. Additionally, global sensitivity analysis was performed to determine system parameter(s), such as the temperature of water flowing inside the pipe, which significantly affect the steady state pipe wall temperature and could cause errors in diagnosis. Two diagnostic models, one using only the change in steady-state temperature as an indicator for FAC-induced pipe wall thinning and the other using water temperature as an additional diagnostic model input were evaluated for their ability to estimate thickness reductions in a pipe using simulated pipe wall temperature data. For the numerical experiments conducted in this work, both models estimated wall thickness with errors within 0.5 mm, indicating that the proposed technique can potentially be used as a low-cost, first-pass method for FAC monitoring.