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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

The Scope and Direction of Health Informatics

Health Informatics (HI) is a dynamic discipline based upon the medical sciences, information sciences, and cognitive sciences. Its domain is can broadly be defined as medical information management. The purpose of this paper is to provide an overview of this domain, discuss the current "state of the art" , and indicate the likely growth areas for health informatics. The sources of information utilized in this paper are selected publications from the literature of Health Informatics, HI 5300: Introduction to Health Informatics, which is a course from the Department of Health Informatics at the University of Texas Houston Health Sciences Center, and the author's personal experience in practicing telemedicine and implementing an electronic medical record at the NASA Johnson Space Center. The conclusion is that the direction of Health Informatics is in the direction of data management, transfer, and representation via electronic medical records and the Internet.

McGinnis, Patrick J.↗
NASA NTRS2006

NASA Tech Briefs, November 2006

Topics include: Simulator for Testing Spacecraft Separation Devices; Apparatus for Hot Impact Testing of Material Specimens; Instrument for Aircraft-Icing and Cloud-Physics Measurements; Advances in Measurement of Skin Friction in Airflow; Improved Apparatus for Testing Monoball Bearings; High-Speed Laser Scanner Maps a Surface in Three Dimensions; Electro-Optical Imaging Fourier-Transform Spectrometer; Infrared Instrument for Detecting Hydrogen Fires; Modified Coaxial Probe Feeds for Layered Antennas; Detecting Negative Obstacles by Use of Radar; Cryogenic Pound Circuits for Cryogenic Sapphire Oscillators; PixelLearn; New Software for Predicting Charging of Spacecraft; Conversion Between Osculating and Mean Orbital Elements; Generating a 2D Representation of a Complex Data Structure; Making Activated Carbon by Wet Pressurized Pyrolysis; Composite Solid Electrolyte Containing Li+- Conducting Fibers; Electrically Conductive Anodized Aluminum Surfaces; Rapid-Chill Cryogenic Coaxial Direct-Acting Solenoid Valve; Variable-Tension-Cord Suspension/Vibration- Isolation System; Techniques for Connecting Superconducting Thin Films; Versatile Friction Stir Welding/Friction Plug Welding System; Thermal Spore Exposure Vessels; Enumerating Spore-Forming Bacteria Airborne with Particles; Miniature Oxidizer Ionizer for a Fuel Cell; Miniature Ion-Array Spectrometer; Promoted-Combustion Chamber with Induction Heating Coil; Miniature Ion-Mobility Spectrometer; Mixed-Salt/Ester Electrolytes for Low-Temperature Li+ Cells; Miniature Free-Space Electrostatic Ion Thrusters; Miniature Bipolar Electrostatic Ion Thruster; Holographic Plossl Retroreflectors; Miniature Electrostatic Ion Thruster With Magnet; Using Apex To Construct CPM-GOMS Models; Sequence Detection for PPM Optical Communication With ISI; Algorithm for Rapid Searching Among Star-Catalog Entries; Expectation-Based Control of Noise and Chaos; Radio Heating of Lunar Soil to Release Gases; Using Electrostriction to Manipulate Ullage in Microgravity; Equations for Scoring Rules When Data Are Missing; Insulating Material for Next-Generation Spacecraft; and Pseudorandom Switching for Adding Radar to the AFF Sensor.

Source record↗
NASA NTRS2015

Contribution of Anthropogenic Warming to California Drought During 2012-2014

A suite of climate data sets and multiple representations of atmospheric moisture demand are used to calculate many estimates of the self-calibrated Palmer Drought Severity Index, a proxy for near-surface soil moisture, across California from 1901 to 2014 at high spatial resolution. Based on the ensemble of calculations, California drought conditions were record breaking in 2014, but probably not record breaking in 2012-2014, contrary to prior findings. Regionally, the 2012-2014 drought was record breaking in the agriculturally important southern Central Valley and highly populated coastal areas. Contributions of individual climate variables to recent drought are also examined, including the temperature component associated with anthropogenic warming. Precipitation is the primary driver of drought variability but anthropogenic warming is estimated to have accounted for 8-27 percent of the observed drought anomaly in 2012-2014 and 5-18 percent in 2014. Although natural variability dominates, anthropogenic warming has substantially increased the overall likelihood of extreme California droughts.

California↗
NASA NTRS2023

Experimental Investigation of Distributed Sand-Grain Roughness Effects on Transition Onset and Turbulent Heating at Mach 6

An experimental investigation of distributed sand-grain surface roughness effects on hypersonic boundary-layer transition and turbulent heating has been performed. Two representative entry vehicle geometries, a sphere-cone aeroshell and a spherical-cap aeroshell, were considered. Cast ceramic models of each geometry were fabricated with distributed sand-grain roughness patterns of different heights that simulated an ablated thermal protection system. Wind tunnel testing was performed at Mach 6 over a range of Reynolds numbers sufficient to produce laminar, transitional, and turbulent flow. Aeroheating and boundary-layer transition onset data were obtained using global phosphor thermography. The experimental heating data are presented herein, as are comparisons to laminar and turbulent smooth-wall heat transfer distributions from computational flow field simulations. The boundary-layer transition data were found to correlate with a functional representation developed in prior roughness studies, although the data scatter was greater owing to the height variability of distributed sand-grain roughness.

Brian R Hollis↗
DOE OSTI2016

Graphics processing unit-assisted lossless decompression

Systems and methods for decompressing compressed data that has been compressed by way of a lossless compression algorithm are described herein. In a general embodiment, a graphics processing unit (GPU) is programmed to receive compressed data packets and decompress such packets in parallel. The compressed data packets are compressed representations of an image, and the lossless compression algorithm is a Rice compression algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Implementation and Evaluation of IEC 61850-based Distributed Control System for Microgrid Applications

This paper presents a resilient, modular and scalable distributed control architecture for microgrid systems which uses distributed agents controlling individual loads, network switches, generators or storage devices to provide intelligent power management and efficient microgrid operation. The communication between the distributed agents is realized by implementing IEC 61850 data models and GOOSE messages. The data models include state representation, measurements at various points, and dispatch requests for reliable operation of microgrid control system. System resiliency is enhanced through implementing distributed agent architecture with no single point of failure.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Calorimeter Pileup Deconvolution for Online Trigger Primitives

In high energy physics experiment, as the luminosity increases, pile-up issues on detectors such as calorimeters become non-negligible. Deconvolution approaches with mathematic pre-assumptions such as Sparse Representation are developed for data analysis stage. For online computation tasks such as for trigger primitive creation, signal availability is significantly different as in offline data analysis stage, and therefore, different (yet simpler) algorithms should be explored. In this document, several approaches of deconvolution suitable for FPGA implementation are discussed.

Wu, Jin-yuan [Fermilab] (ORCID:0000000344329521)↗
NASA NTRS2022

Improved representation of agricultural land use and crop management for large-scale hydrological impact simulation in Africa using SWAT+

To date, most regional and global hydrological models either ignore the representation of cropland or consider crop cultivation in a simplistic way or in abstract terms without any management practices. Yet, the water balance of cultivated areas is strongly influenced by applied management practices (e.g. planting, irrigation, fertilization, and harvesting). The SWAT+ (Soil and Water Assessment Tool) model represents agricultural land by default in a generic way, where the start of the cropping season is driven by accumulated heat units. However, this approach does not work for tropical and subtropical regions such as sub-Saharan Africa, where crop growth dynamics are mainly controlled by rainfall rather than temperature. In this study, we present an approach on how to incorporate crop phenology using decision tables and global datasets of rainfed and irrigated croplands with the associated cropping calendar and fertilizer applications in a regional SWAT+ model for northeastern Africa. We evaluate the influence of the crop phenology representation on simulations of leaf area index (LAI) and evapotranspiration (ET) using LAI remote sensing data from Copernicus Global Land Service (CGLS) and WaPOR (Water Productivity through Open access of Remotely sensed derived data) ET data, respectively. Results show that a representation of crop phenology using global datasets leads to improved temporal patterns of LAI and ET simulations, especially for regions with a single cropping cycle. However, for regions with multiple cropping seasons, global phenology datasets need to be complemented with local data or remote sensing data to capture additional cropping seasons. In addition, the improvement of the cropping season also helps to improve soil erosion estimates, as the timing of crop cover controls erosion rates in the model. With more realistic growing seasons, soil erosion is largely reduced for most agricultural hydrologic response units (HRUs), which can be considered as a move towards substantial improvements over previous estimates. We conclude that regional and global hydrological models can benefit from improved representations of crop phenology and the associated management practices. Future work regarding the incorporation of multiple cropping seasons in global phenology data is needed to better represent cropping cycles in areas where they occur using regional to global hydrological models.

crop phenology↗
DOE OSTI2023

Rapid design of top-performing metal-organic frameworks with qualitative representations of building blocks

Abstract Data-driven materials design often encounters challenges where systems possess qualitative (categorical) information. Specifically, representing Metal-organic frameworks (MOFs) through different building blocks poses a challenge for designers to incorporate qualitative information into design optimization, and leads to a combinatorial challenge, with large number of MOFs that could be explored. In this work, we integrated Latent Variable Gaussian Process (LVGP) and Multi-Objective Batch-Bayesian Optimization (MOBBO) to identify top-performing MOFs adaptively, autonomously, and efficiently. We showcased that our method (i) requires no specific physical descriptors and only uses building blocks that construct the MOFs for global optimization through qualitative representations, (ii) is application and property independent, and (iii) provides an interpretable model of building blocks with physical justification. By searching only ~1% of the design space, LVGP-MOBBO identified all MOFs on the Pareto front and 97% of the 50 top-performing designs for the CO 2 working capacity and CO 2 /N 2 selectivity properties.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Assessing Metal Ion Assignment Accuracy in Protein Data Bank Models via Elemental Spectroscopy

Accurate representation of metal ions in macromolecular structures is critical for chemical interpretation, computational modeling, and machine-learning methods that rely on Protein Data Bank (PDB) entries. However, the elemental identity of metals modeled in crystallographic structures is often inferred indirectly and rarely validated experimentally. Here, we combine Particle Induced X-ray Emission (PIXE) and X-ray Fluorescence Spectroscopy (XRFS) to determine the elemental composition of protein samples used to generate 70 deposited metalloprotein crystal structures. By analyzing the original protein material employed for crystallization, but before the addition of crystallization buffer solutions, we assess whether the modeled metal ions in deposited structures are consistent with experimentally detectable elemental content. We find that in a majority of cases, the metals modeled in the corresponding PDB entries are inconsistent with the metals present in the protein samples before crystallization, or that additional metals are present but not represented in the structural models. Spectroscopic results were integrated with automated crystallographic validation metrics, including real-space Z-difference (RSZD) analysis and systematic rerefinement, to evaluate atomic-number mismatch at metal sites. PIXE and XRFS show strong agreement for dominant elemental signals and provide complementary, scalable approaches for identifying suspect metal assignments. This work does not address physiological or functional metalation but instead highlights a widespread data integrity issue in deposited macromolecular structures, PDB-wide. These results establish an experimentally corroborated link between elemental identity and crystallographic validation metrics, enabling the large-scale detection of chemically inconsistent annotations in structural databases used for computational modeling and machine learning.

Crystallization↗
DOE OSTI2015

Groundwater monitoring report streamlining techniques - 15305

Compliance with national and state environmental regulations (e.g. RCRA and CERCLA [SuperFund] requires Savannah River Site (SRS) to extensively collect and report groundwater monitoring data, with potential fines for missed reporting deadlines. Large reports, which may contain dozens to hundreds of monitoring stations, are especially challenging to present data in a way that is quick and easy to interpret. Initially analyzing hundreds to thousands of records of data to be summarized in the report can be daunting, and producing maps can be extremely time consuming. Several utilities have been developed at SRS to facilitate production of the regulatory reports which include data tables, charts, maps, and cross-sections. Components of each report are generated in accordance with complex sets of regulatory requirements specific to each site monitored. SRS developed a relational database to incorporate the detailed reporting rules with the groundwater data, and created a set of automation tools to interface with the information and generate the report components. Maps produced with ArcGIS use a technique that can be repeatedly used to display data very quickly and in a consistent form. Cross-sections developed with RockWorks provide a more technical and geological analysis and representation of well and boring data, while allowing for automated data placement. These process improvements have streamlined the repetitive job of creating data tables, charts, maps, and cross-sections. It has also enhanced report quality and consistency by centralizing the information, and has reduced manpower and production time through automated efficiencies. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Unbinned extraction of $γ$ from $B\to DK$ with normalizing flows

We introduce an unbinned method for extracting the CKM angle $γ$ from the decay chain $B^\pm \to (D \to K_S π^+ π^-) K^\pm$ using normalizing flows (NFs). The NFs, trained on $D$ decay data, learn a faithful continuous representation of the amplitude and strong phase variation over the $D\to K_Sπ^+π^-$ Dalitz plot whose fidelity improves with increased data sample sizes. With this input, the $B$ decay data can be used to extract the parameters $r_B$, $δ_B$, and $γ$. We test the method on Monte Carlo generated data, where it successfully recovers the injected value of $γ$ within uncertainties. The present implementation propagates statistical uncertainties from finite training data via an ensemble of independently trained flows, and does not attempt to capture the effects of systematic experimental errors. We explore two versions of the method that differ in how the trigonometric constraint on phase variation is encoded, and comment on the possible extension to Bayesian NFs, which would provide direct uncertainty estimates on the learned densities without requiring ensemble training.

Grossman, Yuval [Cornell U., LEPP]↗
DOE OSTI2021

DNTTD (Distributed Non-Negative Tensor Train Decomposition)

The era of exascale computing opens new venues for innovations and discoveries in many scientific, engineering, and commercial fields. However, with the exa flops also come the extra-large high-dimensional data generated by high performance computing. High-dimensional data is presented as multidimensional arrays, aka tensors. The presence of latent (not directly observable) structures in the tensor allows a unique representation and compression of the data by classical tensor factorization techniques. However, the classical tensor methods are not always stable or they can be exponential in their memory requirements, which makes them not suitable for high-dimensional tensors. Tensor train (TT) is a state-of-the-art tensor network introduced for factorization of high-dimensional tensors. TT transforms the initial high-dimensional tensor in a network of three-dimensional tensors that requires only a linear storage. Many real-world data, such as, density, temperature, population, probability, etc., are non-negative and for an easy interpretation, the algorithms preserving non-negativity are preferred. Here, we introduce a distributed non-negative tensor-train and demonstrate its scalability and the compression on synthetic and real world big datasets.

Bhattarai, Manish↗
DOE OSTI2019

Status of free-energy representations for the homogeneous electron gas

Renewed interest in the homogeneous electron gas (HEG) has been stimulated by recent accurate simulations of it over a wide range of densities and temperatures. Those data, combined with known theoretical limits, have led to analytical representations of the free energy. Such a representation is, at least in principle, the complete HEG equation of state. The initial objective here is to establish that the two best representations of the simulation data and constraints are effectively the same in both functional form and accuracy of representation. The second objective is to disclose and delineate a significant difficulty. Despite their expected accuracy for the free energy, the underlying functional form is not adequate for derived thermodynamic properties. As an example, the specific heats obtained from the representations exhibit anomalies suggesting the need first for additional simulation data in critical regimes, then for refined fitting functions. Here, the existing representations are, however, almost certainly adequate for applications based on the free energy alone (e.g., density functional theory for warm dense matter). The third objective is to show that, despite their inability to provide a complete thermodynamic description of the HEG, the best analytical representations do provide a fully adequate exchange-correlation local density approximation for free energy density functional calculations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

L3:RTM.MCH.P19.06: Neutronics UQ

This report summarizes some of the work performed in the last year on the nuclear data uncertainty tasks using the multipole representation. This approach was proposed to provide a simple and integrated way of evaluating the nuclear data uncertainty in a Monte Carlo simulation and evaluating their impact on multigroup cross sections. As previously described, the windowed multipole format allows for a resonance based representation of the cross sections with direct Doppler broadening. This representation also lends itself nicely to the evaluation of sensitivities of resonance parameters and the direct perturbation of resonance parameters in the simulation environment. Both of these approaches rely on the need of conversion of the covariance data (File 32 of the ENDF library) into a pole and residues form: i.e. into a multipole covariance. Section 5 details the current pathways that have been used so far to achieve this. In order to verify the conversion process of the covariance data, the sensitivies to the resonance parameters and the sampling from these parameters, an analytical benchmark of the slowing process was developed. The benchmark provides a closed-form solution for the flux in the presence of Hydrogen and multiple resonant nuclides. Section 2 details the analytical benchmark and provides the closed-form solutions for the flux and its sensitivity to resonance parameters. A verification of the proposed methodology is performed and compared with the analytic solution to demonstrate the accuracy of the benchmark and the applicability of our embedded resonance perturbation approach in a small Monte Carlo code. Papers are currently being drafted documenting the benchmark and the generation of sensitivities. As paths to best generate multipole covariance matrices are being compared, we also explored new methods to propagate nuclear data uncertainty across the Monte Carlo neutron transport calculation. Section 4 studies the use of multipole covariance matrices to perform sensitivity analysis. Section 3.2 documents the results of our investigations into a new way of propagating nuclear data uncertainty across Monte Carlo transport simulations by sampling new epistemic cross sections for each new neutron.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Learning nuclear cross sections across the chart of nuclides with graph neural networks

We explore the use of deep learning techniques to learn how nuclear cross sections change as we add or remove protons and neutrons. As a proof of principle, we focus on the neutron-induced reactions in the fast energy regime. Our approach follows a two-stage learning framework. First, we apply representation learning to encode cross section data into a latent space using either variational autoencoders (VAEs) or implicit neural representations (INRs). Then, we train graph neural networks (GNNs) on the resulting embeddings to predict missing values across the nuclear chart by leveraging the topological structure of neighboring isotopes. We demonstrate accurate cross section predictions within a 9 × 9 block of missing nuclei. We also find that the optimal GNN training strategy depends on the type of latent representation used, with VAE embeddings performing best under end-to-end optimization in the original space, while INR embeddings achieve better results when the GNN is trained only in the latent space. Furthermore, using clustering algorithms, we map groups of latent vectors into regions of the nuclear chart and show that VAEs and INRs can discover some of the neutron magic numbers. These findings suggest that deep-learning models based on the representation encoding of cross sections combined with graph neural networks hold significant potential in augmenting nuclear theory models, e.g., by providing reliable estimates of covariances of cross sections, including cross-material covariances.

Machine learning↗
NASA NTRS1987

Presentations by participants (edited and condensed)

The utility of Geomagnetic Automonous Shuttle-Launched Probe (GASP) for studying the earth's crust can be characterized in terms of lithospheric contributions by past satellite programs. In contrast to the POGO and MAGSAT satellites, GASP will orbit at a lower elevation to provide better resolved lithospheric magnetic anomalies for more detailed and improved geologic analysis. In the geological representation of satellite magnetic anomaly data, two current approaches can serve the analysis of GASP observations. When global scale coverage is available, spherical harmonic expansions of the observations may be utilized. When satellite elevation data is used, a useful and efficient approach is to determine the physical properties of an array of point equivalent sources by least squares matrix inversion of the observations, which will closely duplicate the observed field. An important preliminary step in the representation of satellite magnetic observations by either approach, is to register the data at constant elevation over uniform intervals of latitude and longitude. Further applications of GASP are discussed.

Vonfrese, R. R. B.↗