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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Exoplanet Transit Spectroscopy Using WFC3: WASP-12b, WASP-17b, and WASP-19b

We report an analysis of transit spectroscopy of the extrasolar planets WASP-12 b, WASP-17 b, and WASP-19 b using the Wide Field Camera 3 (WFC3) on the Hubble Space Telescope (HST). We analyze the data for a single transit for each planet using a strategy similar, in certain aspects, to the techniques used by Berta et al., but we extend their methodology to allow us to correct for channel- or wavelength-dependent instrumental effects by utilizing the band-integrated time series and measurements of the drift of the spectrum on the detector over time. We achieve almost photon-limited results for individual spectral bins, but the uncertainties in the transit depth for the band-integrated data are exacerbated by the uneven sampling of the light curve imposed by the orbital phasing of HST's observations. Our final transit spectra for all three objects are consistent with the presence of a broad absorption feature at 1.4 microns most likely due to water. However, the amplitude of the absorption is less than that expected based on previous observations with Spitzer, possibly due to hazes absorbing in the NIR or non-solar compositions. The degeneracy of models with different compositions and temperature structures combined with the low amplitude of any features in the data preclude our ability to place unambiguous constraints on the atmospheric composition without additional observations with WFC3 to improve the signal-to-noise ratio and/or a comprehensive multi-wavelength analysis. Key words: planetary systems - techniques: photometric - techniques: spectroscopic

HST↗
NASA NTRSDate not supplied

Development of a Consistent MODIS and VIIRS Cloud Detection Approach for CERES

A consistent cloud fraction record across various satellite platforms is essential for maintaining a long-term and stable climate data record of Earth's energy budget. With the Aqua satellite nearing the end of its operational lifetime, the continuation of this record relies on utilizing VIIRS observations from NOAA20 for cloud detection in NASA’s Clouds and Earth’s Radiant Energy System (CERES) project. However, integrating data from VIIRS and MODIS instruments poses challenges due to their distinct characteristics, such as varying spatial resolutions and different spectral channels. As a result, deriving consistent cloud properties from these two sensors without introducing artificial discontinuities in the time series remains a complex and challenging task. This paper will present progress toward developing a unified MODIS and VIIRS cloud mask using common channels to produce consistent cloud properties for CERES next edition (Ed5) Earth radiation budget data products. The fundamental approach taken in the CERES cloud mask is to compare the observed radiances to the expected background clear sky radiances. Therefore, one vital step is to compute clear sky radiances with a radiative transfer model that accurately accounts for satellite-specific, spectrally dependent surface reflectance, surface emission, and atmospheric absorption. Refined radiative transfer models and updated ancillary data inputs including surface emissivity maps, IGBP, snow and ice maps are incorporated into the processing framework to improve cloud detection consistency and accuracy. Pixel level cloud mask results and monthly global cloud fraction comparisons between MODIS and VIIRS will be presented to evaluate their consistency. Remaining challenges will be discussed. It is expected that this work will contribute consistent cloud properties for CERES that adequately bridges the MODIS and VIIRS imager data records.

CERES↗
DOE OSTI2025

mzPeak: Designing a Scalable, Interoperable, and Future-Ready Mass Spectrometry Data Format

Advances in mass spectrometry (MS) instrumentation, such as higher resolution, faster scan speeds, and improved sensitivity, have significantly increased the volume and complexity of data. The growing adoption of imaging and ion mobility further amplifies these challenges across MS-based omics fields, including proteomics, metabolomics, and lipidomics. While these technologies unlock new possibilities, they also present significant challenges in data management, storage, and accessibility. Existing open formats, such as the XML-based community standards mzML and imzML, struggle to meet the demands of modern MS workflows due to their large file sizes, slow data access, and limited metadata support. Vendor-specific formats, while optimized for proprietary instruments, lack interoperability, comprehensive metadata support and long-term archival reliability. This white paper lays the groundwork for mzPeak, a next-generation community data format designed to address these challenges and support high-throughput, multi-dimensional MS workflows. By adopting a hybrid model that combines efficient binary storage for numerical data and both human and machine-readable metadata storage, mzPeak will reduce file sizes, accelerate data access, and offer a scalable, adaptable solution for evolving MS technologies. For researchers, mzPeak will enable enhanced interoperability across platforms, seamless support for complex workflows including ion mobility and MS imaging, and faster data access compared to existing community formats such as mzML. Its design will ensure data is managed in compliance with regulatory standards, essential for applications such as precision medicine and chemical safety, where long-term data integrity and accessibility are critical. For vendors, mzPeak provides a streamlined, open alternative to proprietary formats, reducing the burden of regulatory compliance while aligning with the industry's push for transparency and standardization. By offering a high-performance, interoperable solution, mzPeak positions vendors to meet customer demands for sustainable data management tools which will be able to handle emerging and future data types and workflows. mzPeak aspires to become the cornerstone of MS data management, empowering researchers, vendors, and developers to innovate and collaborate more effectively.

data formats↗
DOE OSTI2025

Quantifying Groundwater Response and Uncertainty in Beaver‐Influenced Mountainous Floodplains Using Machine Learning‐Based Model Calibration

Abstract Beavers ( Castor canadensis ) alter river corridor hydrology by creating ponds and inundating floodplains, and thereby improving surface water storage. However, the impact of inundation on groundwater, particularly in mountainous alluvial floodplains with permeable gravel/cobble layers overlain by a soil layer, remains uncertain. Numerical modeling across various floodplain structures considers topographic and sediment complexity and multidirectional flow, linking inundation to groundwater response. This study develops a model‐data integration workflow to address uncertainty in groundwater response to beaver‐induced inundations in a mountainous alluvial floodplain in the Upper Colorado River Basin. Uncertain factors include seasonal hydrologic dynamics, hydraulic conductivities, floodplain structures, and meteorological forcings. We employed an ensemble of groundwater models, based on geophysical and hydrologic data, with machine learning‐based calibration using a neural density estimator. This allowed us to quantify the vertical flux from the soil layer to the permeable gravel bed, the down‐valley underflow within the gravel bed, and their ratios. Results show a significant increase in the vertical flux relative to down‐valley underflow, from 2 during dry pond periods to 20 during wet periods, serving as an analogy for conditions without and with beaver ponds. The study highlights the influence of floodplain structure on groundwater storage, water balance, and water quality impacted by beaver ponds. A thick gravel bed layer, with a large down‐valley underflow, minimizes the effect of beaver‐induced inundation on water quality. We emphasize the need for field‐scale measurements of floodplain structure and improved characterization of evapotranspiration changes to reduce uncertainty in groundwater response. Plain Language Summary Beavers change the flow of water in river corridors by creating ponds, expanding wetlands, and flooding floodplains. This increases surface water area, promotes plant growth, and enhances biodiversity. However, the impact of this flooding on groundwater flow is not well understood, especially in mountainous areas with gravel layers where water moves easily beneath soil. In this study, we used numerical modeling to investigate how beaver ponds influence groundwater in a mountainous floodplain of the Upper Colorado River Basin. We adapted a machine learning method to validate our numerical models using multiple field data sets. Our findings show that beaver ponds significantly increase vertical water flow from the soil to the gravel during wet periods, compared to when the ponds are fully drained. The study also highlights the importance of floodplain structure in controlling both water flow in gravel layers along the river direction and vertical flow from the soil to the gravel with the presence of beavers. To reduce uncertainty in groundwater response, we emphasize the need for more field‐scale measurements of floodplain structure, hydraulic properties, and evapotranspiration changes. Key Points Floodplain structures and hydraulic conductivities are important for groundwater response with beaver ponds in mountainous floodplains Large down‐valley underflow in permeability‐stratified floodplains reduces beaver‐induced impacts on groundwater storage and water quality Machine learning‐based model calibration methods are effective for estimating posterior distributions of groundwater model parameters

Wang, Lijing↗
NASA NTRS2021

The Science Case for Spacecraft Exploration of the Uranian Satellites: Candidate Ocean Worlds in an Ice Giant System

The 27 satellites of Uranus are enigmatic, with dark surfaces coated by material that could be rich in organics. Voyager 2 imaged the southern hemispheres of Uranus’s five largest “classical” moons—Miranda, Ariel, Umbriel, Titania, and Oberon, as well as the largest ring moon, Puck—but their northern hemispheres were largely unobservable at the time of the flyby and were not imaged. Additionally, no spatially resolved data sets exist for the other 21 known moons, and their surface properties are essentially unknown. Because Voyager 2 was not equipped with a near-infrared mapping spectrometer, our knowledge of the Uranian moons’ surface compositions, and the processes that modify them, is limited to disk-integrated data sets collected by ground- and space-based telescopes. Nevertheless, images collected by the Imaging Science System on Voyager 2 and reflectance spectra collected by telescope facilities indicate that the five classical moons are candidate ocean worlds that might currently have, or had, liquid subsurface layers beneath their icy surfaces. To determine whether these moons are ocean worlds, and to investigate Uranus’s ring moons and irregular satellites, close-up observations and measurements made by instruments on board a Uranus orbiter are needed.

Richard J. Cartwright↗
DOE OSTI2024

ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability

Earth system predictability is challenged by the complexity of environmental dynamics and the multitude of variables involved. Current AI foundation models, although advanced by leveraging large and heterogeneous data, are often constrained by their size and data integration, limiting their effectiveness in addressing the full range of Earth system prediction challenges. To overcome these limitations, we introduce the Oak Ridge Base Foundation Model for Earth System Predictability (ORBIT), an advanced vision transformer model that scales up to 113 billion parameters using a novel hybrid tensor-data orthogonal parallelism technique. As the largest model of its kind, ORBIT surpasses the current climate AI foundation model size by a thousandfold. Performance scaling tests conducted on the Frontier supercomputer have demonstrated that ORBIT achieves 684 petaFLOPS to 1.6 exaFLOPS sustained throughput, with scaling efficiency maintained at 41% to 85% across 49,152 AMD GPUs. These breakthroughs establish new advances in AI-driven climate modeling and demonstrate promise to significantly improve the Earth system predictability.

Wang, Xiao↗
NASA NTRS1991

Results from a GPS Shuttle Training Aircraft flight test

A series of Global Positioning System (GPS) flight tests were performed on a National Aeronautics and Space Administration's (NASA's) Shuttle Training Aircraft (STA). The objective of the tests was to evaluate the performance of GPS-based navigation during simulated Shuttle approach and landings for possible replacement of the current Shuttle landing navigation aid, the Microwave Scanning Beam Landing System (MSBLS). In particular, varying levels of sensor data integration would be evaluated to determine the minimum amount of integration required to meet the navigation accuracy requirements for a Shuttle landing. Four flight tests consisting of 8 to 9 simulation runs per flight test were performed at White Sands Space Harbor in April 1991. Three different GPS receivers were tested. The STA inertial navigation, tactical air navigation, and MSBLS sensor data were also recorded during each run. C-band radar aided laser trackers were utilized to provide the STA 'truth' trajectory.

Saunders, Penny E.↗
DOE OSTI2024

Blockchain-Enabled Secure Device-to-Device Communication in Software-Defined Networking

The Internet of Things (IoT) continues to increase the demand for seamless communication among IoT devices. The rapid growth of IoT devices has led to an exponential increase in device-to-device (D2D) communication within the Software-Defined Networking (SDN), though it enables a flexible archi-tecture for managing network resources. However, traditional security models face challenges (e.g., Security, privacy, and trust) in addressing the dynamic and decentralized nature of these communications. Despite of these challenges, this paper proposes a novel approach that leverages blockchain technology to enhance the security, privacy, and trustworthiness of D2D communication within an SDN environment. The proposed approach integrates blockchain nodes in sDN components to establish a decentralized ledger for transparent and verifiable records. Smart contracts enforce authentication rules to ensure that only authenticated devices can access the network and engage in transactions securely. It also automates the security policies to ensure temper resistance execution using the cryptographic mechanism for data integrity and authentic communication. The Implementation of the proposed algorithms validates the resilience of the proposed approach against cyberattacks. Overall, the proposed approach enables efficient and secure D2D communication for resilient SDN infrastructure in IoT ecosystems.

Das, Debashis↗
DOE OSTI2026

Soil metagenomics umbrella narrative

Implementing accessible, authentic research experiences in introductory courses is challenging, particularly at institutions serving diverse student populations. To address this gap, we developed and deployed a Course-based Undergraduate Research Experience (CURE) focused on plant-microbe interactions in General Biology II at Northeastern Illinois University (NEIU), a minority-serving institution with a diverse student body. Students grew sugar beets (Beta vulgaris), extracted DNA from the rhizoplane, and used the Department of Energy Systems Biology Knowledgebase (KBase) for bioinformatic analysis to compare microbial relative abundance in fertilized versus unfertilized soil. Over five semesters, the CURE engaged 103 students and leveraged the intuitive KBase platform to make complex sequencing data accessible. Pre/post-course survey data revealed significant increases in student self-assessed research skills, including the ability to explain results and determine the types of data to collect. Furthermore, students reported significant gains in confidence related to experimental design and hypothesis development, alongside a strong increase in familiarity with KBase. Informal faculty feedback indicated high student engagement and appreciation for the real-world connections (e.g. food systems, agriculture, and health). This scalable, low-cost model effectively integrates data science tools into the foundational curriculum, demonstrating a potent strategy for boosting research skills and broadening participation in authentic scientific inquiry among diverse undergraduate students.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1985

Ordering actions for visibility

The notion of 'atomic actions' has been considered in recent work on data integrity and reliability. It has been found that the standard database operations of 'read' and 'write' carry with them severe performance limitations. For this reason, systems are now being designed in which actions operate on 'objects' through operations with more-or-less arbitrary semantics. An object (i.e., an instance of an abstract data type) comprises data, a set of operations (procedures) to manipulate the data, and a set of invariants. An 'action' is a unit of work. It appears to be primitive to its surrounding environment, and 'atomic' to other actions. Attention is given to the conventional model of nested actions, ordering requirements, the maximum possible visibility (full visibility) for items which must be controlled by ordering constraints, item management paradigms, and requirements for blocking mechanisms which provide the required visibility.

Mckendry, M. S.↗
NASA NTRS2008

NASA Tech Briefs, March 2008

Topics covered include: WRATS Integrated Data Acquisition System; Breadboard Signal Processor for Arraying DSN Antennas; Digital Receiver Phase Meter; Split-Block Waveguide Polarization Twist for 220 to 325 GHz; Nano-Multiplication-Region Avalanche Photodiodes and Arrays; Tailored Asymmetry for Enhanced Coupling to WGM Resonators; Disabling CNT Electronic Devices by Use of Electron Beams; Conical Bearingless Motor/Generators; Integrated Force Method for Indeterminate Structures; Carbon-Nanotube-Based Electrodes for Biomedical Applications; Compact Directional Microwave Antenna for Localized Heating; Using Hyperspectral Imagery to Identify Turfgrass Stresses; Shaping Diffraction-Grating Grooves to Optimize Efficiency; Low-Light-Shift Cesium Fountain without Mechanical Shutters; Magnetic Compensation for Second-Order Doppler Shift in LITS; Nanostructures Exploit Hybrid-Polariton Resonances; Microfluidics, Chromatography, and Atomic-Force Microscopy; Model of Image Artifacts from Dust Particles; Pattern-Recognition System for Approaching a Known Target; Orchestrator Telemetry Processing Pipeline; Scheme for Quantum Computing Immune to Decoherence; Spin-Stabilized Microsatellites with Solar Concentrators; Phase Calibration of Antenna Arrays Aimed at Spacecraft; Ring Bus Architecture for a Solid-State Recorder; and Image Compression Algorithm Altered to Improve Stereo Ranging.

Source record↗
DOE OSTI2025

Machine Learned Empirical Numerical Integrator from Simulated Data

Recently, a number of state-of-the-art surrogate machine learning (ML) models have been designed for global weather and climate prediction, which have been trained using reanalysis data products. Reanalysis data products are constructed using numerical model simulations that combine numerical integration of partial differential equations and parameterization schemes. These products are typically only archived and made available using coarsened spatial and temporal resolutions. This study explores the impact of the numerical generation methods used to produce the training datasets and the temporal resolution of those datasets on machine learning surrogate models. Using the nonlinear vector autoregression (NVAR) machine as an explainable ML technique, simple dynamical systems are emulated with ML models trained on data produced by three classical numerical integration schemes. NVAR is validated as a skillful ML method, capable of producing accurate predictions and, more importantly, reconstructing both the underlying dynamics and the numerical integration scheme used to generate the training data. However, the machine fails to generalize predictions on unseen test data generated by different numerical integration schemes, despite the underlying dynamical system being the same. This result provides a word of caution for the growing field of machine learning emulation of weather and climate dynamics. Furthermore, we illustrate using NVAR that training on temporally coarsened data may increase the required complexity of ML models and potentially introduce new numerical challenges. Finally, we discover that empirical integration schemes with arbitrary time-stepping sizes can be constructed directly from the data, which implies a potential for the development of empirical numerical integration schemes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Feasibility Study to Interactive Workshop: Building End-user Capacity to Integrate Earth Observation Data into Federally Endangered Atlantic Salmon (Salmo salar) Habitat Monitoring in Main

Changes in temperature and precipitation patterns, along with alterations in land cover threaten ongoing conservation efforts for Federally Endangered Atlantic salmon (Salmo salar) in Maine. Earth observation data offers a unique perspective for habitat monitoring that can complement habitat restoration and conservation activity on the ground. As a dual capacity building program, the NASA DEVELOP National Program strives to build the capacity of program participants by leveraging Earth observation data to address environmental concerns across the globe, while also building capacity in partner organizations to integrate Earth observation data into their decision making practices. Between September 2021 and August 2022, three NASA DEVELOP teams demonstrated the feasibility of utilizing NASA Earth observations including Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), Terra MODIS, Landsat 5 Thematic Mapper (TM), Landsat 8 Operation Land Imager (OLI), and Shuttle Radar Topography Mission (SRTM) in conjunction with Sentinel-2 MultiSpectral Instrument (MSI) to assess temperature, precipitation, and land use land cover (LULC) over time throughout salmon habitat in Maine. While the first two teams completed projects that were categorized as NASA DEVELOP’s traditional feasibility projects, the third and final project team generated resources and planned an interactive workshop to transfer project methods to end-user organizations. Ultimately, the goal of this work was to not only inform the partner’s ongoing salmon population recovery and habitat restoration initiatives but provide tools that allow partner organizations to continue integrating Earth observation data into their work beyond their partnership with the program. This project serves as a case study within the NASA DEVELOP Program and provides lessons learned for moving beyond traditional feasibility studies to more interactive partner engagement and knowledge transfer practices.

Nicole Ramberg-Pihl↗
NASA NTRS1975

An investigation of errors and data processing techniques for an RF multilateration system

The development of an RF Multilateration system to provide accurate position and velocity measurements during the approach and landing phase of Vertical Takeoff Aircraft operation is discussed. The system uses an angle-modulated ranging signal to provide both range and range rate measurements between an aircraft transponder and multiple ground stations. Range and range rate measurements are converted to coordinate measurements and the coordinate and coordinate rate information is transmitted by an integral data link to the aircraft. Data processing techniques are analyzed to show advantages and disadvantages. Error analyses are provided to permit a comparison of the various techniques.

Britt, C. L., Jr.↗
NASA NTRSDate not supplied

Comparison of Boeing 777 Landing Gear Noise Simulations with Flight Test Data

Acoustic phased microphone array measurements of aircraft flyover noise acquired during the 2005 Quiet Technology Demonstrator II test were used to assess the accuracy of high-fidelity, full-scale simulations of landing gear noise produced by a large civilian aircraft. The simulations, conducted with the lattice Boltzmann solver PowerFLOW®, used a highly accurate digital model of a Boeing 777-300ER aircraft with the nose and main landing gear components replicating the full-scale geometries. The simulations were performed for aircraft parameters that matched those recorded during the flyover test conditions. For benchmarking purposes, several aircraft configurations were simulated: a) nose landing gear deployed with main landing gear and wing high-lift devices stowed, b) nose and main landing gear deployed with wing high-lift devices stowed and c) nose and main landing gear with wing high-lift devices deployed. To facilitate direct comparison with measured data, the simulated data sets were used to generate synthetic pressure records at the same array microphone locations as those used during the flight test. Broadly self-consistent beamforming techniques and procedures were used to process the synthetic pressure records and the measured data. Integration of select regions of the beamform maps containing the nose or main landing gear yielded good agreement between predicted and measured integrated far-field spectra for forward directivity angles where airframe noise is more prominent.

airframe noise↗
NASA NTRS1989

Fitting theoretical photometric functions to asteroid phase curves

The ways in which variations in the parameters of Hapke's (1981, 1984, 1986) theoretical photometric function for fitting photometric phase data of asteroids can affect the shape of the theoretical curve are considered. It is noted that, between phase angles of 2 and 25 deg, the opposition effect parameters, the roughness parameter, and the single-particle phase function parameter have similar effects on the shape and the photometric curve and are difficult to separate on the basis of disk-integrated data alone. The uniqueness of asteroid surface properties deduced from phase curve observations over a limited phase-angle range is judged to remain questionable.

Domingue, Deborah↗
NASA NTRS1983

Data management applications

Kennedy Space Center's primary institutional computer is a 4 megabyte IBM 4341 with 3.175 billion characters of IBM 3350 disc storage. This system utilizes the Software AG product known as ADABAS with the on line user oriented features of NATURAL and COMPLETE as a Data Base Management System (DBMS). It is operational under the OS/VSI and is currently supporting batch/on line applications such as Personnel, Training, Physical Space Management, Procurement, Office Equipment Maintenance, and Equipment Visibility. A third and by far the largest DBMS application is known as the Shuttle Inventory Management System (SIMS) which is operational on a Honeywell 6660 (dedicated) computer system utilizing Honeywell Integrated Data Storage I (IDSI) as the DBMS. The SIMS application is designed to provide central supply system acquisition, inventory control, receipt, storage, and issue of spares, supplies, and materials.

Source record↗
NASA NTRS2023

Exploration Medical Capability Clinical Decision Support System Architecture Recommendation

A new era in space exploration has arrived with the goal of establishing a long-term presence on the Moon and using those learning to take the next giant leap: sending the first astronauts to Mars. These ambitious goals will require significant changes in in-flight medical care due to constraints on mass, volume, power, crew time, and medical evacuation capabilities. Furthermore, while crews currently rely on real-time communications with ground-based medical providers, as distance from Earth increases, so do communication delays and disruptions. The crew will need to autonomously detect, diagnose, treat, and prevent medical events. These constraints require development of transformative solutions and new technologies. Through participation in the Human Research Program’s (HRP) Exploration Medical Capability (ExMC) research, NASA has developed and demonstrated a Medical Data Architecture (MDA), a platform upon which a robust capability of Clinical Decision Support (CDS) can be built. As a result, NASA is now positioned to build an advanced autonomous CDS System (CDSS) which will aid in crew health decision-making. The CDSS combines data management aspects of a system that would lead to the autonomy required by NASA-STD-3001, Section 3, Health and Medical Care Standards. The project addresses the ExMC gap Medical-701: We need to increase inflight medical capabilities and identify new capabilities that (a) maximize benefit and/or (b) reduce “costs” on human system/mission/vehicle resources. This architecture recommendation document introduces an integrated data architecture that provides self-sufficient medical care for crews on long-duration spaceflight missions.

ExMC↗