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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Magnetospheric ULF waves observed during the major magnetospheric compression of November 1, 1984

The magnetospheric ULF waves observed during the magnetospheric compression event of November 1, 1984, were investigated using the magnetic field and medium-energy ion data obtained by the AMPTE Charge Composition Explorer. The electric field of ULF waves was inferred from the particle data, using a method developed on the basis of the gyration acceleration mechanism. Three types of waves were found: (1) a 10-15-min wave with perturbations in the magnetic field intensity and in the flux of ions; (2) a 3-5-min standing Alfven wave with perturbations in the azimuthal magnetic field component and in the radial component of the electric field; and (3) a 2-5-min irregular disturbance near the magnetopause, which involves all components of the magnetic field and the intensity of the ion flux. The origins of these waves are discussed.

Takahashi, K.↗
NASA NTRS2008

Development of Time-Distance Helioseismology Data Analysis Pipeline for SDO/HMI

The Helioseismic and Magnetic Imager of SDO will provide uninterrupted 4k x 4k-pixel Doppler-shift images of the Sun with approximately 40 sec cadence. These data will have a unique potential for advancing local helioseismic diagnostics of the Sun's interior structure and dynamics. They will help to understand the basic mechanisms of solar activity and develop predictive capabilities for NASA's Living with a Star program. Because of the tremendous amount of data the HMI team is developing a data analysis pipeline, which will provide maps of subsurface flows and sound-speed distributions inferred form the Doppler data by the time-distance technique. We discuss the development plan, methods, and algorithms, and present the status of the pipeline, testing results and examples of the data products.

DuVall, T. L., Jr.↗
DOE OSTI2026

Semi-Analytical Hierarchical Bayesian Inference of Nonlinear Model Structure in Stochastic Dynamics: Applied to Compartmental Models of Infectious Diseases

A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential equation-based models are often simplified mechanistic or phenomenological representations, robust inference from noisy measurement data requires explicit treatment of model error and uncertainty. Model error and time-varying parameters can be represented as random processes, enabling inference while making minimal assumptions about the underlying sources of discrepancy and variability. Adopting stochastic differential equation representations affords the model significant flexibility, but can also render it susceptible to overfitting during statistical inversion, where the inferred model may track noise rather than the underlying signal. To alleviate the effects of overfitting and to enable the discovery of the optimal sparse representation of the time-invariant parameters, a Bayesian sparse learning algorithm is embedded within the framework. This sparse learning framework adopts an approximate hierarchical Bayesian setting defined by a series of semi-analytical expressions. The model structure inference framework is validated using a stochastic compartmental model for tracking and forecasting active cases of an infectious disease. Compartmental models describe population-level infectious disease dynamics through interactions among population fractions grouped by disease state. Mathematically, such models consist of a system of coupled ordinary differential equations. This example adopts an expressive compartmental model that includes multiple possible interactions between disease states, motivated by early uncertainty surrounding COVID-19 reinfection dynamics and their implications for long-term epidemic forecasting. The sparse learning exercise permits the inference of a priori unknown epidemiological dynamics from simulated public health data, discovering the nested compartmental model that optimizes the trade-off between average data-fit and model complexity. It is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Foundation Models for Zero-Shot Segmentation of Scientific Images without AI-Ready Data

Zero-shot and prompt-based models have excelled at visual reasoning tasks by leveraging large-scale natural image corpora, but they often fail on sparse and domain-specific scientific image data. We introduce Zenesis, a no-code interactive computer vision platform designed to reduce data readiness bottlenecks in scientific imaging workflows. Zenesis integrates lightweight multimodal adaptation for zero-shot inference on raw scientific data, human-in-the-loop refinement, and heuristic-based temporal enhancement. We validate our approach on Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) datasets of catalyst-loaded membranes. Zenesis outperforms baselines, achieving an average accuracy of 0.947, Intersection over Union (IoU) of 0.858, and Dice score of 0.923 on amorphous catalyst samples; and 0.987 accuracy, 0.857 IoU, and 0.923 Dice on crystalline samples. These results represent a significant performance gain over conventional methods such as Otsu thresholding and standalone models like the Segment Anything Model (SAM). Zenesis enables effective image segmentation in domains where annotated datasets are limited, offering a scalable solution for scientific discovery.

Mukherjee, Shubhabrata↗
NASA NTRS1974

Effect of aerosol emission on the inference of trace gas concentrations from limb radiance data

This note reports on an analysis of atmospheric emission spectra in the 10-12 micron region measured with a limb-oriented balloon-borne spectrometer. Data clearly show the existence of an additional feature at 10.8 microns which is probably due to measurement contamination from aerosols. The 10.8 micron emitter has a strong reducing effect on the HNO3 mixing ratio profile as determined by application of a symmetrical band shape model to the 10.8 spectral feature.

Remsberg, E. E.↗
NASA NTRS1983

Inferring electric fields and currents from ground magnetometer data - A test with theoretically derived inputs

Advanced techniques considered by Kamide et al. (1981) seem to have the potential for providing observation-based high time resolution pictures of the global ionospheric current and electric field patterns for interesting events. However, a reliance on the proposed magnetogram-inversion schemes for the deduction of global ionospheric current and electric field patterns requires proof that reliable results are obtained. 'Theoretical' tests of the accuracy of the magnetogram inversion schemes have, therefore, been considered. The present investigation is concerned with a test, involving the developed KRM algorithm and the Rice Convection Model (RCM). The test was successful in the sense that there was overall agreement between electric fields and currents calculated by the RCM and KRM schemes.

Wolf, R. A.↗
NASA NTRS1984

A high performance magnetoplasmadynamic thruster

A flared-anode MPD thruster has been modified to permit injection of propellant through the backplate near the anode wall. At 6 g/sec argon, this thruster displays an onset current of 41.4 kA, almost double the value observed for propellant injection at the cathode and intermediate radial positions. A magnetic field survey of the interelectrode region shows current density is highest at the upstream and downstream ends of the chamber. The operating efficiency at onset current inferred from magnetic field data exceeds 50 percent, but swinging-gate thrust stand measurements reveal a progressive divergence between inferred and actual thrust with increasing power. Near onset, the measured thrust is approximately 20 percent lower than that inferred from magnetic probing. Explanations for this behavior have been explored with viscous drag emerging as the most probable cause of performance degradation.

Wolff, M.↗
NASA NTRS2000

Climate and infectious disease: use of remote sensing for detection of Vibrio cholerae by indirect measurement

It has long been known that cholera outbreaks can be initiated when Vibrio cholerae, the bacterium that causes cholera, is present in drinking water in sufficient numbers to constitute an infective dose, if ingested by humans. Outbreaks associated with drinking or bathing in unpurified river or brackish water may directly or indirectly depend on such conditions as water temperature, nutrient concentration, and plankton production that may be favorable for growth and reproduction of the bacterium. Although these environmental parameters have routinely been measured by using water samples collected aboard research ships, the available data sets are sparse and infrequent. Furthermore, shipboard data acquisition is both expensive and time-consuming. Interpolation to regional scales can also be problematic. Although the bacterium, V. cholerae, cannot be sensed directly, remotely sensed data can be used to infer its presence. In the study reported here, satellite data were used to monitor the timing and spread of cholera. Public domain remote sensing data for the Bay of Bengal were compared directly with cholera case data collected in Bangladesh from 1992-1995. The remote sensing data included sea surface temperature and sea surface height. It was discovered that sea surface temperature shows an annual cycle similar to the cholera case data. Sea surface height may be an indicator of incursion of plankton-laden water inland, e.g., tidal rivers, because it was also found to be correlated with cholera outbreaks. The extensive studies accomplished during the past 25 years, confirming the hypothesis that V. cholerae is autochthonous to the aquatic environment and is a commensal of zooplankton, i.e., copepods, when combined with the findings of the satellite data analyses, provide strong evidence that cholera epidemics are climate-linked.

Cholera/epidemiology↗
DOE OSTI2025

Announcing the Biomedical Data Translator: Initial Public Release

ABSTRACT The growing availability of biomedical data offers vast potential to improve human health, but the complexity and lack of integration of these datasets often limit their utility. To address this, the Biomedical Data Translator Consortium has developed an open‐source knowledge graph–based system—Translator—designed to integrate, harmonize, and make inferences over diverse biomedical data sources. We announce here Translator's initial public release and provide an overview of its architecture, standards, user interface, and core features. Translator employs a scalable, federated, knowledge graph framework for the integration of clinical, genomic, pharmacological, and other biomedical knowledge sources, enabling query retrieval, inference, and hypothesis generation. Translator's user interface is designed to support the exploration of knowledge relationships and the generation of insights, without requiring deep technical expertise and gradually revealing more detailed evidence, provenance, and confidence information, as needed by a given user. To demonstrate Translator's application and impact, we highlight features of the user interface in the context of three real‐world use cases: suggesting potential therapeutics for patients with rare disease; explaining the mechanism of action of a pipeline drug; and screening and validating drug candidates in a model organism. We discuss strengths and limitations of reasoning within a largely federated system and the need for rich concept modeling and deep provenance tracking. Finally, we outline future directions for enhancing Translator's functionality and expanding its data sources. Translator represents a significant step forward in making complex biomedical knowledge more accessible and actionable, aiming to accelerate translational research and improve patient care.

Research & Experimental Medicine↗
NASA NTRS2021

An Ontology-Based Virtual Orrery

Tutorials in this technical memorandum explain how to use the Protégé ontology editor to import data from Excel and export data from Protégé in the JavaScript Object Notation Linked Data (JSON-LD) format then how to transform the JSON-LD file into variables for web apps. These tutorials apply JavaScript and R programming languages and 3D graphics libraries. Ontologies enable standardization, data sharing, and semantic interoperability. An ontology models a domain of knowledge as taxonomies of annotated classes, data properties, relational object properties, and inference rules. This technical memorandum includes a data dictionary for a subset of the Space Situational Awareness Ontology (SSAO).

Ontologies↗
NASA NTRS1979

Synthesis of regional crust and upper-mantle structure from seismic and gravity data

Available seismic and ground based gravity data are combined to infer the three dimensional crust and upper mantle structure in selected regions. This synthesis and interpretation proceeds from large-scale average models suitable for early comparison with high-altitude satellite potential field data to more detailed delineation of structural boundaries and other variations that may be significant in natural resource assessment. Seismic and ground based gravity data are the primary focal point, but other relevant information (e.g. magnetic field, heat flow, Landsat imagery, geodetic leveling, and natural resources maps) is used to constrain the structure inferred and to assist in defining structural domains and boundaries. The seismic data consists of regional refraction lines, limited reflection coverage, surface wave dispersion, teleseismic P and S wave delay times, anelastic absorption, and regional seismicity patterns. The gravity data base consists of available point gravity determinations for the areas considered.

Alexander, S. S.↗
NASA NTRS1980

Development and usage of a false color display technique for presenting Seasat-A scatterometer data

A computer generated false color program which creates digital multicolor graphics to display geophysical surface parameters measured by the Seasat-A satellite scatterometer (SASS) is described. The data is incrementally scaled over the range of acceptable values and each increment and its data points are assigned a color. The advantage of the false color display is that it visually infers cool or weak data versus hot or intense data by using the rainbow of colors. For example, with wind speeds, levels of yellow and red could be used to imply high winds while green and blue could imply calmer air. The SASS data is sorted into geographic regions and the final false color images are projected onto various world maps with superimposed land/water boundaries.

Jackson, C. B.↗
NASA NTRS1990

Arctic multiyear ice classification and summer ice cover using passive microwave satellite data

Passive microwave data collected by Nimbus 7 were used to classify and monitor the Arctic multilayer sea ice cover. Sea ice concentration maps during several summer minima are analyzed to obtain estimates of ice floes that survived summer, and the results are compared with multiyear-ice concentrations derived from these data by using an algorithm that assumes a certain emissivity for multiyear ice. The multiyear ice cover inferred from the winter data was found to be about 25 to 40 percent less than the summer ice-cover minimum, indicating that the multiyear ice cover in winter is inadequately represented by the passive microwave winter data and that a significant fraction of the Arctic multiyear ice floes exhibits a first-year ice signature.

Comiso, J. C.↗
DOE OSTI2024

Deployment of inference as a service at the US CMS Tier-2 data centers

Coprocessors, especially GPUs, will be a vital ingredient of data production workflows at the HL-LHC. At CMS, the GPU-as-a-service approach for production workflows is implemented by the SONIC project (Services for Optimized Network Inference on Coprocessors). SONIC provides a mechanism for outsourcing computationally demanding algorithms, such as neural network inference, to remote servers, where requests from multiple clients are intelligently distributed across multiple GPUs by a load-balancing service. This talk highlights the recent progress in deploying SONIC at selected U.S. CMS Tier-2 data centers. Using realistic CMS Run3 data processing workflows, such as those containing transformer-based algorithms, we demonstrate how SONIC is integrated into the production-like environment to enable accelerated inference offloading. We will present developments from both the client and server sides, including production job and data center configurations for NVIDIA and AMD GPUs. We will also present performance scaling benchmarks and discuss the challenges of operating SONIC in CMS production, such as server discovery, GPU saturation, fallback server logic, etc.

Holzman, Burt↗
NASA NTRS1972

Improvement of synoptic scale moisture and wind field analyses using the Nimbus 4 THIR 6.7 micron observations

The Nimbus 4 temperature-humidity infrared radiometer (THIR) monitors radiation in the 6.5 to 7.2 micron water vapor absorption with a 23 kilometer spatial resolution at the sub-satellite point. Radiation monitored in this spectral region results primarily from emission in the 250 to 500 millibar region of the upper troposphere. The THIR 6.7 micron observations are readily available in photofacsimile imagery form which shows very distinctive patterns associated with spatial variations in atmospheric water vapor. These radiometric observations were combined in several instances with moisture values measured in the upper troposphere by the standard radiosonde network. In each instance, the result is a much more consistent analysis showing increased spatial detail that agrees with the radiometric observations and does not compromise the conventional data. The improved moisture analyses show relatively dry and moist tongues that are very difficult or impossible to infer from the conventional data alone. The patterns in the moisture fields can be tracked over 12 and 24 hour periods. In addition, by keeping in mind the advective properties of the moisture field, success was achieved in improving streamline analyses at the 400 mb level over data sparse regions on a global scale.

Steranko, J.↗
NASA NTRS1973

Application of Nimbus 4 THIR 6.7-micron observations to regional and global moisture and wind field analyses.

The Nimbus 4 temperature-humidity infrared radiometer (THIR) monitors radiation in the 6.5 to 7.2-micron water vapor absorption region with a 23-km spatial resolution at the subsatellite point. Radiation monitored in this spectral region results primarily from emission in the 250 to 500-mb region of the upper troposphere. The THIR 6.7-micron observations are readily available in photofacsimile imagery form which shows very distinctive patterns associated with spatial variations in atmospheric water vapor. These radiometric observations have been combined in several instances with moisture values measured in the upper troposphere by the standard radiosonde network. In each instance, the result is a much more consistent analysis showing increased spatial detail that agrees with the radiometric observations and does not compromise the conventional data. The improved moisture analyses show relatively dry and moist tongues that are very difficult or impossible to infer from the conventional data alone.

Steranka, J.↗
DOE OSTI2024

Monitoring of ground water table depth and soil moisture at the Point Reyes field site

Ground water table (GWT) depth and soil moisture (SM) have been monitored at several locations at the Point Reyes field site (Californian coastal grassland) from 2021 to 2024. Monitoring is still on-going and data may be added to this archive at later time. The SM data have been acquired using Teros 12 Meter soil moisture sensors placed at 10, 30, 60 and 90 cm depth at 5 locations along a small hillslope. These sensors also collect soil temperature and bulk conductance. In addition, some collocated sensors provide pore pressure and Photochemical Reflectance Index (PRI). The GWT depth has been inferred from various type of Onset pressure transducers. The pressure measurements have been corrected for atmospheric pressure variations and sensor position relative to the ground surface to infer GWT depth, as well as with RTK GPS data to infer GWT elevation. The GWT data have been acquired at 5 distinct locations from 2020 to 2024 with the sensors placed at about 4 m depth. In addition, GWT data has been acquired for the 2023-2024 period with sensors located in 1 m deep shallow wells installed near each deeper well. This data is intended to evaluate possibly different dynamic in shallow (perched) and deep aquifer. The datasets are all provided in csv format. Please note that the interpretation of the GWT data needs to be done with consideration of environmental and well characteristics at the site and uncertainty in various variables. For more information on GWT and SM data, please contact the author.

54 ENVIRONMENTAL SCIENCES↗