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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

AI foundation models for experimental fusion tasks

Artificial Intelligence (AI) foundation models, while successful in various domains of language, speech, and vision, have not been adopted in production for fusion energy experiments. This brief paper presents how AI foundation models can be used for fusion energy diagnostics, enabling, for example, visual automated logbooks to provide greater insights into chains of plasma events in a discharge, in time for between-shot analysis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Editorial: Visualizing offline and live data with AI (VOLDA) workshop first edition Princeton 11-13th June 2024

The first edition of the Visualizing Offline and Live Data with AI’ (VOLDA) Workshop took place at the Princeton University Campus, Mader Hall from 11 to 13 June 2024. This annual workshop held for the first time aims at bringing together the fusion community to discuss the challenges brought by Artificial Intelligence (AI) and visualizing large datasets in fusion experiment and simulation.

artificial intelligence↗
DOE OSTI2024

Toward Smart, Automated Junctional Tourniquets—AI Models to Interpret Vessel Occlusion at Physiological Pressure Points

Hemorrhage is the leading cause of preventable death in both civilian and military medicine. Junctional hemorrhages are especially difficult to manage since traditional tourniquet placement is often not possible. Ultrasound can be used to visualize and guide the caretaker to apply pressure at physiological pressure points to stop hemorrhage. However, this process is technically challenging, requiring the vessel to be properly positioned over rigid boney surfaces and applying sufficient pressure to maintain proper occlusion. As a first step toward automating this life-saving intervention, we demonstrate an artificial intelligence algorithm that classifies a vessel as patent or occluded, which can guide a user to apply the appropriate pressure required to stop flow. Neural network models were trained using images captured from a custom tissue-mimicking phantom and an ex vivo swine model of the inguinal region, as pressure was applied using an ultrasound probe with and without color Doppler overlays. Using these images, we developed an image classification algorithm suitable for the determination of patency or occlusion in an ultrasound image containing color Doppler overlay. Separate AI models for both test platforms were able to accurately detect occlusion status in test-image sets to more than 93% accuracy. In conclusion, this methodology can be utilized for guiding and monitoring proper vessel occlusion, which, when combined with automated actuation and other AI models, can allow for automated junctional tourniquet application.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Bridging Additive Manufacturing and Electronics Printing in the Age of AI

Printing techniques have been instrumental in developing flexible and stretchable electronics, including organic light-emitting diode displays, organic thin film transistor arrays, electronic skins, organic electrochemical transistors for biosensors and neuromorphic computing, as well as flexible solar cells with low-cost processes such as inkjet printing, ultrasonic nozzle, roll-to-roll coating. The rise of additive manufacturing provides even more opportunities to print electronics in automated and customizable ways. In this work, we will review the current technologies of printing electronics (including printed batteries, supercapacitors, fuel cells, and sensors), especially with 3D printing. In this age of ongoing AI revolution, the application of AI algorithms is discussed in terms of combining them with 3D printing and electronics printing for a future with automated optimization, sustainable design, and customizable and scalable manufacturing.

Chemistry↗
DOE OSTI2024

Towards Next-Generation Urban Decision Support Systems through AI-Powered Construction of Scientific Ontology Using Large Language Models—A Case in Optimizing Intermodal Freight Transportation

The incorporation of Artificial Intelligence (AI) models into various optimization systems is on the rise. However, addressing complex urban and environmental management challenges often demands deep expertise in domain science and informatics. This expertise is essential for deriving data and simulation-driven insights that support informed decision-making. In this context, we investigate the potential of leveraging the pre-trained Large Language Models (LLMs) to create knowledge representations for supporting operations research. By adopting ChatGPT-4 API as the reasoning core, we outline an applied workflow that encompasses natural language processing, Methontology-based prompt tuning, and Generative Pre-trained Transformer (GPT), to automate the construction of scenario-based ontologies using existing research articles and technical manuals of urban datasets and simulations. From these ontologies, knowledge graphs can be derived using widely adopted formats and protocols, guiding various tasks towards data-informed decision support. The performance of our methodology is evaluated through a comparative analysis that contrasts our AI-generated ontology with the widely recognized pizza ontology, commonly used in tutorials for popular ontology software. We conclude with a real-world case study on optimizing the complex system of multi-modal freight transportation. Our approach advances urban decision support systems by enhancing data and metadata modeling, improving data integration and simulation coupling, and guiding the development of decision support strategies and essential software components.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors for Modeling Radio Frequency Heating in Fusion Energy Science

This work presents a detailed case study on using Generative AI (GenAI) to develop AI surrogates for simulation models in fusion energy research. The scope includes the methodology, implementation, and results of using GenAI to assist in model development and optimization, comparing these results with previous manually developed models.

Artificial Intelligence (cs.AI)↗
DOE OSTI2025

Energy and AI: Evaluating Future Grid and Water Stress Due to Data Centers

Projections of the need for new data centers to support Artificial Intelligence (AI) are large but highly uncertain. Recent projections indicate up to a 15% annual growth rate in data center electricity demand within the next 5-10 years. Given that most electric utilities are required to have a reserve margin of roughly the same magnitude as the projected growth in demand, these new data center loads could soon threaten resource adequacy and reliability unless data centers build their own generation, interruptible loads are negotiated, commensurate new capacity and/or transmission is built, or some combination of these options. Similarly, depending on the cooling technology and geographic location of new data centers, they could threaten water adequacy in water scarce regions. This presentation highlights the grid and water implications of new data centers to support AI.

Mongird, Kendall (ORCID:0000000328077088)↗
NASA NTRS1985

Application of Modern Aperture Integration (AI) and Geometrical Theory of Diffraction (GTD) Techniques for Analysis of Large Reflector Antennas

The application of aperture integration (AI) and geometrical theory of diffraction (GTO) techniques to analyze large reflector antennas is outlined. The following techniques were used: computer modeling, validation of analysis and computer codes, computer aided design modifications, limitation on the conventional aperture integration (AIC) method, extended aperture integration (AIE) method, the AIE method for feed scattering calculations, near field probing predictions for 15 meter model, limitation on AIC for surface tolerance effects, aperture integration on the surface (AIS) method, and AIC and GTD calculations for compact range reflector.

Rudduck, R. C.↗
NASA NTRS1985

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures.

Firschein, O.↗
NASA NTRS1984

Remote detection of geobotanical anomalies associated with hydrocarbon microseepage using thematic mapper simulator (TMS) and airborne imaging spectrometer (AIS) data

An interpretation of TMS and AIS data sets collected from Lost River, West Virginia, are presented, along with a brief review of the supervised vegetation classification approach to vegetation mapping used at Lost River. A preliminary study of AIS data suggests that contiguous high-spectral resolution data from a very limited portion of the spectrum (1.2-1.5 micron) provide a greater discriminatory capability than do broad-band sensors such as the TMS covering of wider spectral range (0.45-2.35 microns).

Rock, B. N.↗
NASA NTRS1985

Preliminary Analysis of AIS Spectral Data Acquired from Semi-arid Shrub Communities in the Owens Valley, California

Spectral characteristics of semic-arid plant communities using 128 channel airborne imaging spectrometer (AIS) data acquired on October 30, 1984. Both field and AIS spectra of vegetation were relatively featureless and differed from substrate spectra primarily in albedo. Unvegetated sand dunes were examined to assess spectral variation resulting from topographic irregularity. Although shrub cover as low as 10% could be detected on relatively flat surfaces, such differences were obscured in more heterogeneous terrain. Sagebrush-covered fans which had been scarred by fire were studied to determine the effect of changes in plant density on reflectance. Despite noise in the atmospherically corrected spectra, these provide better resolution of differences in plant density than spectra which are solar-corrected only. A high negative correlation was found between reflectance and plant cover in areas which had uniform substrates and vegetation types. A lower correlation was found where vegetation and substrates were more diverse.

Ustin, S. L.↗
NASA NTRS1985

A First Look at Airborne Imaging Spectrometer (AIS) Data in an Area of Altered Volcanic Rocks and Carbonate Formations, Hot Creek Range, South Central Nevada

Three flight lines of Airborne Imaging Spectrometer (AIS) data were collected in 128 bands between 1.2 and 2.4 microns in the Hot Creek Range, Nevada on July 25, 1984. The flight lines are underlain by hydrothermally altered and unaltered Paleozoic carbonates and Tertiary rhyolitic to latitic volcanics in the Tybo mining district. The original project objectives were to discriminate carbonate rocks from other rock types, to distinguish limestone from dolomite, and to discriminate carbonate units from each other using AIS imagery. Because of high cloud cover over the prime carbonate flight line and because of the acquisition of another flight line in altered and unaltered volcanics, the study has been extended to the discrimination of alteration products. In an area of altered and unaltered rhyolites and latites in Red Rock Canyon, altered and unaltered rock could be discriminated from each other using spectral features in the 1.16 to 2.34 micron range. The altered spectral signatures resembled montmorillonite and kaolinite. Field samples were gathered and the presence of montmorillonite was confirmed by X-ray analysis.

Feldman, S. C.↗
NASA NTRS1985

Evaluation of AIS Data for Agronomic and Rangeland Vegetation: Preliminary Results for August 1984 Flight over Nebraska Sandhills Agricultural Laboratory

Since 1978 scientists from the Center for Agricultural Meteorology and Climatology at the University of Nebraska have been conducting research at the Sandhills Agricultural Laboratory on the effects of water stress on crop growth, development and yield using remote sensing techniques. We have been working to develop techniques, both remote and ground-based, to monitor water stress, phenological development, leaf area, phytomass production and grain yields of corn, soybeans and sorghum. Because of the sandy soils and relatively low rainfall at the site it is an excellent location to study water stress without the necessity of installing expensive rainout shelters. The primary objectives of research with the airborne imaging spectrometer (AIS) data collected during an August 1984 flight over the Sandhills Agricultural Laboratory are to evaluate the potential of using AIS to: (1) discriminate crop type; (2) to detect subtle architectural differences that exist among different cultivars or hybrids of agronomic crops; (3) to detect and quantify, if possible, the level of water stress imposed on the crops; and (4) to evaluate leaf area and biomass differences for different crops.

Blad, B. L.↗
NASA NTRS1985

Preliminary Evaluation of AIS Spectra Along a Topographic/moisture Gradient in the Nebraska Sandhills

Six spectral plots, each summarizing single-pixel reflectance for 128 channels of Airborne Imaging Spectrometer (AIS) data, were examined. The six sample pixels were located along a topographic/moisture gradient from lake surface to dune top in the Nebraska Sandhills. AIS spectra for various moisture regimes/vegetative zones appear quite logical, with a general positive relationship between increasing elevation (i.e., decreasing access of plant roots to water) and increasing reflectance in the spectral regions diagnostic of leaf-water content (i.e., bands centered on 1.65 and 2.20 microns).

Runquist, D. C.↗
NASA NTRS1985

Discrimination of Coastal Vegetation and Biomass Using AIS Data

The Airborne Imaging Spectrometer (AIS) was flown over a coastal wetlands region near Lewes, Delaware, adjacent to the Delaware Bay on 16 August 1984. Using the AIS data, it was possible to discriminate between four different types of wetland vegetation canopies: (1) trees; (2) broadleaf herbaceous plants (e.g., Acnida cannabina, Hisbiscus moscheutos); (3) the low marsh grass Spartina alterniflora; and (4) the high marsh grasses Distichlis spicata and Spartina patens. The single most useful region of the spectrum was that between 1.40 and 1.90 microns, where slopes of portions of the radiance curve and ratios of radiance at particular wavelengths were significantly different for the four canopy types. The ratio between the highest digital number in the 1.40 to 1.90 microns and .84 to .94 microns regions and a similar ratio between the peaks in radiance in the 1.12 to 1.40 microns and .84 to .94 microns spectral regions were also very effective at discriminating between vegetation types. Differences in radiance values at various wavelengths between samples of the same vegetation type could potentially be used to estimate biomass.

Gross, M. F.↗
NASA NTRS1985

AIS Investigation of Agricultural Monocultures

Airborne Imaging Spectrometer (AIS) data were acquired over an agricultural area in eastern San Joaquin County, California in July, 1984. Cover type information was subsequently collected for all fields along this flight line. The lack of detailed ground data on individual fields, however, limited AIS data analysis to a qualitative comparison of the spectral reflectance curves for a total of nine cover types. Based on this analysis, it appears that cover types with a positive slope in the 1550 to 1700 nm region have a higher spectral response in the 1200 to 1300 nm region compared to those cover types with a negative slope in the 1550 to 1700 nm region. Within cover type, spectral variability was also found to be greater than that between cover types. Given the lack of additional field data, the reason for these differences is a matter of speculation.

Wood, B. L.↗
NASA NTRS1986

The use of Airborne Imaging Spectrometer (AIS) data to differentiate marsh vegetation

The Airborne Imaging Spectrometer (AIS) is a high spectral resolution (9.6-nm-wide bands between 0.9 and 2.4 microns) instrument. Analysis of AIS data revealed significant differences in characteristics of the spectral radiance curves of four types of wetland vegetation canopies (trees, broadleaf herbaceous, Spartina alterniflora, and S. patens/Distichlis spicata) in Delaware, enabling them to be distinguished. The single most useful spectral region was that between 1.40 and 1.90 microns. Differences in radiance values at various wavelengths between samples of the same vegetation type could potentially be used to estimate biomass. Thus, high spectral resolution spectrometry appears to have significant value for remote sensing studies of wetland vegetation.

Gross, M. F.↗
NASA NTRS1986

Analysis of AIS radiometry with emphasis on determination of atmospheric properties and surface reflectance

Airborne Imaging Spectrometer data from Mono Lake, California, were studied in order to establish spectral radiance of test areas under solar illumination. The objective is to provide a method of atmospheric correction for major absorbers from the spectrometer data themselves. Crucial to the analysis is radiometric calibration of the instrument. Good agreement is found between calculated and measured radiances for uniform surface targets (beaches), but simulations of atmospheric properties with LOWTRAN lead to unreasonably low values of atmospheric precipitable water. Absorptions from carbon dioxide are not detected in the AIS data, but are strongly present in the LOWTRAN model. The apparent low contrast of all atmospheric absorption bands leads to a study of contamination from overlapping spectral orders in the AIS data. The suspected contamination is shown unambiguously to be present beyond approximately 1500 nm and consists of an extra radiance term including atmospheric bands from the delta/2 wavelength interval. A rigorous removal of the unwanted spectral contamination does not seem possible for any data taken in the rock mode. Rough estimates for tree-mode observations might be pieced together is a suitable after-the-fact radiometric claibration of the instrument can be formulated.

Conel, J. E.↗