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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Reliable Integration of AI Data Centers at Scale – Analysis, Modeling and Synthetic Data Generation

This report analyzes the power consumption of large dynamic digital loads using the open-source MIT supercloud and SURF datasets. With an emphasis on the MIT data, we calculate important power consumption characteristics to help system operators improve generation planning and resource allocation. We also introduce a rudimentary model for generating synthetic load profiles.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

Large-scale analysis and forecast experiments with wind data from the Seasat A scatterometer

A series of data assimilation experiments is performed to assess the impact of Seasat A satellite scatterometer (SASS) wind data on Goddard Laboratory for Atmospheric Sciences (GLAS) model forecasts. The SASS data are dealiased as part of an objective analysis system utilizing a three-pass procedure. The impact of the SASS data is evaluated with and without temperature soundings from the NOAA 4 Vertical Temperature Profile Radiometer (VTPR) instrument in order to study possible redundancy between surface wind data and upper air temperature data. In the northern hemisphere the SASS data are generally found to have a negligible effect on the forecasts. In the southern hemisphere the forecast impact from SASS data is somewhat larger and primarily beneficial in the absence of VTPR data. However, the inclusion of VTPR data effectively eliminates the positive impact over Australia and South America. This indicates that SASS data can be beneficial for numerical weather prediction in regions with large data gaps, but in the presence of satellite soundings the usefulness of SASS data is significantly reduced.

Baker, W. E.↗
NASA NTRS1985

On-three time scale analysis

In the present consideration of a three-timescale system, a block diagonalization employing three-stage linear transformations is used to decouple the original system into slow, fast, and faster subsystems. A numerical algorithm is given to solve the resulting Ricatti-type equations, and the technique is verified for the case of a ninth-order example. The technique yields substantial computational requirment reductions, since it involves subsystems that are of a lower order than the original system.

Naidu, D. S.↗
NASA NTRS2009

Contribution of Soil Moisture Information to Streamflow Prediction in the Snowmelt Season: A Continental-Scale Analysis

In areas dominated by winter snowcover, the prediction of streamflow during the snowmelt season may benefit from three pieces of information: (i) the accurate prediction of weather variability (precipitation, etc.) leading up to and during the snowmelt season, (ii) estimates of the amount of snow present during the winter season, and (iii) estimates of the amount of soil moisture underlying the snowpack during the winter season. The importance of accurate meteorological predictions and wintertime snow estimates is obvious. The contribution of soil moisture to streamflow prediction is more subtle yet potentially very important. If the soil is dry below the snowpack, a significant fraction of the snowmelt may be lost to streamflow and potential reservoir storage, since it may infiltrate the soil instead for later evaporation. Such evaporative losses are presumably smaller if the soil below the snowpack is wet. In this paper, we use a state-of-the-art land surface model to quantify the contribution of wintertime snow and soil moisture information -- both together and separately -- to skill in forecasting springtime streamflow. We find that soil moisture information indeed contributes significantly to streamflow prediction skill.

Reichle, Rolf↗
NASA NTRS2024

Multi-Scale Analysis of an Ilmenite Microcrater in Lunar Sample 71055

Micrometeorite impacts are a key space weathering process on the lunar surface, generating a variety of physical and chemical changes in the lunar regolith. Several experiments using nanosecond pulsed lasers to simulate micrometeorite impact have been published in the past two decades, providing a wealth of knowledge, including how minerals vary in their response to laser irradiation. Although these experiments have produced many features analogous to those observed in natural samples, none could be attributed to the passage of a shock wave, resulting instead from hot ablation. Recent femtosecond laser irradiation of olivine and enstatite have more accurately reproduced the shock textures observed in lunar samples, improving our ability to coordinate experimental and lunar sample data. Both pulsed laser experiments and studies of lunar impact craters have focused on silicates, as they comprise the bulk of the regolith. Ilmenite (FeTiO 3 ) is the most abundant opaque mineral in lunar rocks and an important sink of solar wind implanted helium. Ilmenite is known to vary in its response to radiation processing relative to the silicates, but its response to micrometeorite impact has not yet been studied in natural samples nor in the laboratory. In this investigation, we characterize an ilmenite microcrater using scanning electron microscopy (SEM) and transmission electron microscopy (TEM) to learn how ilmenite responds to impact and if there are implications for helium cycling in the regolith.

B A Cymes↗
DOE OSTI2017

Understanding Social Media’s Take on Climate Change through Large-Scale Analysis of Targeted Opinions and Emotions

Social media is a powerful data source for researchers interested in understanding population-level behavior, having been successfully leveraged in a number of different application areas including flu and illness prediction models, detecting civil unrest, and measuring public sentiment towards a given topic of interest within the public discourse. In this work, we present a study of a large collection of Twitter data centered on the social conversation around global cli- mate change during the UN Climate Change Conference, held in Paris, France during December 2015 (COP21). We first developed a mechanism for distinguishing between personal and non-personal accounts. We then analyzed demographics and emotion and opinion dynamics over time and location in order to understand how the different user types converse around meaningful topics on social media. This methodology offers an in-depth insight into the behavior and opinions around a topic where multiple distinct narratives are present, and lays the groundwork for future work in studying narratives in social media.

social media research↗