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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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368 records · Page 21
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Lessons Learned on Mega-Constellation Deployments and Impact to Space Domain Awareness

The breakneck expansion of multi-payload launches in low Earth orbit (LEO) has seen significant escalation over the last three years in the Space Domain Awareness (SDA) enterprise. The uptick in satellite deployment frequency, gradation of mega-constellation deployments utilizing electric propulsion, and surge in metric observation density from the Space Surveillance Network (SSN) have driven major system enhancements to ensure spaceflight safety. Beginning with the launch phase, new tactics, software, and procedures have been developed over the last few years to optimize the tasking of the SSN and ensure custody of all recently launched satellites that are added to the space catalog. Ensuring appropriate tasking levels were pivotal during the satellite separation phase, which necessitated updates to the mission systems for improved delineation between clustered satellites in a short window of time. The improved incorporation of utilizing satellite owner/operator-provided ephemeris coupled with critical code improvements have revolutionized how we maintain custody of Earth orbiting objects. The exponential population growth of Low-Earth orbiting satellites have also increased the quantity of Conjunction Data Messages sent out to partners such as the Trajectory Operations Group (TOPO) in Johnson Space Center. We will discuss the need for continued collaboration between the National Aeronautics and Space Administration (NASA) and other mega-constellation satellite operators to safeguard human spaceflight operations. Lastly, we will discuss how mega-constellations are driving best practices for safe operations across the space community.

Christian Ramos↗
NASA NTRS2023

A Single-Point Modeling Approach for the Intercomparison and Evaluation of Ozone Dry Deposition Across Chemical Transport Models (Activity 2 of AQMEII4)

A primary sink of air pollutants and their precursors is dry deposition. Dry deposition estimates differ across chemical transport models, yet an understanding of the model spread is incomplete. Here, we introduce Activity 2 of the Air Quality Model Evaluation International Initiative Phase 4 (AQMEII4). We examine 18 dry deposition schemes from regional and global chemical transport models as well as standalone models used for impact assessments or process understanding. We configure the schemes as single-point models at eight Northern Hemisphere locations with observed ozone fluxes. Single-point models are driven by a common set of site-specific meteorological and environmental conditions. Five of eight sites have at least 3 years and up to 12 years of ozone fluxes. The interquartile range across models in multiyear mean ozone deposition velocities ranges from a factor of 1.2 to 1.9 annually across sites and tends to be highest during winter compared with summer. No model is within 50 % of observed multiyear averages across all sites and seasons, but some models perform well for some sites and seasons. For the first time, we demonstrate how contributions from depositional pathways vary across models. Models can disagree with respect to relative contributions from the pathways, even when they predict similar deposition velocities, or agree with respect to the relative contributions but predict different deposition velocities. Both stomatal and nonstomatal uptake contribute to the large model spread across sites. Our findings are the beginning of results from AQMEII4 Activity 2, which brings scientists who model air quality and dry deposition together with scientists who measure ozone fluxes to evaluate and improve dry deposition schemes in the chemical transport models used for research, planning, and regulatory purposes.

Dry deposition↗
NASA NTRS2023

Public Health Data Applications Using the CDC Tracking Network: Augmenting Environmental Hazard Information with Lower-latency NASA Data

Exposure to environmental hazards is an important determinant of health, and the frequency and severity of exposures is expected to be impacted by climate change. Through a partnership with the U.S. National Aeronautics and Space Administration, the U.S. Centers for Disease Control and Prevention’s National Environmental Public Health Tracking Network is integrating timely observations and model data of priority environmental hazards into its publicly accessible Data Explorer (https://ephtracking.cdc.gov/DataExplorer/). Newly integrated datasets over the contiguous U.S. (CONUS) include: daily 5-day forecasts of air quality based on the Goddard Earth Observing System Composition Forecast (GEOS-CF), daily historical (1980-present) concentrations of speciated PM2.5 based on the Modern Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), and Moderate Resolution Imaging Spectroradiometer (MODIS) daily near real-time maps of flooding (MCDWD). Data integrated into the CDC Tracking Network are broadly intended to improve community health through action by informing both research and early warning activities, including (1) describing temporal and spatial trends in disease and potential environmental exposures, (2) identifying populations most affected, (3) generating hypotheses about associations between health and environmental exposures, and (4) developing, guiding, and assessing environmental public health policies and interventions aimed at reducing or eliminating health outcomes associated with environmental factors.

air quality↗
NASA NTRS2025

Direct Impact Split-Hopkinson Bar Experiment for Compression Testing at Strain Rate of 50,000 S-1

A direct impact Hopkinson compression bar experiment is used for compression testing at strain rates in the order of 50,000 s-1. A short specimen mounted on the cross-section surface of a transmitter bar is impacted directly by a projectile (the incident bar in the classical split Hopkinson bar is eliminated).The quantities measured in the test are the projectile impact velocity, the force in the transmitter bar and the strain on the surface of the specimen, which is measured using the digital image correlation method with high-speed cameras. The deformation measurements show a nonuniform deformation in the specimen. To study the strain rate sensitivity properties of the specimen’s material, the experiment is simulated using an assumed material model properties which is validated by comparing the calculated and measured quantities. For aluminum 2024-T351 the results show a small strain rate sensitivity (increase of stress with strain rate) at strain rates above 10,000 s-1.

Nathan Spulak↗