SBIR/STTR Research Award

Improved Forecasts of Solar Particle Events using Eruptive Event Generators based on Gibson-Low and Titov-Demoulin Magnetic Configurations

Award identifier 3ad80602f1ae828a2a040fa672d2968b9c26b68486729a3390d4cfe76bf555a5Awarding agency National Aeronautics and Space Administration

Award amount

$124,934

Program and phase

STTR · Phase I

Award year

2015

Explore This Research Award in CaptureIQ

Research the technology, evaluate the recipient and agency, identify related opportunities, and explore potential follow-on work with CaptureIQ.

Award details

Awarding agency
National Aeronautics and Space Administration
Branch
Not reported
Recipient company
MICHIGAN AEROSPACE CORP
Proposal award date
2015-06-17
Contract end date
2016-06-17
UEI
K15KKHPFZ5T9
Solicitation number
Not reported
Topic code
T6.02

Research abstract

Radiation hazards constitute a serious risk to human and robotic space operations beyond Low-Earth orbit. Primary contributors to space radiation include Solar Particle Events (SPEs) associated with Coronal Mass Ejections (CMEs). Because the mechanisms that produce coronal mass ejections (CME) are exceedingly complex, no reliable deterministic methods for predicting eruptions are yet available, and the most successful approaches are phenomenological and probabilistic in nature. But predicting the eruption is only part of the problem. In order to forecast the time, location, flux, and the energy spectrum of a Solar Particle Event (in order to better model its effect on specific hardware and instruments, for example) we must also understand the intervening plasma environment, including the steady-state magnetic configuration, as well as the dynamic, eruption driven configurations that provide for the time dependent transport and diffusive acceleration of solar energetic particles. Progress has been made in the understanding of the solar atmosphere due to the increased availability of observational data and the development of analytical and numerical models of the solar wind. One aspect of this development is the construction of complex three-dimensional (3D) models, which can be validated with observations and further refined to improve the comparison. In order to improve SPE forecasts Michigan Aerospace Corporation (MAC) and the University of Michigan's department of Atmospheric, Oceanic, and Space Science (AOSS) intend to cooperate on this STTR project, which seeks, over Phase 1 and Phase 2, to 1) Use data-driven statistical models to forecast the likelihood of solar eruptions; 2) Couple these predictions with eruption generation models in the context of the Space Weather Modeling Framework (SWMF) to forecast the likely time, location, flux, and energy spectrum of Solar Energetic Particles.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

71 SBIR/STTR records share this UEI (1999–2023). Reported award amounts total $22,024,167. Reported amounts are not federal obligations.

Phases: Phase I, Phase II

Agencies: Department of Defense, National Aeronautics and Space Administration, Department of Energy, Department of Commerce, Environmental Protection Agency, National Science Foundation

Agency intelligence

National Aeronautics and Space Administration

Back to SBIR/STTR Research Awards

Don't take our word for it. Ask AI about RFPCapture.

See what leading AI assistants have to say about RFPCapture and CaptureIQ.

Gemini opens without a prefilled question. The question is copied to your clipboard when the button works. You can also copy it here:

What is RFPCapture.com and its CaptureIQ platform from Smart Logic AI? How does it help federal contractors discover opportunities, evaluate bid/no-bid decisions, research government contracts, and prepare proposals? How does it compare with other federal capture platforms? Use independent sources where available.