Using Artificial Intelligence Methods to Predict Doses from Large Solar Particle Events in Space 2004-01-2324
When planning space missions, radiation effects due to large solar particle events (SPEs) can become a major concern since doses can become mission threatening to both the crew and the spacecraft electronic components. As mission duration increases, the possibility that a significant dose is delivered also increases, especially during the more active parts of the solar cycle. Therefore, a method of predicting when certain limiting doses will be reached following the onset of a large SPE needs to be available. Typical dose versus time profiles of a SPE can be represented by a Weibull functional form, which is comprised of three unknown parameters. Since these dose-time profiles are nonlinear functions, the use of artificial neural networks as the forecasting mechanism is ideal. In this work we report on the status of development of a “nowcast” methodology that utilizes a set of artificial neural networks that can forecast profiles of dose versus time since event onset using dose and dose rate information obtained early on as the event begins.
Citation: Nichols, T., Hines, J., Hoff, J., and Townsend, L., "Using Artificial Intelligence Methods to Predict Doses from Large Solar Particle Events in Space," SAE Technical Paper 2004-01-2324, 2004, https://doi.org/10.4271/2004-01-2324. Download Citation
Author(s):
Theodore F. Nichols, J. Wesley Hines, Jennifer L. Hoff, Lawrence W. Townsend
Affiliated:
Department of Nuclear Engineering, The University of Tennessee
Pages: 6
Event:
International Conference On Environmental Systems
ISSN:
0148-7191
e-ISSN:
2688-3627
Also in:
SAE 2004 Transactions Journal of Aerospace-V113-1
Related Topics:
Neural networks
Artificial intelligence (AI)
Sun and solar
Particulate matter (PM)
Radiation
Spacecraft
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