Date of Award
Fall 2012
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Electrical & Computer Engineering
Program/Concentration
Electrical Engineering
Committee Director
R. P. Joshi
Committee Member
L. L. Vahala
Committee Member
M. Thitsa
Call Number for Print
Special Collections LD4331.E55 N578 2012
Abstract
Developing successful and optimal solutions for mitigating the hazards of radiation in deep space is critical for the success of deep-space long duration explorations, including mission to the Moon, Mars and beyond. A recent report (Tripathi et al., 2008) had explored the feasibility of using electrostatic shielding in conjunction with state-of-the-art materials shielding technologies.
In this thesis research, we study the active electrostatic shielding strategy and examine a hybrid configuration that utilizes both electrostatic and magnetostatic fields. The main advantages of this system are shown to be: (i) much lower magnetic fields that could be below the thresholds set for health and safety for long-term exposures, (ii) a much better shielding and repulsion of incident ions from both solar particle events (SPEs) and galactic cosmic rays (GCRs), and (iii) reductions in the power requirement for re-charging the electrostatic sub-system.
Furthermore, our results show that SPEs radiation can be almost eliminated by these electrostatic configurations. It is also shown that the power needed to replenish the electrostatic charges due to particle strikes from the GCR and SPE radiation is minimal and insignificant.
Rights
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DOI
10.25777/7sjb-dx67
Recommended Citation
Nguyen, Trac K..
"Configuration Studies of Electrostatic and Magnetostatic Active Space Radiation Shielding"
(2012). Master of Science (MS), Thesis, Electrical & Computer Engineering, Old Dominion University, DOI: 10.25777/7sjb-dx67
https://digitalcommons.odu.edu/ece_etds/456
Included in
Electrical and Electronics Commons, Electromagnetics and Photonics Commons, Space Vehicles Commons, Systems Engineering and Multidisciplinary Design Optimization Commons