GPU-accelerated optimization of CubeSat constellation design considering cloud cover uncertainty구름 차폐 불확실성을 고려한 GPU 기반의 큐브 위성 군집 가속 최적화 기법 연구

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Over the past decades, the miniaturization of commercial off-the-shelf electronics has led to the development of nanosatellites, especially on the basis of CubeSat standard. And recently, space community begins to realize that constellations of hundreds of CubeSats have potential to provide comparable or even greater capabilities than a single traditional large satellite in real space missions, but with lower cost and higher robustness. However, to design such a satellite constellation for reaping its benefits entails solving large-scale multi-objective optimization problems. One obvious problem is to decide the orbit elements of each CubeSat in the constellation with the purpose of optimizing some performance measures. In order to reduce the burden on the orbit design engineers and speed up the design process of future constellation missions, this study developed a tool named ACCELERATOR, which is essentially a multi-objective genetic algorithm with GPU-based modules, for obtaining optimal orbital configuration of the CubeSat constellations in reasonable time. Moreover, the primary limiting factor in the optical satellite constellation scheduling problem, namely the cloud cover uncertainty, was considered during the iterations of the genetic algorithm with Monte Carlo method. Therefore the resulting optimal constellations were believed to be robust enough to cope with the uncertainty while in the operations phase of the satellite constellation.
Advisors
Bang, Hyochoongresearcher방효충researcher
Description
한국과학기술원 :항공우주공학과,
Publisher
한국과학기술원
Issue Date
2020
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 항공우주공학과, 2020.2,[iv, 52 p. :]

Keywords

CubeSat constellation▼aRobust design▼aMulti-objective genetic algorithm▼aOrbit propagation▼aMonte Carlo method▼aGPU-accelerated computing; 큐브 위성 군집▼a강건 설계▼a다목적 유전자 알고리즘▼a궤도 전파▼a몬테카를로 방법▼aGPU 가속 컴퓨팅

URI
http://hdl.handle.net/10203/283976
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=910767&flag=dissertation
Appears in Collection
AE-Theses_Master(석사논문)
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