Network Methods for Multiple Gravity-Assist Mission Design- [electronic resource]
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Network Methods for Multiple Gravity-Assist Mission Design- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101306
- ISBN
- 9798379874100
- DDC
- 629.1
- 저자명
- Moore, James W.
- 서명/저자
- Network Methods for Multiple Gravity-Assist Mission Design - [electronic resource]
- 발행사항
- [S.l.]: : Purdue University., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(250 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
- 주기사항
- Advisor: Longuski, James.
- 학위논문주기
- Thesis (Ph.D.)--Purdue University, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약An innovative network model of the gravity assist problem enables the quick discovery and characterization of candidate trajectories from a small set of search criteria. The network elements encapsulate information about individual gravity assist encounters and connectivity. This organization of the astrodynamical information makes it possible to deploy well-established search methods to find sequences of flyby encounters with reduced human effort and in a fraction of the time previously required. The connectivity encoded in the model considers energy feasibility and scheduling constraints. Therefore, paths found using the network algorithms are feasible from both an energy and phasing perspective.Current initial-guess methods only identify a sequence of planet names that may form a tour. Broad searches over launch date and launch V∞ (sometimes requiring months of computation time) are currently required to identify realistic paths from each possible sequence. The network approach provides (in a shorter period of time) more detailed initial guesses that include the approximate V∞ and date of each encounter. These initial guesses can directly generate a set of patched-conic trajectories or initialize existing grid-search tools. The technique can accept fidelity improvements and may be extended for use on other mission types.A collection of potential gravity assist encounters serve as the network vertices. Keplerian models for connecting the gravity assists in energy and time translate into network edges. Network models of more sophisticated trajectory concepts such as resonant transfers and V∞-leveraging extend the approach to include more complex paths.General network traversal algorithms form the basis for gravity-assist trajectory searches. Problem-specific network filtering reduces network size and search times. A detailed discussion of algorithm complexity and problem size is also provided.The new search technique successfully rediscovers known trajectories from historical gravity assist missions. The network method also identifies preliminary gravity-assist trajectories to the Trans-Neptunian Objects Haumea and Makemake.
- 일반주제명
- Aerospace engineering.
- 기타저자
- Purdue University.
- 기본자료저록
- Dissertations Abstracts International. 85-01B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■0820 ▼a629.1
■1001 ▼aMoore, James W.
■24510▼aNetwork Methods for Multiple Gravity-Assist Mission Design▼h[electronic resource]
■260 ▼a[S.l.]:▼bPurdue University. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(250 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-01, Section: B.
■500 ▼aAdvisor: Longuski, James.
■5021 ▼aThesis (Ph.D.)--Purdue University, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aAn innovative network model of the gravity assist problem enables the quick discovery and characterization of candidate trajectories from a small set of search criteria. The network elements encapsulate information about individual gravity assist encounters and connectivity. This organization of the astrodynamical information makes it possible to deploy well-established search methods to find sequences of flyby encounters with reduced human effort and in a fraction of the time previously required. The connectivity encoded in the model considers energy feasibility and scheduling constraints. Therefore, paths found using the network algorithms are feasible from both an energy and phasing perspective.Current initial-guess methods only identify a sequence of planet names that may form a tour. Broad searches over launch date and launch V∞ (sometimes requiring months of computation time) are currently required to identify realistic paths from each possible sequence. The network approach provides (in a shorter period of time) more detailed initial guesses that include the approximate V∞ and date of each encounter. These initial guesses can directly generate a set of patched-conic trajectories or initialize existing grid-search tools. The technique can accept fidelity improvements and may be extended for use on other mission types.A collection of potential gravity assist encounters serve as the network vertices. Keplerian models for connecting the gravity assists in energy and time translate into network edges. Network models of more sophisticated trajectory concepts such as resonant transfers and V∞-leveraging extend the approach to include more complex paths.General network traversal algorithms form the basis for gravity-assist trajectory searches. Problem-specific network filtering reduces network size and search times. A detailed discussion of algorithm complexity and problem size is also provided.The new search technique successfully rediscovers known trajectories from historical gravity assist missions. The network method also identifies preliminary gravity-assist trajectories to the Trans-Neptunian Objects Haumea and Makemake.
■590 ▼aSchool code: 0183.
■650 4▼aAerospace engineering.
■690 ▼a0538
■71020▼aPurdue University.
■7730 ▼tDissertations Abstracts International▼g85-01B.
■773 ▼tDissertation Abstract International
■790 ▼a0183
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16933569▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
