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Convex Shape Optimization of Aerospace Vehicles
Convex Shape Optimization of Aerospace Vehicles
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151403
- ISBN
- 9798382229591
- DDC
- 629.1
- 서명/저자
- Convex Shape Optimization of Aerospace Vehicles
- 발행사항
- [Sl] : Stanford University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 217 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
- 주기사항
- Advisor: Alonso, Juan;Kochenderfer, Mykel.
- 학위논문주기
- Thesis (Ph.D.)--Stanford University, 2024.
- 초록/해제
- 요약The field of aerodynamic shape optimization (ASO) has seen dramatic advances in the last several decades through major developments in computational power and numerical methods. Today, the process of gradient-based optimization of complex, three-dimensional aerospace vehicles, using high-fidelity physical models, is mature.This dissertation explores a new horizon of shape optimization for aerospace vehicles, employing techniques from the field of convex optimization. Unlike traditional nonconvex, gradient-based optimization techniques, which iteratively refine an initial design towards a new local optimum, convex optimization seeks a globally optimal solution.These techniques address two important open problems in ASO. The first is the need to be able to efficiently and robustly explore high dimensional, constrained, multi-objective design spaces in order to assess performance tradeoffs and limits during the initial development of new vehicles. The second problem is linking these exploratory, or conceptual design, studies to the high-fidelity, gradient-based optimization frameworks for final refinement. These frameworks require a parameterization and an initial design point. Good parameterizations and initial design points can significantly decrease the overhead of these computationally expensive processes. Convex optimization offers an exciting avenue to addressing both of these problems.In the first part of this dissertation, I develop a framework called Convexity Assisted Shape Optimization, or CASO. CASO provides a set of rules and requirements for approaching aerospace vehicle shape optimization problems through the lens of convex optimization. I also propose two new types of smooth and accurate convex surrogates that will be useful in reducing this framework to practice.In the second part of this dissertation, I propose several new classes of orthogonal basis functions for parameterizing shapes in aerodynamic shape optimization problems. In some cases, these bases simplify the derivation and expression of useful aerodynamic objective functions. In other cases they offer a natural path to representing important aspects of aerodynamic shapes. I also show how these bases can be used to develop convex formulations of several common aerodynamic performance indicators, spanning multiple flow regimes.In the third part, I extend these methods to nonconvex objective functions that have convex trust regions that may be represented accurately and smoothly using convex surrogates. I also consider the cases of nonconvex objective functions that benefit from a transformation and relaxation strategy or a bi-level optimization scheme to preserve the ability to identify a global optimum.Finally, I show how these methods can be applied to actual design problems and link these conceptual results to a high-fidelity design framework. These design problems span multiple flight regimes, performance indicators, and shape representations, in order to provide a broad sampling of the types of problems that can be approached using CASO.
- 일반주제명
- Aerospace engineering
- 일반주제명
- Mechanical engineering
- 기타저자
- Stanford University.
- 기본자료저록
- Dissertations Abstracts International. 85-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798382229591
■035 ▼a(MiAaPQ)AAI31255674
■035 ▼a(MiAaPQ)vg280nq1560
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a629.1
■1001 ▼aBerkenstock, Dan.
■24510▼aConvex Shape Optimization of Aerospace Vehicles
■260 ▼a[Sl]▼bStanford University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a217 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-11, Section: B.
■500 ▼aAdvisor: Alonso, Juan;Kochenderfer, Mykel.
■5021 ▼aThesis (Ph.D.)--Stanford University, 2024.
■520 ▼aThe field of aerodynamic shape optimization (ASO) has seen dramatic advances in the last several decades through major developments in computational power and numerical methods. Today, the process of gradient-based optimization of complex, three-dimensional aerospace vehicles, using high-fidelity physical models, is mature.This dissertation explores a new horizon of shape optimization for aerospace vehicles, employing techniques from the field of convex optimization. Unlike traditional nonconvex, gradient-based optimization techniques, which iteratively refine an initial design towards a new local optimum, convex optimization seeks a globally optimal solution.These techniques address two important open problems in ASO. The first is the need to be able to efficiently and robustly explore high dimensional, constrained, multi-objective design spaces in order to assess performance tradeoffs and limits during the initial development of new vehicles. The second problem is linking these exploratory, or conceptual design, studies to the high-fidelity, gradient-based optimization frameworks for final refinement. These frameworks require a parameterization and an initial design point. Good parameterizations and initial design points can significantly decrease the overhead of these computationally expensive processes. Convex optimization offers an exciting avenue to addressing both of these problems.In the first part of this dissertation, I develop a framework called Convexity Assisted Shape Optimization, or CASO. CASO provides a set of rules and requirements for approaching aerospace vehicle shape optimization problems through the lens of convex optimization. I also propose two new types of smooth and accurate convex surrogates that will be useful in reducing this framework to practice.In the second part of this dissertation, I propose several new classes of orthogonal basis functions for parameterizing shapes in aerodynamic shape optimization problems. In some cases, these bases simplify the derivation and expression of useful aerodynamic objective functions. In other cases they offer a natural path to representing important aspects of aerodynamic shapes. I also show how these bases can be used to develop convex formulations of several common aerodynamic performance indicators, spanning multiple flow regimes.In the third part, I extend these methods to nonconvex objective functions that have convex trust regions that may be represented accurately and smoothly using convex surrogates. I also consider the cases of nonconvex objective functions that benefit from a transformation and relaxation strategy or a bi-level optimization scheme to preserve the ability to identify a global optimum.Finally, I show how these methods can be applied to actual design problems and link these conceptual results to a high-fidelity design framework. These design problems span multiple flight regimes, performance indicators, and shape representations, in order to provide a broad sampling of the types of problems that can be approached using CASO.
■590 ▼aSchool code: 0212.
■650 4▼aAerospace engineering
■650 4▼aMechanical engineering
■653 ▼aConvex optimization
■653 ▼aAerodynamic shape optimization
■653 ▼aAerospace vehicles
■690 ▼a0548
■690 ▼a0538
■71020▼aStanford University.
■7730 ▼tDissertations Abstracts International▼g85-11B.
■790 ▼a0212
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161487▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
