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Data-Driven Dimensionality Reduction for Simulating Combustion Systems
Data-Driven Dimensionality Reduction for Simulating Combustion Systems
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151351
- ISBN
- 9798382842851
- DDC
- 621
- 서명/저자
- Data-Driven Dimensionality Reduction for Simulating Combustion Systems
- 발행사항
- [Sl] : Cornell University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 117 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Advisor: Pepiot, Perrine.
- 학위논문주기
- Thesis (Ph.D.)--Cornell University, 2024.
- 초록/해제
- 요약There is a critical need for the development of cleaner and more efficient combustion technologies to limit their detrimental effects on the climate and the environment and reduce the world's dependence on fossil fuels. Predictive tools, such as computational fluid dynamics simulations (CFD), are essential for the design of future reacting combustion technologies. The ability to accurately predict the chemical kinetics in combustion technologies is critical to accurately describing the system's behavior. However, using a detailed representation of the chemistry is often computationally cost-prohibitive due to the high dimensionality and nonlinearity of the chemical kinetics. In this dissertation, I present frameworks to reduce the dimensionality of large chemical mechanisms with complex dynamics to enable simulations of next-generation combustion technologies at a reduced computational cost.First, a novel methodology for the reduction of large detailed plasma-assisted combustion mechanisms to smaller skeletal ones is presented and applied to an ethylene-air mechanism. The methodology extends a commonly used graph-based reduction technique, the Directed Relation Graph with Error Propagation (DRGEP), to consider the energy branching characteristics of plasma discharges during the reduction. The performance of the novel framework, named P-DRGEP, is assessed for the simulation of ethylene-air ignition by nanosecond repetitive pulsed discharges at conditions relevant to supersonic combustion and flame holding in scramjet cavities. The generated skeletal mechanism is capable of simulating ignition with a computational speed-up of 84% and with ignition delay time errors below 10%.Next, a novel empirical manifold framework is presented with the goal of enabling a posteriori CFD simulations with a very low-dimensional representation of the thermochemical state. The presented framework uses an autoencoder (AE) to learn the low-dimensional manifold and the Neural Ordinary Differential Equations (NODE) training framework to learn a source term approximation of the low-dimensional variables. The framework is validated by simulating ignition in an a posteriori framework with only 6 latent variables relative to the skeletal mechanism of a sustainable aviation fuel containing 152 species. The AE-NODE framework is able to capture the time to ignition and the equilibrium temperature and composition and exhibits a 96% reduction in memory relative to the thermochemical state vector and approximately 96% reduction in the time associated with the direct integration.
- 일반주제명
- Mechanical engineering
- 일반주제명
- Thermodynamics
- 일반주제명
- Energy
- 일반주제명
- Fluid mechanics
- 키워드
- Machine learning
- 기타저자
- Cornell University Mechanical Engineering
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211151351
■006m o d
■007cr#unu||||||||
■020 ▼a9798382842851
■035 ▼a(MiAaPQ)AAI31243251
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621
■1001 ▼aKincaid, Nicholas.▼0(orcid)0000-0001-5563-4172
■24510▼aData-Driven Dimensionality Reduction for Simulating Combustion Systems
■260 ▼a[Sl]▼bCornell University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a117 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Pepiot, Perrine.
■5021 ▼aThesis (Ph.D.)--Cornell University, 2024.
■520 ▼aThere is a critical need for the development of cleaner and more efficient combustion technologies to limit their detrimental effects on the climate and the environment and reduce the world's dependence on fossil fuels. Predictive tools, such as computational fluid dynamics simulations (CFD), are essential for the design of future reacting combustion technologies. The ability to accurately predict the chemical kinetics in combustion technologies is critical to accurately describing the system's behavior. However, using a detailed representation of the chemistry is often computationally cost-prohibitive due to the high dimensionality and nonlinearity of the chemical kinetics. In this dissertation, I present frameworks to reduce the dimensionality of large chemical mechanisms with complex dynamics to enable simulations of next-generation combustion technologies at a reduced computational cost.First, a novel methodology for the reduction of large detailed plasma-assisted combustion mechanisms to smaller skeletal ones is presented and applied to an ethylene-air mechanism. The methodology extends a commonly used graph-based reduction technique, the Directed Relation Graph with Error Propagation (DRGEP), to consider the energy branching characteristics of plasma discharges during the reduction. The performance of the novel framework, named P-DRGEP, is assessed for the simulation of ethylene-air ignition by nanosecond repetitive pulsed discharges at conditions relevant to supersonic combustion and flame holding in scramjet cavities. The generated skeletal mechanism is capable of simulating ignition with a computational speed-up of 84% and with ignition delay time errors below 10%.Next, a novel empirical manifold framework is presented with the goal of enabling a posteriori CFD simulations with a very low-dimensional representation of the thermochemical state. The presented framework uses an autoencoder (AE) to learn the low-dimensional manifold and the Neural Ordinary Differential Equations (NODE) training framework to learn a source term approximation of the low-dimensional variables. The framework is validated by simulating ignition in an a posteriori framework with only 6 latent variables relative to the skeletal mechanism of a sustainable aviation fuel containing 152 species. The AE-NODE framework is able to capture the time to ignition and the equilibrium temperature and composition and exhibits a 96% reduction in memory relative to the thermochemical state vector and approximately 96% reduction in the time associated with the direct integration.
■590 ▼aSchool code: 0058.
■650 4▼aMechanical engineering
■650 4▼aThermodynamics
■650 4▼aEnergy
■650 4▼aFluid mechanics
■653 ▼aChemical kinetics
■653 ▼aCombustion technologies
■653 ▼aMachine learning
■653 ▼aReduction techniques
■653 ▼aComputational fluid dynamics
■690 ▼a0548
■690 ▼a0204
■690 ▼a0348
■690 ▼a0791
■71020▼aCornell University▼bMechanical Engineering.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0058
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161402▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
