자료유형  

 학위논문

Control Number  

0017161402

International Standard Book Number  

9798382842851

Dewey Decimal Classification Number  

621

Main Entry-Personal Name  

Kincaid, Nicholas.

Publication, Distribution, etc. (Imprint  

[S.l.] : Cornell University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

117 p.

General Note  

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

General Note  

Advisor: Pepiot, Perrine.

Dissertation Note  

Thesis (Ph.D.)--Cornell University, 2024.

Summary, Etc.  

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.

Subject Added Entry-Topical Term  

Mechanical engineering.

Subject Added Entry-Topical Term  

Thermodynamics.

Subject Added Entry-Topical Term  

Energy.

Subject Added Entry-Topical Term  

Fluid mechanics.

Index Term-Uncontrolled  

Chemical kinetics

Index Term-Uncontrolled  

Combustion technologies

Index Term-Uncontrolled  

Machine learning

Index Term-Uncontrolled  

Reduction techniques

Index Term-Uncontrolled  

Computational fluid dynamics

Added Entry-Corporate Name  

Cornell University Mechanical Engineering

Host Item Entry  

Dissertations Abstracts International. 85-12B.

Electronic Location and Access  

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Control Number  

joongbu:655628