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dc.contributor.advisorJu, Yiguang
dc.contributor.authorZhang, Tianhan
dc.contributor.otherMechanical and Aerospace Engineering Department
dc.description.abstractOne of the most fundamental features of combustion is its multi-scale nature. This dissertation focuses on multi-scale modeling of combustion waves, from detonation to deflagration, with detailed combustion chemistry and transport at near-limit conditions. In addition, deep learning-based methods are proposed to tackle two challenging topics in combustion simulation: global model reduction and stiff ODE integration.The first two chapters introduce the background and classical theories on thermodynamics, chemical kinetics, and modeling. The third and fourth chapters study detonation formation mechanisms with detailed kinetic mechanisms. It reveals the importance of detailed kinetic mechanism in detonation initiation. The concept of the concentration gradient and the concentration gradient-induced detonation is first introduced. Then, the effects of low-temperature chemistry on detonation initiation are studied. The coupling between concentration gradient and temperature gradient and the interaction between low temperature autoignition and turbulence transport is demonstrated. The fifth to seventh chapters aim to investigate cool flame dynamics. Due to the inherent short ignition delay time of low-temperature chemistry, the ignition Damkӧhler number is proved to be a critical dimension to understand cool flame propagation. A systematic analysis is performed on cool flame propagation under different ignition Damkӧhler numbers. The ignition-assisted cool flame and double flame model is further applied in spherical flame modeling under shock-tube conditions, which provides important insights for future engine-relevant studies. The eighth and ninth chapters explore two new deep learning-based methods: DeepMR and DeepCombustion. The DeepMR is a global model reduction method including a deep neural network implementation and an iterative sampling strategy. The DeepCombustion-v1.0 is a deep neural network-based chemistry ODE integrator, with two main advantages: arbitrarily large time steps and robustness under various conditions. These two methods can significantly reduce chemistry mechanism size and simulation efficiency.
dc.publisherPrinceton, NJ : Princeton University
dc.relation.isformatofThe Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog: <a href=></a>
dc.subject.classificationMechanical engineering
dc.subject.classificationAerospace engineering
dc.subject.classificationComputational physics
dc.titleMulti-scale modeling of near-limit combustion waves with detailed kinetics and transport
dc.typeAcademic dissertations (Ph.D.)
pu.departmentMechanical and Aerospace Engineering
Appears in Collections:Mechanical and Aerospace Engineering

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