Date of Completion
Kinetic model reduction, Molecular diffusion model reduction, Stiff chemistry solver, Dynamic adaptive hybrid integration
Baki M. Cetegen
Field of Study
Doctor of Philosophy
Large-scale high-fidelity numerical simulation with detailed chemistry is an important approach to the study of combustion problems, which may involve turbulence and complex chemical reactions. However, detailed chemistry can involve a large number of species and reactions as well as severe chemical stiffness, resulting in high computational cost. This thesis presents a systematic study on reducing the computational cost of reacting flow simulations when detailed chemistry is involved. The effort includes reduction of chemical kinetic models and molecular diffusion, as well as development of advanced stiff chemistry solvers. First, a reduced kinetic model for ethylene/air with polycyclic aromatic hydrocarbons (PAHs) is developed for sooting flame simulations; reduced kinetic models are developed for n-dodecane as a jet fuel surrogate and for real jet fuels from detailed HyChem models by using a two-stage reduction method. Second, in addition to chemical kinetics, molecular diffusion is another important process in flames, and the mixture-averaged diffusion (MAD) model is frequently used in high-fidelity combustion simulations. However, the computational cost of the MAD model is typically a quadratic function of the number of species and can be high for large reaction models, necessitating the reduction of the MAD. Different approaches are therefore proposed to obtain small and accurate reduced models for the MAD. Third, in addition to model reduction, efficient stiff chemistry solvers can also substantially reduce the computation cost of combustion simulations. However, it is shown in the present study that the widely used operator splitting schemes can fail in error control for flames where significant radical sources are present in the transport term. Therefore, an advanced stiff chemistry solver, namely the dynamic adaptive hybrid integration (AHI), is developed as a substitute of the operator-splitting schemes to achieve higher accuracy and computational efficiency for such flame simulations.
Gao, Yang, "Model Reduction and Dynamic Adaptive Hybrid Integration for Efficient Combustion Simulations" (2017). Doctoral Dissertations. 1634.