A Model-Based Closure Approach for Turbulent Combustion using the One-Dimensional Turbulence Model

Reynolds-Averaged Navier-Stokes (RANS);Non-premixed Flames;Turbulent Combustion;One-Dimensional Turbulence Model

Ranganath, Bhargav Bindiganavile ; Dr. Tarek Echekki, Committee Chair,Dr. William Roberts, Committee Member,Dr. Kevin Lyons, Committee Member,Dr. Zhilin Li, Committee Member,Ranganath, Bhargav Bindiganavile ; Dr. Tarek Echekki ; Committee Chair ; Dr. William Roberts ; Committee Member ; Dr. Kevin Lyons ; Committee Member ; Dr. Zhilin Li ; Committee Member

University:North Carolina State University

关键词: Reynolds-Averaged Navier-Stokes (RANS);    Non-premixed Flames;    Turbulent Combustion;    One-Dimensional Turbulence Model;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/5637/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

A new model-based closure approach for turbulent combustion using the One-Dimensional turbulence model (ODT) is developed and validated in context to a turbulent jet diffusion flame. The interaction of turbulence and chemistry provides interesting finite rate chemistry effects including the phenomena of extinction and re-ignition. The ODT model resolves both spatially and temporally all the scales in a turbulent reaction flow problem, thus, combining the accuracy of a DNS like solver with efficiency by reduction in the number of dimensions. The closure approach is based on identifying the mechanisms responsible for the above mentioned effects and parameterizing the ODT results with a minimum set of scalars transported in the coarse grained solvers like the Reynolds-Averaged Navier-Stokes (RANS) or Large Eddy Simulation (LES). Thus, the closure from ODT is based on a "one-way" coupling between the coarse grained solvers and ODT. Two approaches for closure are developed in the present work with respect to a RANS solver; however, they can be easily extended to LES. The first approach relies on ODT to provide the history effects associated with the geometry, which represent the interactions of turbulence and chemistry, by tabulating scalar statistics (first and second moments) on two parameters measuring, the extent of mixing, the radial mean mixture fraction, and the extent of entrainment, the centerline mean mixture fraction. However, based on the above parameterization, the approach is limited to jet diffusion flame geometry. Furthermore, the closure requires a one to one correspondence between the flames simulated in the coarse grained solver and ODT. As a second approach, the results from ODT are parameterized based on general representative scalars; mixture fraction, which specifies the mixedness of the mixture and temperature, which specifies the reactedness of the mixture. The history effects associated with the flow geometry are provided by the RANS solver in the form of probability distribution functions (PDFs).Two classes of turbulent jet diffusion flames; hydrogen⁄air (Flame H3) and piloted methane/air (Sandia flames D and F), are considered for validation of the above ODT-based closure approaches. The piloted methane air flames, owing to higher turbulence, exhibit severe extinction in the near field followed by re-ignition around the flame height. Good comparisons of the conditional statistics for temperature and reactive scalars with the experiments are obtained for both the flames. Good predictions of entrainment as well as mixing for both the flames, as seen in the comparisons of Favre averaged axial and radial profiles, are obtained. Furthermore, the correct trends of extinction and re-ignition are predicted successfully for the piloted methane/air flames. Thus, the results show the capability of ODT to address the closure needs for a turbulent combustion problem both at molecular length scales (conditional profiles) and integral length scales (Favre averaged axial and radial profile) successfully. Refinements in terms correct representation of the PDFs for the second closure approach can be recognized, whereas, a robust "two-way" coupling of RANS and ODT may yield good results.