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LARGE EDDY SIMULATION OF A PREMIXED BUNSEN FLAME USING A MODIFIED THICKENED-FLAME MODEL AT TWO REYNOLDS NUMBER

机译:使用两个雷诺数的改进型增厚火焰模型对预混邦森火焰进行大涡模拟

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摘要

A modified thickened flame (TF) model based on large eddy simulation (LES) methodology is used to investigate premixed combustion, and the model predictions are evaluated by comparing with the piloted premixed stoichiometric methane-air flame data for Reynolds numbers Re = 24,000 (flame F3) and Re = 52,000 (flame F1). The basic idea of the TF approach is that the flame front is artificially thickened to resolve on the computational LES grid while keeping the laminar flame speed (s_L~0) constant. The artificially thickening of the flame front is obtained by enhancing the molecular diffusion and decreasing the pre-exponential factor of the Arrhenius law. Because the flame front is artificially thickened, the response of the thickened flame to turbulence is affected and taken care of by incorporating an efficiency function (E) in the governing equations. The efficiency function (E) in the modified TF model is proposed based on the direct numerical simulations (DNS) data set of flame-vortex interactions. The predicted simulation results are compared with the experimental data and with computations reported using a Reynolds averaged Navier-Stokes (RANS)-based probability distribution function (PDF) modeling approach and RANS-based G-equation approach. It is shown that the results with the modified TF model are generally in good agreement with the data, with the TF predictions consistently comparable to the PDF model predictions and superior to the results with the G-equation approach.
机译:使用基于大涡模拟(LES)方法的改进的加厚火焰(TF)模型研究预混燃烧,并通过与雷诺数Re = 24,000(火焰)的先导预混化学计量甲烷空气火焰数据进行比较来评估模型预测F3)和Re = 52,000(火焰F1)。 TF方法的基本思想是,在保持层流火焰速度(s_L〜0)恒定的同时,人为地增加火焰前锋以在计算的LES网格上分解。通过增强分子扩散并降低阿伦尼乌斯定律的指数前因子,可以人工增加火焰锋。由于火焰前缘是人为地增厚的,因此通过在控制方程式中加入效率函数(E)可以影响并注意增厚的火焰对湍流的响应。基于火焰-涡流相互作用的直接数值模拟(DNS)数据集,提出了改进的TF模型中的效率函数(E)。将预测的仿真结果与实验数据进行比较,并与使用基于雷诺平均Navier-Stokes(RANS)的概率分布函数(PDF)建模方法和基于RANS的G方程法报告的计算结果进行比较。结果表明,改进的TF模型的结果通常与数据非常吻合,TF预测始终与PDF模型的预测可比,并且优于G方程法的结果。

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