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A Priori Assessment of Scalar Dissipation Rate Closure for Large Eddy Simulations of Turbulent Premixed Combustion Using a Detailed Chemistry Direct Numerical Simulation Database

机译:使用详细的化学直接数值模拟数据库对湍流预混燃烧的大涡模拟进行标量耗散率封闭的先验评估

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摘要

Algebraic and transport equation-based closures of Favre-filtered scalar dissipation rate (SDR) (N) over tilde (c) of the reaction progress variable, in the context of large eddy simulations, have been assessed using detailed chemistry direct numerical simulation (DNS) data of a stoichiometric H-2-air turbulent V flame. The Favre-filtered SDR (N) over tilde (c) and the unclosed terms of its transport equation have been extracted by explicitly filtering the DNS data for different choices of the reaction progress variable. An algebraic closure of SDR, which was proposed previously using simple chemistry DNS data, has been found to predict the Favre-filtered SDR (N) over tilde (c) satisfactorily for detailed chemistry DNS data for different choices of the reaction progress variable. Similarly, the models of the unclosed sub-grid convection, density variation, scalar-turbulence interaction, reaction rate gradient, molecular dissipation, and diffusivity gradient terms of the Favre-filtered SDR (N) over tilde (c) transport equation, which have previously been proposed based on simple chemistry DNS data, have been found to satisfactorily predict both the qualitative and quantitative behaviors of these unclosed terms for a range of filter widths Delta, for two different choices of reaction progress variable in the case of the detailed chemistry DNS dataset considered in this analysis.
机译:在大型涡流模拟的背景下,已经使用详细的化学直接数值模拟(DNS)评估了反应进度变量的波浪号(c)上基于Favre滤波的标量耗散率(SDR)(N)的基于代数和输运方程的闭包)化学计量的H-2-空气湍流V火焰数据。通过对反应进度变量的不同选择显式过滤DNS数据,提取了代号(c)上经过Favre滤波的SDR(N)及其传输方程的未封闭项。已经发现以前使用简单的化学DNS数据提出的SDR的代数闭合,可以令人满意地预测法夫尔滤波的SDR(N)超过波浪号(c)的情况,对于不同的反应进程变量选择,其详细的化学DNS数据令人满意。类似地,在代字(c)输运方程中,经Favre滤波的SDR(N)的未封闭子网格对流,密度变化,标量-湍流相互作用,反应速率梯度,分子耗散和扩散梯度项的模型具有以前是基于简单的化学DNS数据提出的,现已发现对于详细的化学DNS的两种不同的反应进程变量选择,对于一系列过滤宽度Delta而言,可以令人满意地预测这些未封闭项的定性和定量行为分析中考虑的数据集。

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