DNS-driven analysis of the Flamelet/Progress Variable model assumptions on soot inception, growth, and oxidation in turbulent flames
DNS-driven analysis of the Flamelet/Progress Variable model assumptions on soot inception, growth, and oxidation in turbulent flames
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DOI：
10.1016/j.combustflame.2020.01.012
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发表时间：
2020-04
影响因子：
4.4
通讯作者：
Achim Wick;A. Attili;F. Bisetti;H. Pitsch
中科院分区：
文献类型：
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作者：
Achim Wick;A. Attili;F. Bisetti;H. Pitsch
用于湍流火焰中烟灰演化的大涡模拟的建模套件包括许多描述烟灰前体化学、粒子动力学、异质烟灰化学、湍流混合和燃烧的子模型。为了了解模型失败的原因并提高模型的整体性能，有必要识别并随后改进对整体误差具有先导效应的模型组件。在这项工作中，使用烟灰湍流射流扩散火焰的大规模直接数值模拟 (DNS) 数据，在先验和部分后验组合分析中隔离和量化了与基于小火焰的燃烧模型相关的烟灰预测误差。首先在 DNS 中分析进入烟灰源项计算并因此将燃烧模型耦合到烟灰模型的气相量。然后，使用混合平均传输模型和统一路易斯数对两个 DNS 案例的 Flamelet/Progress Variable 模型相对于这些量的性能进行先验分析。然后，使用直接从 DNS 和火焰表中获取的速率系数重新计算沿着从 DNS 提取的拉格朗日轨迹的烟尘演化。在部分事后分析的背景下，还将火焰引起的误差与化学烟灰模型引起的误差进行比较。火焰库可以很好地预测烟灰表面生长和氧化速率系数。模型预测的烟灰质量中相当大的误差源自进入基于 PAH 的烟灰增长率计算的表格数量。然而，如果该速率与多环芳烃 (PAH) 的质量分数适当缩放，这些误差可以减少到几个百分点。然而，这需要求解 PAH 质量分数的传输方程，并且对该方程中的源项进行建模具有挑战性。总体而言，最大的不确定性可归因于 PAH 形成的化学机制和 PAH 源项的模型。
Modeling suites for Large-Eddy Simulations of soot evolution in turbulent flames include a number of submodels describing the chemistry of soot precursors, particle dynamics, heterogeneous soot chemistry, turbulent mixing, and combustion. To understand the reasons for model failure and to enhance the overall model performance, it is necessary to identify and subsequently improve model components with a leading order effect on the overall error. In this work, errors in soot predictions associated with flamelet-based combustion models are isolated and quantified in a combined a-priori and partial a-posteriori analysis using large-scale Direct Numerical Simulation (DNS) data of a sooting turbulent jet diffusion flame. Gas-phase quantities entering the calculation of the soot source terms and hence coupling the combustion model to the soot model are analyzed in the DNS first. The performance of a Flamelet/Progress Variable model with respect to these quantities is then analyzed a-priori for two DNS cases employing a mixture-averaged transport model and unity Lewis numbers. Then, the soot evolution along Lagrangian trajectories extracted from the DNS is re-computed using rate coefficients directly taken from the DNS and from the flamelet table. In the context of this partial a-posteriori analysis, flamelet-induced errors are also compared to errors induced by the chemical soot model. The soot surface growth and oxidation rate coefficients are reasonably well predicted by the flamelet library. Considerably larger errors in the model-predicted soot mass originate from the tabulated quantities entering the calculation of PAH-based soot growth rates. However, these errors can be reduced to a few percent if the rate is appropriately scaled with the mass fraction of polycyclic aromatic hydrocarbons (PAH). However, this requires the solution of a transport equation for the PAH mass fraction, and modeling the source term in this equation is shown to be challenging. Overall, the largest uncertainties can be attributed to the chemical mechanism for PAH formation and the model for the PAH source term.