Large Eddy simulation of the turbulent spark ignition and of the flame propagation in spark ignition engines
Large Eddy simulation of the turbulent spark ignition and of the flame propagation in spark ignition engines
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火花点火发动机中湍流火花点火和火焰传播的大涡模拟
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发表时间：
2016
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通讯作者：
S. Mouriaux
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作者：
S. Mouriaux
在火花点火发动机（SIE）中使用稀当量比或高EGR率使得能够优化CO2和NOx排放;然而，太重要的稀释率导致循环间可变性增加。后者主要是由于点火阶段，这成为关键时，稀释率是重要的，需要高点火能量。目前IFPEN中使用的ECFM-LES模型是基于火焰面密度的概念，不足以描述这些临界条件下的点火。在这项研究中选择了TF-LES方法，主要是因为它直接解决了化学，因此可以通过局部温度升高来模拟点火。本工作定义并评估了SIE配置中TF-LES的模拟策略，该策略能够精确预测临界点火和湍流火焰传播。使用热能存款（艾德模型，Lacaze等人）对点火阶段进行建模。模拟再现了Cardin等人的点火实验，他们确定了贫预混甲烷/空气混合物在不同湍流特性下的最小点火能量（MIE）。该研究的主要目的是确定数值和物理模型参数，从而能够重现Cardin等人的实验。两种类型的动力学计划进行了评估：一个简化的动力学计划和分析动力学计划（ARC），可以预测的自燃延迟和层流火焰速度，同时保持负担得起的CPU时间。结果分析能够定义点火标准，并强调使用两种动力学方案的点火预测方面的差异。结果还表明，所选择的方法可以恢复正确的点火能量水平的层流和低Karlovitz数的情况下（Ka<10）。对于较高的Karlovitz数情况，发现艾德模型不足以预测点火，需要对能量存款进行更精细的描述。2012）被研究以描述在传播阶段期间火焰的不平衡行为。首先对层流球形火焰进行了研究，以评估模型的层流退化。然后，由于在发动机配置中的第一次测试已经揭示了模型的不兼容性，因此提出了修改。最后在ICAMDAC发动机配置中对修改后的动态模型进行了测试。的模拟结果进行了比较与罗伯特等人。获得与ECFM-LES模型，使用传输方程的火焰表面密度，可以描述的火焰的不平衡湍流的先前的结果。用动态模型得到的结果与Robert等人的结果非常一致，从而证明了动态模型预测发动机配置中的不平衡值的能力。此外，动态模型自适应湍流条件，因此不需要任何模型参数的调整，因为它是基于火焰表面密度传输方程的模型的情况下。
The use of lean equivalence ratios or high EGR rates in spark ignition engines (SIE) enables to optimize CO2 and NOx emissions; however too important dilution rates leads to increased cycle-to-cycle variability. These latter are mostly due to the ignition phase, which becomes critical when dilution rates are important and requires high ignition energy. The ECFM-LES model currently used in IFPEN, which is based on the flame surface density concept, is not sufficient to describe ignition in these critical conditions. The TF-LES approach was chosen in this study, principally because it directly resolved chemistry and can thus model ignition via a local raise of the temperature. The present work defines and evaluates a simulation strategy for TF-LES in SIE configurations, that enables a fine prediction of critical ignitions and of the turbulent flame propagation.In the first part, DNS of turbulent ignition were performed. The ignition phase was modeled using a thermal energy deposit (ED model, Lacaze et al.). Simulations reproduced the ignition experiments of Cardin et al. who determined the minimum ignition energy (MIE) of lean premixed methane/air mixtures, for different turbulence characteristics. The main purpose of the study was to determine the numerical and physical model parameters, which enable to reproduce Cardin et al. experiments. Two types of kinetic schemes were evaluated: a simplified kinetic scheme and an analytical kinetic scheme (ARC), that can predict both the auto-ignition delays and the laminar flame speed, while keeping affordable CPU times. Results analysis enabled to define ignition criteria and to highlight the differences in terms of ignition prediction using the two kinetic schemes. Results also demonstrated that the chosen approach could recover correct levels of ignition energy for laminar and low Karlovitz number cases (Ka<10). For higher Karlovitz number cases, the ED model was found to be insufficient to predict the ignition and a finer description of the energy deposit is required.In the second part, a dynamic wrinkling model (Wang et al., 2012) was studied to describe the out-of-equilibrium behavior of the flame during the propagation phase. Studies on laminar spherical flames were first performed, to assess the laminar degeneration of the model. Then, as first tests in an engine configuration have revealed incompatibilities of the model, modifications were proposed. The modified dynamic model was finally tested in the ICAMDAC engine configuration. Results of the simulations were compared against previous results of Robert et al. obtained with the ECFM-LES model using a transport equation for the flame surface density that can describe the out-of-equilibrium wrinkling of the flame. Results obtained with the dynamic model are in very good agreement with the ones of Robert et al., thus demonstrating the ability of the dynamic model to predict out-of-equilibrium values in the engine configuration. Besides, the dynamic model self-adapts to the turbulence conditions, hence does not require any model parameter adjustment, as is it the case for models based on the flame surface density transport equation.