利用半精细 Jeta-1 化学预测 RQL 燃烧器中的烟尘

Etienne Lameloise, B. Cuenot, E. Riber, Aurélien Perrier, Gilles Cabot, Frédéric Grisch
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引用次数: 0

摘要

本研究提出了一种方法,在航空发动机大涡模拟中考虑到实际燃料的复杂性,以合理的计算成本将精确的动力学纳入烟尘建模。该方法的基础是构建一个分析减少的动力学机制,描述燃烧和气态烟尘前体的生长,并对选定的目标特性有足够的准确性。这一过程分为几个步骤,首先是选择详细的燃烧和烟尘前体生长动力学模型,然后是确定描述复杂实际混合燃料的燃料替代模型。最后,使用 ARCANE 代码对选定的动力学模型进行分析还原,同时控制所考虑的燃料代用物的火焰特性和烟尘预测误差。为了对典型烟尘火焰进行所有评估和还原测试,在 Cantera 中实施了烟尘离散截面模型。由此产生的代码(Cantera-soot)现在可用于快速计算任何燃料在层流火焰中的烟尘生成。通过将 LES 与拉格朗日烟尘跟踪模型和测量结果进行比较,最终在文献中的 Rich-Quench-Lean 燃烧器中验证了所获得的简化动力学方案的烟尘预测能力。结果表明,在使用更符合实际情况的简化动力学方案时,烟尘水平预测有了明显改善,同时还能对烟尘排放机制进行更详细的分析。这表明使用改进的还原动力学可以提高精度,并验证了建立此类方案的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Soot in an RQL Burner Using a Semi-Detailed Jeta-1 Chemistry
The present work proposes a methodology to include accurate kinetics for soot modeling taking into account real fuel complexity in Large Eddy Simulation of aeronautical engines at a reasonable computational cost. The methodology is based on the construction of an analytically reduced kinetic mechanism describing both combustion and gaseous soot precursors growth with sufficient accuracy on selected target properties. This is achieved in several steps, starting from the selection of the detailed kinetic model for combustion and soot precursors growth, followed by the determination of a fuel surrogate model describing the complex real fuel blend. Finally the selected kinetic model is analytically reduced with the code ARCANE while controlling the error on flame properties and soot prediction for the considered fuel surrogate. To perform all evaluation and reduction tests on canonical sooting flames, a Discrete Sectional Model for soot has been implemented in Cantera. The resulting code (Cantera-soot) is now available for the fast calculation of soot production in laminar flames for any fuel. The obtained reduced kinetic scheme is finally validated in a Rich-Quench-Lean burner of the literature in terms of soot prediction capabilities by comparison of LES coupled to the Lagrangian Soot Tracking model with measurements. Results show a significant improvement of the soot level prediction when using the reduced more realistic kinetics, which also allows a more detailed analysis of the soot emission mechanisms. This demonstrates the gain in accuracy obtained with improved reduced kinetics, and validates the methodology to build such schemes.
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