Including analytically reduced chemistry (ARC) in CFD applications
Including analytically reduced chemistry (ARC) in CFD applications
复制标题
在 CFD 应用中包括分析还原化学 (ARC)
DOI：
10.1016/j.actaastro.2019.03.035
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发表时间：
2019
影响因子：
3.5
通讯作者：
Cuenot, Bénédicte
中科院分区：
文献类型：
--
作者：
Felden, Anne;Pepiot, Perrine;Esclapez, Lucas;Riber, Eleonore;Cuenot, Bénédicte
今天的反应数值模拟通常是基于由几个经验反应组成的合适的全球反应方案，或者基于详细的化学计算的预先列出的层流火焰解。虽然这两种方法都可以准确地预测层流火焰速度和燃烧气体组成等全球变量，但它们都有很大的局限性。特别是，两者都不能直接和充分地描述污染物化学的复杂性。然而，在减少下一代航空燃烧室有害排放的背景下，在燃烧模拟中包括这些所需的额外动力学细节变得至关重要。在可预见的未来，在精确的湍流燃烧模型中直接积分详细的化学成分是不可行的选择，因为过多的计算要求和数值刚性。在这种背景下，解析简化化学(ARC)代表了精度和效率之间的一种有吸引力的折衷，并已被用于相对复杂的直接数值模拟(DNS)和大涡模拟(LES)。ARCs是以知识为基础的紧凑机制，只保留直接从详细化学模型中提取的最相关的动力学信息，而不进行拟合，使用专门的还原技术(通过图形搜索识别重要物种、将具有相似特征的物种聚集在一起、识别短寿命物种等)。近年来，已经开发了几种多步骤高效的自动归约工具，使得能够以最少的输入和来自用户的知识轻松地生成弧线。本文的主要目的是对从甲烷到航空煤油替代品等各种燃料的弧线进行综述，这些弧线是最近通过这样一个多步骤自动还原工具：YARC得出的。给出了关于每种派生机制的适用性和有效性范围的信息，以及进一步的参考资料。每一种都是为了便于在CFD中使用而专门推导出来的；特别是，刚性被视为一个关键因素，最终运输的物种数量从未超过30种。在最后一节中，该方法的巨大潜力被展示在多相反应性LES应用中，其中燃料是真正的多组分运输燃料。为此，基于新型混合化学(HyChem)方法描述的Jet A的ARC与动态增厚火焰LES(DTFLES)模型耦合并直接集成到LES求解器AVBP中。使用拉格朗日喷雾描述。在温度和主要物种(CO2、H2O、CO、NO)质量分数方面与实验数据进行了比较，得到了非常令人满意的结果。
Reacting numerical simulations today are often based on either fitted global reaction schemes, comprised of a few empirical reactions, or pre-tabulated laminar flame solutions computed with detailed chemistry. Although both methods can accurately predict global quantities such as laminar flame speed and burnt gas composition, they have significant limitations. In particular, neither are able to directly and adequately describe the complexity of pollutant chemistry. In the context of reducing harmful emissions of the next generation of aeronautical combustors, however, including these needed additional kinetic details in combustion simulations is becoming essential. Direct integration of detailed chemistry in accurate turbulent combustion models is not a viable option in the foreseeable future, because of excessive computational demands and numerical stiffness. In this context, Analytically Reduced Chemistry (ARC) represents an attractive compromise between accuracy and efficiency, and is already employed in relatively complex Direct Numerical Simulations (DNS) and Large Eddy Simulations (LES). ARCs are knowledge-based compact mechanisms retaining only the most relevant kinetic information as extracted directly, and without fitting, from detailed chemical models using specialized reduction techniques (important species identification through graph search, lumping of species with similar features, short-living species identification, etc.). In recent years, several multi-step efficient and automated reduction tools have been developed, enabling the easy generation of ARCs with minimum input and knowledge from the user. The main objective of this paper is to present a review of ARCs for fuels ranging from methane to aviation kerosene surrogates, recently derived with such a multi-step automated reduction tool: YARC. Information about the applicability and range of validity of each derived mechanism are given, along with further references. Each one was specifically derived to be convenient to use in CFD; in particular, the stiffness was regarded as a key factor and the final number of transported species never exceeds thirty. In a final section, the great potential of the methodology is illustrated in a multi-phase, reactive LES application where the fuel is a real multi-component transportation fuel. To that end, an ARC based on a Jet A described by the novel Hybrid Chemistry (HyChem) approach is coupled with the Dynamically Thickened Flame LES (DTFLES) model and directly integrated into the LES solver AVBP. A Lagrangian spray description is used. Results are compared to experimental data in terms of temperature and major species (CO2, H2O, CO, NO) mass fractions, leading to very satisfying results.
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