基于表格化学高效工作流的柴油机燃烧预测CFD建模

F. Tap, C. Meijer, D. Goryntsev, A. Starikov, Mijo Tvrdojevic, P. Priesching
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引用次数: 3

摘要

基于表格化学的三维CFD燃烧模型的使用正变得越来越流行。特别是运行时的优势是有吸引力的,因为表格化学方法允许在CFD模拟中包含最先进的化学反应方案。在这项工作中,使用AVL FIRE™中的Tabkin FGM燃烧模型来评估轻型柴油机测量数据的大型数据库的预测性。AVL TABKIN™软件用于创建TABKIN FGM模型的化学查找表。TABKIN软件已经扩展了动态煤烟模型,其中煤烟质量分数的计算是在化学制表过程中完成的,以及使用第二个进程变量的NO模型。从最近的验证研究中,得出了一个最佳实践和几乎自动化的工作流程,可以在最小输入的基础上为柴油机应用创建查找表。本研究对这种自动化建模工作流程进行了评估。研究了5个发动机负载点的大范围参数变化,包括有和没有EGR,总共186例。大量的CFD模拟以自动化的方式运行,CFD子模型的参数和所有数值设置保持不变。结果提出了燃烧和排放(NO和烟灰)。燃烧参数和NO排放与实验数据库的相关性非常好,R2值在0.95以上。烟尘预测在大多数情况下给出了数量级的一致性;然而,这一趋势并不总是得到尊重,这限制了所有病例的总体相关性,正如其他作者所报道的那样。烟尘形成和氧化过程的建模还需要进一步的基础研究来改进模型。在CPU时间方面,目前的研究是在一个现成的HPC集群上执行的,每个案例使用8个CPU内核,每个案例需要大约3小时的时间,例如,在一个标准的HPC集群上,这样大的一组计算可以在一夜之间模拟。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictive CFD Modeling of Diesel Engine Combustion Using an Efficient Workflow Based on Tabulated Chemistry
The use of 3D CFD combustion models based on tabulated chemistry is becoming increasingly popular. Especially the runtime benefit is attractive, as the tabulated chemistry method allows to include state-of-the-art chemical reaction schemes in CFD simulations. In this work, the Tabkin FGM combustion model in AVL FIRE™ is used to assess the predictivity on a large database of a light-duty Diesel engine measurements. The AVL TABKIN™ software is used to create the chemistry look-up tables for the Tabkin FGM model. The TABKIN software has been extended with the kinetic soot model, where the soot mass fraction calculation is done during the chemistry tabulation process, as well as an NO model using a second progress variable. From recent validation studies, a best-practice and nearly automated workflow has been derived to create the look-up tables for Diesel engine applications based on minimal input. This automated modeling workflow is assessed in the present study. A wide range of parameter variations are investigated for 5 engine load points, with and without EGR, in total 186 cases. This large number of CFD simulations is run in an automated way and the parameters of the CFD sub-models are kept equal as well as all numerical settings. Results are presented for combustion and emissions (NO and soot). Combustion parameters and NO emissions correlate very well to the experimental database with R2 values above 0.95. Soot predictions give order-of-magnitude agreement for most of the cases; the trend however is not always respected, which limits the overall correlation for all cases together, as reported by other authors. Further fundamental research on modeling soot formation and oxidation process remains required to improve the models. In terms of CPU time, the present study was executed on an off-the-shelf HPC cluster, using 8 CPU cores per case and requiring around 3 hrs of wall-time per case, e.g. such a large set of calculations can be simulated overnight on a standard HPC cluster.
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