...
首页> 外文期刊>Combustion and Flame >From Large-Eddy Simulation to Direct Numerical Simulation of a lean premixed swirl flame: Filtered laminar flame-PDF modeling
【24h】

From Large-Eddy Simulation to Direct Numerical Simulation of a lean premixed swirl flame: Filtered laminar flame-PDF modeling

机译:从大涡模拟到稀薄预混旋流火焰的直接数值模拟:层流过滤-PDF建模

获取原文
获取原文并翻译 | 示例
           

摘要

Large-Eddy Simulations (LES) and Direct Numerical Simulation (DNS) are applied to the analysis of a swirl burner operated with a lean methane-air mixture and experimentally studied by Meier et al. [19]. LES is performed for various mesh refinements, to study unsteady and coherent large-scale behavior and to val?idate the simulation tool from measurements, while DNS enables to gain insight into the flame structure and dynamics. The DNS features a 2.6 billion cells unstructured-mesh and a resolution of less than 100 microns, which is sufficient to capture all the turbulent scales and the major species of the flame brush; the unresolved species are taken into account thanks to a tabulated chemistry approach. In a second part of the paper, the DNS is filtered at several filter widths to estimate the prediction capabilities of modeling based on premixed flamelet and presumed probability density functions. The similarities and differences between spatially-filtered laminar and turbulent flames are discussed and a new sub-grid scale closure for premixed turbulent combustion is proposed, which preserves spectral properties of sub-filter flame length scales. All these simulations are performed with a solver specifically tailored for large-scale com?putations on massively parallel machines.
机译:大涡模拟(LES)和直接数值模拟(DNS)用于分析以稀甲烷-空气混合物运行的旋流燃烧器,并由Meier等人进行了实验研究。 [19]。 LES用于各种网格细化,以研究不稳定和连贯的大规模行为并通过测量来验证模拟工具,而DNS可以深入了解火焰结构和动力学。 DNS具有26亿个非结构化网孔,分辨率小于100微米,足以捕获所有湍流尺度和火焰刷的主要种类;由于采用了列表化学方法,因此将未解决的物种也考虑在内。在本文的第二部分中,以几种过滤器宽度对DNS进行过滤,以基于预混合的小火焰和假定的概率密度函数估计建模的预测能力。讨论了空间过滤层流和湍流火焰之间的异同,并提出了一种用于预混湍流燃烧的新的子网格规模封闭,该子网格规模封闭保留了子过滤器火焰长度尺度的光谱特性。所有这些模拟都是使用专门为大型并行计算机上的大规模计算量身定制的求解器执行的。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号