Turbulence modeling of internal combustion engines using RNG k-epsilon models
Turbulence modeling of internal combustion engines using RNG k-epsilon models
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DOI：
10.1080/00102209508907782
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发表时间：
1995-01-01
影响因子：
1.9
通讯作者：
Reitz, RD
中科院分区：
文献类型：
--
作者：
Han, Z;Reitz, RD
本研究对Yakhot和Orszag（1986）基于重整化群理论推导的RNG k-epsilon湍流模型进行了修正，并将其应用于变密度发动机流动。原始的基于rng的湍流输运近似是针对不可压缩流正式发展起来的。为了考虑流动可压缩性，通过各向同性快速畸变分析对RNG e方程进行了修正和封闭。对发动机压缩/膨胀弓进行了计算，并将计算结果与实际生产柴油机的实验结果进行了比较。将改进的RNG k-epsilon模型应用于柴油喷雾燃烧计算。结果表明，使用RNG模型进行喷雾燃烧建模是有必要的，因为湍流与平均应变时间尺度的比例是可观的，这是由于喷雾产生的平均Bow梯度，并且该模型引入了一个术语来解释这些影响。预测了受喷雾和挤压影响的大尺度流动结构，与内窥镜燃烧图像一致。讨论了流动可压缩性对非反应压缩膨胀流和反应膨胀流的影响。结果表明，湍流模型中流动可压缩性的处理对预测的燃烧参数，特别是烟尘排放有显著影响。
The RNG k-epsilon turbulence model derived by Yakhot and Orszag (1986) based on the Renormalization Group theory has been modified and applied to variable-density engine flows in the present study. The original RNG-based turbulence transport approximations were developed formally for an incompressible flow. In order to account for flow compressibility the RNG E-equation is modified and closed through an isotropic rapid distortion analysis. Computations were made of engine compressing/expanding Bows and the results were compared with available experimental observations in a production diesel engine geometry. The modified RNG k-epsilon model was also applied to diesel spray combustion computations. It is shown that the use of the RNG model is warranted for spray combustion modeling since the ratio of the turbulent to mean-strain time scales is appreciable due to spray-generated mean Bow gradients, and the model introduces a term to account for these effects. Large scale flow structures are predicted which are affected by the spray and the squish and are consistent with endoscope combustion images. The effects of flow compressibility on both non-reacting compressing/expanding flows and reacting Rows are discussed. It is concluded that predicted combustion parameters, particularly, soot emissions, are significantly influenced by the treatment of flow compressibility in the turbulence model.