The KH-RT (Kelvin-Helholtz-Rayleigh-Taylor) breakup and dynamic structure subgrid-scale (DS SGS) models have been widely utilized to predict the spray evolutions in diesel engines, while the prediction accuracy is highly dependent on the tunable constants. Most of the tuning efforts for both of the above models are limited to the nonevaporating conditions or specified ambient conditions with the Reynolds averaged Navier-Stokes (RANS) approach, and then the tunable constants are supposed to be valid for other conditions. The purpose of this work is to evaluate whether this hypothesis is effective and how large a deviation it will make within the large eddy simulation (LES) framework for the evaporating diesel sprays. Firstly, the spray visualization experiments were conducted under various conditions. Then, the time-efficient Box-Behnken design (BBD) methodology was employed to comprehensively evaluate the sensitivity of prediction accuracy to the tunable constants. The results show that the KH breakup time constant B₁ is the most dominant constant for the liquid penetration, while three constants in the DS SGS model significantly affect the vapor penetration. The optimum constants were determined under a specified evaporating condition, which were different from those obtained with RANS. Finally, the performance of the above optimum constants on predicting the spray characteristics was evaluated under further operating conditions. It is found that the discrepancy between the experimental and predicted results varied from 2% to 20% for other conditions, indicating that the optimum constants obtained based on a specified condition cannot provide a satisfactory result, especially when the disturbance intensity and amount of air entrainment into the sprays are changed.

中文翻译：

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柴油机样工况下蒸发喷雾的KH-RT分解和动态结构SGS模型的评估

KH-RT（Kelvin-Helholtz-Rayleigh-Taylor）分解和动态结构子网格规模（DS SGS）模型已被广泛用于预测柴油机中的喷雾演变，而预测精度高度依赖于可调常数。上述两个模型的大多数调整工作都限于雷诺平均Navier-Stokes（RANS）方法的非蒸发条件或指定的环境条件，然后该可调常数被认为对其他条件有效。这项工作的目的是评估该假设是否有效，以及在蒸发柴油机喷雾的大型涡模拟（LES）框架内将产生多大的偏差。首先，在各种条件下进行了喷雾可视化实验。然后，高效的Box-Behnken设计（BBD）方法被用来全面评估预测精度对可调常数的敏感性。结果表明，KH分解时间常数B₁是液体渗透的最主要常数，而DS SGS模型中的三个常数会显着影响蒸气渗透。最佳常数是在指定的蒸发条件下确定的，该常数不同于使用RANS获得的常数。最后，在其他操作条件下评估了上述最佳常数在预测喷雾特性方面的性能。发现在其他条件下，实验结果与预测结果之间的差异在2％到20％之间变化，这表明基于特定条件获得的最佳常数不能提供令人满意的结果，尤其是当干扰强度和空气夹带量较大时变成喷雾剂。