Longitudinal vortices are a common flow phenomenon in the flow around aircraft. They emerge wherever sharp edges are encountered and affect the stability of the boundary layers with which they interact. Hence, for an optimal aircraft design, it is necessary to accurately predict not only the formation of these vortices, but also their downstream evolution. This paper presents a numerical simulation approach for the formation and downstream transport of longitudinal vortices. The approach consists in a hybrid Reynolds-averaged Navier–Stokes/large eddy simulation (RANS/LES) method with synthetic turbulence forcing for which different RANS/LES interface locations are investigated. To provide a quantitative analysis, an isolated sharp-edged delta wing configuration is adopted that allows to get rid of unwanted uncertainties. The presented RANS/LES approach is validated against particle image velocimetry data and compared with a wall-modeled LES and to a differential Reynolds stress model simulation. The results show that the RANS simulation is able to accurately predict the formation of the bulk vortical structure. However, its downstream transport can be accurately predicted only by a scale-resolving simulation. The hybrid RANS/LES approach allows to achieve this while saving computational resources by modeling the roll-up of the vortex. Nevertheless, its predictive accuracy highly depends on the appropriate placement of the RANS/LES interface.

纵向涡流是飞机周围流动中常见的流动现象。它们出现在遇到尖锐边缘的地方，并影响与它们相互作用的边界层的稳定性。因此，对于最佳飞机设计，不仅需要准确预测这些涡流的形成，还需要准确预测它们的下游演变。本文提出了纵向涡流的形成和下游传输的数值模拟方法。该方法包括混合雷诺平均纳维-斯托克斯/大涡模拟 (RANS/LES) 方法和合成湍流强迫，研究不同的 RANS/LES 界面位置。为了提供定量分析，采用了孤立的锐边三角翼配置，可以消除不需要的不确定性。提出的 RANS/LES 方法针对粒子图像测速数据进行了验证，并与壁建模 LES 和差分雷诺应力模型模拟进行了比较。结果表明，RANS模拟能够准确预测体涡结构的形成。然而，它的下游传输只能通过尺度解析模拟来准确预测。混合 RANS/LES 方法允许实现这一点，同时通过对涡旋的卷起建模来节省计算资源。然而，其预测准确性在很大程度上取决于 RANS/LES 接口的适当位置。结果表明，RANS模拟能够准确预测体涡结构的形成。然而，它的下游传输只能通过尺度解析模拟来准确预测。混合 RANS/LES 方法允许实现这一点，同时通过对涡旋的卷起建模来节省计算资源。然而，其预测准确性在很大程度上取决于 RANS/LES 接口的适当位置。结果表明，RANS模拟能够准确预测体涡结构的形成。然而，它的下游传输只能通过尺度解析模拟来准确预测。混合 RANS/LES 方法允许实现这一点，同时通过对涡旋的卷起建模来节省计算资源。然而，其预测准确性在很大程度上取决于 RANS/LES 接口的适当位置。