Abstract An effective way to increase the volume of cargo transported by freight trains is to increase their operational speed. The complex flow generated by trains moving through air has attracted much attention from researchers aiming to further improve their performance. Unfortunately, wind tunnel tests do not fully represent the complex geometries and realistic flow conditions observed at full scale (wind gusts, atmospheric turbulence and relative motion of the train with respect to the ground). The availability of a reliable numerical model is therefore critical to analyze the influence of these conditions. Steady RANS models, although still in use in both industry and academia, have shown to be unsuitable, while LES solutions still involve an excessive computational cost. In this work a second-generation URANS closure (STRUCT − e ), proposed by Xu (2020) has been applied to the simulation of freight trains. The approach aims at advancing the robustness and applicability of hybrid turbulence models by relying on the efficiency of an extensively validated anisotropic k − e method, while locally introducing the necessary resolution of complex unsteady flow structures. The proposed model does not leverage any grid dependent parameter, but triggers controlled resolution of turbulence only in regions of poor URANS applicability, while reverting to a URANS solution when rapidly varying structures are not identified. In the case considered in this work, the reduction of computational cost has been reached by increasing the cell size, while maintaining CFL numbers around 1.0. The work has demonstrated the LES-like capabilities of the STRUCT − e approach on much coarser grids, which allows a reduction of the total computing time by a factor of 5, as an essential enabler for effective aerodynamic design applications, especially aerodynamics optimization, crosswind stability and slipstream studies.

中文翻译：

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应用于简化货运列车的第二代 URANS 模型 (STRUCT-ε)

摘要 提高货运列车运行速度是增加货运列车运输量的有效途径。火车在空气中移动所产生的复杂流动引起了旨在进一步提高其性能的研究人员的广泛关注。不幸的是，风洞测试并不能完全代表在全尺度上观察到的复杂几何形状和实际流动条件（阵风、大气湍流和列车相对于地面的相对运动）。因此，可靠的数值模型的可用性对于分析这些条件的影响至关重要。稳定的 RANS 模型虽然仍在工业界和学术界使用，但已证明不合适，而 LES 解决方案仍然涉及过多的计算成本。在这项工作中，第二代 URANS 闭包 (STRUCT − e )，Xu (2020) 提出的方法已应用于货运列车的模拟。该方法旨在通过依赖广泛验证的各向异性 k - e 方法的效率来提高混合湍流模型的鲁棒性和适用性，同时局部引入复杂不稳定流动结构的必要分辨率。所提出的模型不利用任何网格相关参数，而是仅在 URANS 适用性差的区域触发受控的湍流分辨率，同时在未识别出快速变化的结构时恢复到 URANS 解决方案。在这项工作中考虑的情况下，通过增加单元大小来降低计算成本，同时将 CFL 数量保持在 1.0 左右。这项工作已经证明了 STRUCT - e 方法在更粗糙的网格上的类 LES 能力，