A second-generation URANS model (STRUCT-ε) applied to simplified freight trains

Resumen

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−ε), 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−ε 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−ε 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.