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

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.

[1]  C. Baker,et al.  Large-eddy simulation of the flow around a freight wagon subjected to a crosswind , 2010 .

[2]  Chao Li,et al.  Wind Tunnel Investigation of a Double Stacked Wagon in Free-Stream , 2015 .

[3]  Hisashi Ninokata,et al.  Improved Turbulence Modeling for Performance Evaluation of Novel Fuel Designs , 2007 .

[4]  Sinisa Krajnovic,et al.  Exploring Flow Structures Around a Simplified ICE2 Train Subjected to A 30° Side Wind Using LES , 2009 .

[5]  B. Launder,et al.  Application of the energy-dissipation model of turbulence to the calculation of flow near a spinning disc , 1974 .

[6]  Hassan Hemida,et al.  Comparison of RANS and Detached Eddy Simulation Results to Wind-Tunnel Data for the Surface Pressures Upon a Class 43 High-Speed Train , 2015 .

[7]  Tomas W. Muld.  Slipstream and Flow Structures in the Near Wake of High-Speed Trains , 2012 .

[8]  F.-R. Grosche,et al.  Research at DLR Göttingen on bluff body aerodynamics, drag reduction by wake ventilation and active flow control , 2001 .

[9]  W. Jones,et al.  The prediction of laminarization with a two-equation model of turbulence , 1972 .

[10]  P. A. Mackrodt,et al.  Aerodynamics of Road- and Rail Vehicles , 1985 .

[11]  Sinisa Krajnovic.  On Unsteady Simulations in Train Aerodynamics , 2014 .

[12]  Daniele Rocchi,et al.  High-Speed Train Crosswind Analysis: CFD Study and Validation with Wind-Tunnel Tests , 2010 .

[13]  David Matthew Burton,et al.  Assessment of various turbulence models (ELES, SAS, URANS and RANS) for predicting the aerodynamics of freight train container wagons , 2017 .

[14]  Sinisa Krajnovic.  Numerical simulation of the flow around an ICE2 train under the influence of a wind gust , 2008 .

[15]  Wolfgang Rodi,et al.  Large eddy simulation of the flow over periodic hills , 2000 .

[16]  Giancarlo Lenci,et al.  A methodology based on local resolution of turbulent structures for effective modeling of unsteady flows , 2016 .

[17]  Ben Diedrichs.  Aerodynamic Crosswind Stability of a Regional Train Model , 2010 .

[18]  Javier García,et al.  Evaluation of RANS, SAS and IDDES models for the simulation of the flow around a high-speed train subjected to crosswind , 2017 .

[19]  Emilio Baglietto,et al.  STRUCT: A Second-Generation URANS Approach for Effective Design of Advanced Systems , 2017 .

[20]  Chao Li,et al.  Flow topology of a container train wagon subjected to varying local loading configurations , 2017 .

[21]  Michel Visonneau,et al.  Cross wind effects on a simplified car model by a DES approach , 2013 .

[22]  David Soper,et al.  Detached-eddy simulation of the slipstream of an operational freight train , 2014 .