Comparison of RANS and detached eddy simulation results to wind-tunnel data for the surface pressures upon a class 43 high-speed train

Currently, there are three different methodologies for evaluating the aerodynamics of trains; full-scale measurements, physical modeling using wind-tunnel, and moving train rigs and numerical modeling using computational fluid dynamics (CFD). Moreover, different approaches and turbulence modeling are normally used within the CFD framework. The work in this paper investigates the consistency of two of these methodologies; the wind-tunnel and the CFD by comparing the measured surface pressure with the computed CFD values. The CFD is based on Reynolds-Averaged Navier-Stokes (RANS) turbulence models (five models were used; the Spalart-Allmaras (S-A), k-ε, k-ε re-normalization group (RNG), realizable k-ε, and shear stress transport (SST) k-ω) and two detached eddy simulation (DES) approaches; the standard DES and delayed detached eddy simulation (DDES). This work was carried out as part of a larger project to determine whether the current methods of CFD, model scale and full-scale testing provide consistent results and are able to achieve agreement with each other when used in the measurement of train aerodynamic phenomena. Similar to the wind-tunnel, the CFD approaches were applied to external aerodynamic flow around a 1/25th scale class 43 high-speed tunnel (HST) model. Comparison between the CFD results and wind-tunnel data were conducted using coefficients for surface pressure, measured at the wind-tunnel by pressure taps fitted over the surface of the train in loops. Four different meshes where tested with both the RANS SST k-ω and DDES approaches to form a mesh sensitivity study. The four meshes featured 18, 24, 34, and 52 × 10⁶ cells. A mesh of 34 × 10⁶ cells was found to provide the best balance between accuracy and computational cost. Comparison of the results showed that the DES based approaches; in particular, the DDES approach was best able to replicate the wind-tunnel results within the margin of uncertainty.