The Simultaneous Optimization of the Nose and Tail Geometry of a High-Speed Train for Drag and Crosswind Stability

Abstract

The South African Government requires that a high-speed rail network is available to commuters by the year 2050. One of the key role players in achieving the aforementioned is Transnet Engineering (TE), which at present has neither experience in the field of high speed rail, nor a prototype train that would perform well aerodynamically travelling at speeds of approximately 350km/h. As such the final deliverable of this study was a nose and tail geometry for a train travelling at 350km/h, which had been optimized for total drag under windless conditions and drag as well as crosswind stability when subjected to crosswinds. In this study attention was thus given to the flow surrounding high-speed trains so as to address the knowledge deficit within TE, as well as the challenges typically faced in high speed rail applications with the purpose of ensuring the validity of the optimization goals set out by TE. This study furthermore identified appropriate geometric variables from literature which are important for an efficient aerodynamic nose and tail shape, i.e., the nose length (L), nose-tip height (Z0) and the inflection point height (H). Another objective was to identify an appropriate turbulence model that was able to accurately analyze the flow surrounding the train body, with attention also being given to the choice of a suitable optimization algorithm. The validation case completed on the Ahmed’s body not only revealed an appropriate grid resolution to use for the optimization study on the train geometry, but further showed the aptness of the linear pressure-strain Reynold’s stress turbulence model and the SHERPA optimization algorithm. For the optimization of the nose and tail geometry under windless conditions, the design space was sampled by making use of a 5-level full factorial, while the radial basis function with thin spline method was used to connect the data points obtained from simulation. The surrogate model was found to be highly predictive with the largest discrepancy between its results and simulation values being -1.6%. The SHERPA algorithm was used for the optimization study itself and identified an optimal geometry with L=7.7m, H=2.73m and Z0=1.364m. The associated minimized total drag force is 13.6kN which is 30.4% less than the maximum drag force that can be actualized by the geometric parameters within their respective ranges. In the case of a train subjected to crosswinds, the Latin Hypercube sampling method along with 27 designs points was made use of in order to sample the design space. The surrogate model was obtained by making use of the same fitting method as above and was once again found to be predictive with the greatest discrepancy reported being 1.6%. The same search algorithm as above was also used in order to identify the Pareto front, with the recommended geometry displaying the following features; L=7.7m, H=2.71m and Z0=1.364m. This yielded a minimum drag force of 14.6kN and rolling moment of 93.5kN, which corresponds to a reduction of 23.5% and 12.6%, respectively, from the maximum values that can be actualized by the geometric parameters within their respective ranges. Finally, it was found that either of the aforementioned optimized geometries is able to perform well when exposed to the other’s load case.