A high-speed train uses two symmetrically corresponding shaped power cars at both ends. Consequently, the same nose shape plays a role as a leading part and a role as a trailing part in one train at the same time. Thus the existing model of the optimi...
A high-speed train uses two symmetrically corresponding shaped power cars at both ends. Consequently, the same nose shape plays a role as a leading part and a role as a trailing part in one train at the same time. Thus the existing model of the optimized first car nose shape which does not consider the entire train is not sound in terms of the aerodynamic drag. Also, while accurate simulation of the wake area behind the train is very significant for the design optimization of the three-dimensional shape, accuracy of previous studies has been limited by their train shapes and boundary conditions. Therefore, it is necessary to consider the entire train including the first car nose and the last car nose and especially accurate simulation of the wake area for the optimization of the shape design of a three-dimensional symmetric train in order to reduce the total aerodynamic drag.
In this dissertation, two nose shape optimizations of the front-rear symmetric train are performed with no constraint for the reduction of the total aerodynamic drag and with the constraint of the optimized cross-sectional area distribution for the reduction of the total aerodynamic drag and the micro-pressure wave respectively. The three-dimensional train nose shape is constructed through Vehicle Modeling Function and a viscous compressible flow solver is adopted with unstructured meshes to predict the aerodynamic drag. The two optimizations are respectively performed under consideration of two cases – for the total aerodynamic drag of the entire train and for the aerodynamic drag of the first car only by the previous method for the reduction of design time. Also, an Artificial Neural Network is constructed with the experimental points extracted by Maxi-min Latin Hypercube Sampling method.
In the unconstrained optimization, it was found that the total aerodynamic drag of the entire train with the optimized shape for the entire train was reduced by 5.8% when compared to the unconstrained base model, whereas that with the optimized shape for only the first car is changed little. On the other hand, in the constrained optimization, the total aerodynamic drag of the entire train with the optimized shape for the entire train was effectively reduced by 15.3 % when compared to that of the constrained base model while that with the optimized shape for only the first car is increased by 9.7% on the contrary.
The low-risen and long vertical nose shape of the unconstrained optimum weakens the whirled flow around the nose tip. On the other hand, the low-risen and wide horizontal nose shape of the constrained optimum weakens the up-wash flow and vortices behind the blunt nose. Both shape characteristics reduce the overall aerodynamic drag of each base model.
Therefore, the three dimensional modeling is very necessary for design optimization of the actual front-rear symmetric train in that the wake area behind the train must be simulated as accurately as possible. In doing so, Vehicle Modeling Function is a valuable tool in successful three-dimensional shape optimization since it has no modeling constraint to functionalize three-dimensional shape thus efficiently enables the various models of the three-dimensional train shape. Also, it is required to design symmetrically identical both noses in order to reduce the total aerodynamic drag.