Shape Optimization of High-speed Trains for Improved Aerodynamic Performance

This paper presents a new procedure for optimization of aerodynamic properties of trains, where simple response surface (RS) models are used as a basis for optimization instead of a large number of evaluations of the Navier–Stokes solver. The suggested optimization strategy is demonstrated in 2 flow optimization cases: optimization of the train's front for the crosswind stability and optimization of vortex generators (VGs) for the purpose of drag reduction. Besides finding the global minimum for each aerodynamic objective, a strategy for finding a set of optimal solutions is demonstrated. This is based on the use of genetic algorithms on RS models. The resulting Pareto-optimal solutions are used to explore the extreme designs and find trade-offs between design objectives. For optimization of VGs, 3 different RS models are used: polynomial functions, radial basis neural networks (RBNN), and RBNN-enhanced polynomial RSs. The 3 approaches produce different results, and the combination of RBNN and polynomial functions in the last approach is found to be the best as it enables the construction of high-order polynomial functions, and the model's fit with the data is the best.

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  • English

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  • Accession Number: 01144443
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Nov 5 2009 1:33PM