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An aerodynamic optimization of the ICE 2 high-speed train nose in term of front wind action sensitivity is carried out in this paper. The nose is parametrically defined by Bézier Curves, and a three-dimensional representation of the nose is obtained using thirty one design variables. This implies a more complete parametrization, allowing the representation of a real model. In order to perform this study a genetic algorithm (GA) is used. Using a GA involves a large number of evaluations before finding such optimal. Hence it is proposed the use of metamodels or surrogate models to replace Navier-Stokes solver and speed up the optimization process. Adaptive sampling is considered to optimize surrogate model fitting and minimize computational cost when dealing with a very large number of design parameters. The paper introduces the feasibility of using GA in combination with metamodels for real high-speed train geometry optimization.

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This paper proposes a simple methodology to analyse numerically the effects of air damping on the oscillatory motion of heated micro-cantilevers. The proposed methodology is fully solved by the fluid solver, because the solid domain is embedded into a subroutine executed at the end of each time step. The methodology is first validated against numerical and experimental data yielding acceptable results. Next, it is applied to predict the quality factor of a micro-cantilever heated by an electric resistance inserted inside it. The transferred heat flow and the evolution in time of temperature are analysed and the quality factors of both heated and isothermal micro-cantilevers are compared.