Aerodynamic optimization of the ICE 2 high-speed train nose using a genetic algorithm and metamodels
Journal article, 2012

© Civil-Comp Press, 2012. 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.

Shape optimization

Metamodel

Bézier curves

High-speed train

Genetic algorithm

Author

J. Muñoz-Paniagua

Technical University of Madrid

J. García

Technical University of Madrid

A. Crespo

Technical University of Madrid

Sinisa Krajnovic

Chalmers, Applied Mechanics, Fluid Dynamics

Civil-Comp Proceedings

17593433 (ISSN)

Vol. 98

Subject Categories

Computational Mathematics

Probability Theory and Statistics

Control Engineering

DOI

10.4203/ccp.98.165

More information

Latest update

10/14/2019