Aerodynamic optimization of the ICE2 high-speed train nose using a genetic algorithm and metamodels

Muñoz Paniagua, Jorge; García García, Javier; Crespo Martínez, Antonio y Krajnovic, Sinisa (2012). Aerodynamic optimization of the ICE2 high-speed train nose using a genetic algorithm and metamodels. En: "First International Conference on Railway Technology: Research, Development and Maintenance", 18/04/2012 - 20/04/2012, Las Palmas de Gran Canaria, España.

Descripción

Título: Aerodynamic optimization of the ICE2 high-speed train nose using a genetic algorithm and metamodels
Autor/es:
  • Muñoz Paniagua, Jorge
  • García García, Javier
  • Crespo Martínez, Antonio
  • Krajnovic, Sinisa
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: First International Conference on Railway Technology: Research, Development and Maintenance
Fechas del Evento: 18/04/2012 - 20/04/2012
Lugar del Evento: Las Palmas de Gran Canaria, España
Título del Libro: Proceedings of the First International Conference on Railway Technology: Research, Development and Maintenance
Fecha: 2012
Materias:
Palabras Clave Informales: Shape optimization, high-speed train, genetic algorithm, metamodel, Bézier curves.
Escuela: E.T.S.I. Industriales (UPM)
Departamento: Ingeniería Energética y Fluidomecánica [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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 Be?zier 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 feasi- bility of using GA in combination with metamodels for real high-speed train geometry optimization.

Más información

ID de Registro: 19169
Identificador DC: http://oa.upm.es/19169/
Identificador OAI: oai:oa.upm.es:19169
Depositado por: Memoria Investigacion
Depositado el: 25 Ene 2014 11:37
Ultima Modificación: 21 Abr 2016 17:24
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