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Muñoz Paniagua, Jorge ORCID: https://orcid.org/0000-0002-4450-2438, García García, Javier ORCID: https://orcid.org/0000-0002-2986-7228, Crespo Martínez, Antonio and 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.
Título: | Aerodynamic optimization of the ICE2 high-speed train nose using a genetic algorithm and metamodels |
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Autor/es: |
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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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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.
ID de Registro: | 19169 |
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Identificador DC: | https://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 |