Aerodynamic Optimization of High-Speed Trains Nose using a Genetic Algorithm and Artificial Neural Network

Muñoz Paniagua, Jorge; García García, Javier y Crespo Martínez, Antonio (2011). Aerodynamic Optimization of High-Speed Trains Nose using a Genetic Algorithm and Artificial Neural Network. En: "CFD & Optimization 2011. An ECCOMAS Thematic Conference", 23/05/2011 - 25/05/2011, Antalya, Turquía. ISBN 978-605-61427-4-1. pp. 1-19.

Descripción

Título: Aerodynamic Optimization of High-Speed Trains Nose using a Genetic Algorithm and Artificial Neural Network
Autor/es:
  • Muñoz Paniagua, Jorge
  • García García, Javier
  • Crespo Martínez, Antonio
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: CFD & Optimization 2011. An ECCOMAS Thematic Conference
Fechas del Evento: 23/05/2011 - 25/05/2011
Lugar del Evento: Antalya, Turquía
Título del Libro: Proceedings of CFD & Optimization 2011. An ECCOMAS Thematic Conference
Fecha: 2011
ISBN: 978-605-61427-4-1
Materias:
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 train aerodynamic characteristics in term of front wind action sensitivity is carried out in this paper. In particular, a genetic algorithm (GA) is used to perform a shape optimization study of a high-speed train nose. The nose is parametrically defined via Bézier Curves, including a wider range of geometries in the design space as possible optimal solutions. Using a GA, the main disadvantage to deal with is the large number of evaluations need before finding such optimal. Here it is proposed the use of metamodels to replace Navier-Stokes solver. Among all the posibilities, Rsponse Surface Models and Artificial Neural Networks (ANN) are considered. Best results of prediction and generalization are obtained with ANN and those are applied in GA code. The paper shows the feasibility of using GA in combination with ANN for this problem, and solutions achieved are included.

Más información

ID de Registro: 13104
Identificador DC: http://oa.upm.es/13104/
Identificador OAI: oai:oa.upm.es:13104
URL Oficial: http://eccomas.ae.metu.edu.tr/
Depositado por: Memoria Investigacion
Depositado el: 30 Nov 2012 11:04
Ultima Modificación: 21 Abr 2016 12:24
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