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

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

Description

Title: Aerodynamic Optimization of High-Speed Trains Nose using a Genetic Algorithm and Artificial Neural Network
Author/s:
  • Muñoz Paniagua, Jorge
  • García García, Javier
  • Crespo Martínez, Antonio
Item Type: Presentation at Congress or Conference (Article)
Event Title: CFD & Optimization 2011. An ECCOMAS Thematic Conference
Event Dates: 23/05/2011 - 25/05/2011
Event Location: Antalya, Turquía
Title of Book: Proceedings of CFD & Optimization 2011. An ECCOMAS Thematic Conference
Date: 2011
ISBN: 978-605-61427-4-1
Subjects:
Faculty: E.T.S.I. Industriales (UPM)
Department: Ingeniería Energética y Fluidomecánica [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 13104
DC Identifier: http://oa.upm.es/13104/
OAI Identifier: oai:oa.upm.es:13104
Official URL: http://eccomas.ae.metu.edu.tr/
Deposited by: Memoria Investigacion
Deposited on: 30 Nov 2012 11:04
Last Modified: 21 Apr 2016 12:24
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