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International Conference on Evolutionary Multi-Criterion Optimization

EMO 2015: Evolutionary Multi-Criterion Optimization pp 313–327Cite as

Aircraft Air Inlet Design Optimization via Surrogate-Assisted Evolutionary Computation

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9019))

Abstract

In aviation, the performance impact of auxiliary air inlets used for system ventilation is significant. The flow phenomena and consequently the numerical model, is highly non-linear, leading to a compromise between pressure recovery and drag for a given mass flow condition. This work follows a step-by-step approach which highlights the important issues related to solving such complex optimization problem, using surrogate methods coupled to evolutionary algorithms. Its conclusions can be used as a guideline to similar industrial applications.

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Author information

Authors and Affiliations

  1. Embraer, São José dos Campos, São Paulo, SP, Brazil

    Andre Lombardi

  2. Instituto Tecnológico de Aeronáutica, São José dos Campos, São Paulo, SP, Brazil

    Denise Ferrari

  3. Universidade de São Paulo, São Paulo, SP, Brazil

    Luis Santos

Authors
  1. Andre Lombardi
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  2. Denise Ferrari
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  3. Luis Santos
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Corresponding author

Correspondence to Andre Lombardi .

Editor information

Editors and Affiliations

  1. Institute for Polymers and Composites/I3N University of Minho Guimarães Portugal, Guimaraes, Portugal

    António Gaspar-Cunha

  2. Dept. of Electrical and Computer Engg, University of Coimbra, Coimbra, Portugal

    Carlos Henggeler Antunes

  3. CINVESTAV-IPN Depto. de Computacíon, Col. San Pedro Zacatenco, México DF, Mexico

    Carlos Coello Coello

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© 2015 Springer International Publishing Switzerland

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Lombardi, A., Ferrari, D., Santos, L. (2015). Aircraft Air Inlet Design Optimization via Surrogate-Assisted Evolutionary Computation. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C. (eds) Evolutionary Multi-Criterion Optimization. EMO 2015. Lecture Notes in Computer Science(), vol 9019. Springer, Cham. https://doi.org/10.1007/978-3-319-15892-1_21

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  • DOI: https://doi.org/10.1007/978-3-319-15892-1_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15891-4

  • Online ISBN: 978-3-319-15892-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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