CFD design in aeronautics using a robust multilevel parallel evolutionary optimiser

L. González*, E. J. Whitney, K. Srinivas, J. Périaux

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


    This chapter explores the potential merit of an innovative parallel evolutionary algorithm (EA) coupled with current computational fluid dynamics (CFD) solvers. The chapter outlines the hierarchical asynchronous parallel evolution algorithm (HAPEA), and its important differences to a more conventional evolutionary method. A multi-objective test case involving the reconstruction of a set of two dimensional aerofoil geometries from scratch that are found by considering multiple prescribed pressure distributions at two different flow states is described. The chapter presents conclusions on the increased speed and robustness of the HAPEA algorithm operating in various parallel states, as compared to a more traditional method. Numerical experiments presented in this chapter provide the designer a gateway for practical applicability of parallel EAs in 3D industrial environments and MDO approaches using Navier-Stokes flow analysis solvers coupled with complex turbulence models. The increased speed and robustness obtained from the results underscore that the proper application of sound engineering judgment in conjunction with evolutionary techniques and parallel computing architectures can lead to optimal design solutions and significant computational savings when applied to real world problems.

    Original languageEnglish
    Title of host publicationParallel Computational Fluid Dynamics 2004
    Subtitle of host publicationMultidisciplinary Applications
    EditorsGabriel Winter, Jacques Periaux, Pat Fox, A. Ecer, N. Satofuka
    Place of PublicationAmsterdam
    Number of pages10
    ISBN (Electronic)9780080460963
    ISBN (Print)9780444520241
    Publication statusPublished - Jul 2005
    EventParallel CFD 2004 - Las Palmas de Gran Canaria, Spain
    Duration: 24 May 200427 May 2004


    ConferenceParallel CFD 2004
    CityLas Palmas de Gran Canaria


    • Cluster computing
    • Hierarchical parallel methods
    • Pareto fronts


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