• Login
    JavaScript is disabled for your browser. Some features of this site may not work without it.
    Sensitivity computation and shape optimisation in aerodynamics using the adjoint methodology and Automatic Differentiation. 
    •   QMRO Home
    • Queen Mary University of London Theses
    • Theses
    • Sensitivity computation and shape optimisation in aerodynamics using the adjoint methodology and Automatic Differentiation.
    •   QMRO Home
    • Queen Mary University of London Theses
    • Theses
    • Sensitivity computation and shape optimisation in aerodynamics using the adjoint methodology and Automatic Differentiation.
    ‌
    ‌

    Browse

    All of QMROCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects
    ‌
    ‌

    Administrators only

    Login
    ‌
    ‌

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Sensitivity computation and shape optimisation in aerodynamics using the adjoint methodology and Automatic Differentiation.

    View/Open
    Christakopoulos_F_PHD_final.pdf (24.83Mb)
    Publisher
    Queen Mary University of London
    Metadata
    Show full item record
    Abstract
    Adjoint based optimisation has until now demonstrated a great promise for optimisation in aerodynamics due to its independence of the number of design variables. This is essential in large industrial applications, where hundreds of parameters might be needed so as to describe the geometry. Although the computational cost of the methodology is smaller than that of stochastic optimisation methods, the implementation and related program maintenance time and effort could be particularly high. The aim of the present is to contribute to the effort of redusing the cost above by examining whether programs using the adjoint methodology for optimisation can be automatically generated and maintained via Automatic Differentiation, while presenting comparable performance to hand derived adjoints. This could lead to accurate adjoint based optimisation codes, which would inherit any change or addition to the relative original Computational Fluid Dynamics code. Such a methodology is presented and all the different steps involved are detailed. It is found that although a considerable initial effort is required for preparation of the source code for differentiation, hand assembly of the sensitivity algorithms and scripting for the automation of the entire process, the target of this research program is achieved and fully automatically generated adjoint codes with comparable performance can be acquired. After applying the methodology to a number of aerodynamic shape optimisation examples, the logic is also extended to higher derivatives, which could also be included in the optimisation process for robust design.
    Authors
    Christakopoulos, Faidon
    URI
    http://qmro.qmul.ac.uk/xmlui/handle/123456789/8379
    Collections
    • Theses [3321]
    Copyright statements
    The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the author The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the author
    Twitter iconFollow QMUL on Twitter
    Twitter iconFollow QM Research
    Online on twitter
    Facebook iconLike us on Facebook
    • Site Map
    • Privacy and cookies
    • Disclaimer
    • Accessibility
    • Contacts
    • Intranet
    • Current students

    Modern Slavery Statement

    Queen Mary University of London
    Mile End Road
    London E1 4NS
    Tel: +44 (0)20 7882 5555

    © Queen Mary University of London.