Designing an aircraft that operates optimally under various flight conditions requires the consideration of flight operational data in the aircraft design process. Traditionally, aircraft design optimization assumes one nominal or multiple conditions, mostly during the cruise stage, neglecting operational variations. This study introduces a data-driven, cluster-based approach to address this gap, incorporating real-world operational data into the aerodynamic shape optimization of commercial aircraft wings. Using the NASA Common Research Model wing configuration, this study employs a compact modal parameterization method to efficiently capture wing shape variations. A physics-based mission analysis and performance model is employed to extract relevant flight conditions from flight data and evaluate fuel consumption. The Gaussian mixture model is performed on the flight data distribution to identify relevant clusters and derive the multipoint objective function, which is modeled as a weighted average of drag coefficients. The proposed cluster-based 17-point optimization formulation shows a notable total fuel burn reduction of around 2.3% compared to conventional methods (evaluated across 100 of our airline partner’s most flown flight missions), demonstrating the efficacy of incorporating operational information into the optimization problem formulation to improve aerodynamic performance and fuel efficiency.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Operation-Aware Aircraft Wing Design Using Cluster-Based Multipoint Aerodynamic Shape Optimization


    Beteiligte:
    Yang, Aobo (Autor:in) / Lyu, Yuan (Autor:in) / Li, Jichao (Autor:in) / Liem, Rhea P. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.05.2025




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch