Not only for aircraft performance calculations and trajectory predictions but also for air traffic management simulation tools and operational equipment, accomplishment of an accurate propulsive model for transport aircraft has a crucial priority and is a remarkable topic for aircraft industry. In the literature, there are very few propulsive modeling studies; furthermore, the demand for an accurate thrust model still remains unfulfilled. In this study, a new turbofan engine propulsive model determining the relationship between thrust, flight altitude, and Mach number was developed by using genetic algorithms (GAs) method and multilayer feed-forward neural networks (FNNs) utilizing Levenberg-Marquardt (LM), delta-bar-delta (DBD), and conjugate gradient (CG) learning algorithms. Estimated thrust values by the derived models showed a good fitting with actual thrust data for both models, which validated each model’s accuracy.


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    Titel :

    Propulsive Modeling for TF-30 and JT9D-3 Turbofan Engines of Transport Aircraft by Using Genetic Algorithms and Neural Networks



    Erschienen in:

    Erscheinungsdatum :

    2015-10-19


    Format / Umfang :

    5 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






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