In this study, an aerostructural analysis using a proper orthogonal decomposition with a neural network is proposed for accurate and efficient aerostructural wing design optimization using the reduced-order model. Because reduced-order-model basis weighting estimation has a limitation in that its robustness cannot be guaranteed by various design variables and wing deformation due to fluid structure interaction, this study employs the neural network, which is capable of perceiving the relationship between the input variables and reduced variables for the proper orthogonal decomposition to complement the defects. To construct the proper orthogonal decomposition with a neural network, the neural network is learned using pairs of design variables and reduced variables from snapshot data obtained from the aerostructural analysis. Because the proposed aerostructural analysis using a proper orthogonal decomposition with a neural network is applied to validation cases and its results are compared to those of the full-order analysis, it is investigated that the proposed analysis algorithm has the capability to accurately and efficiently predict the aerodynamic and structural performances of wings that are considered about wing deformation. Furthermore, because the design optimization problem minimizing the weight of a wing design is performed with the analysis algorithm, it is confirmed that it can be a more efficient design than a conventional design method using a second-order polynomial model, which consists of a greater number of experiment designs than the number of snapshots.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Reduced-Order Model with an Artificial Neural Network for Aerostructural Design Optimization


    Contributors:

    Published in:

    Journal of Aircraft ; 50 , 4 ; 1106-1116


    Publication date :

    2013-07-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English






    Aerostructural Wing Design Exploration with Multidisciplinary Design Optimization

    Bons, Nicolas / Martins, Joaquim R. R. A. | AIAA | 2020


    Aerostructural Control based on Parametric State-Consistent Reduced Order Models

    Sevart, Chadwick D. / Kim, Jinhyuk / Shu, Jung I. et al. | AIAA | 2024


    Aerostructural Optimization of Drooped Wings

    Khosravi, Shahriar / Zingg, David W. | AIAA | 2018