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Park K.H.,Seoul National University | Jun S.O.,Samsung | Baek S.M.,Hyundai Heavy Industries | Baek S.M.,Construction Equipment Research Institute | And 4 more authors.
Journal of Aircraft | Year: 2013

In this study, an aerostructural analysis using a proper orthogonal decompositionwith a neural network is proposed for accurate and efficient aerostructural wing design optimization using the reduced-order model. Because reducedorder- 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 aboutwing 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. © 2012 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

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