Sangmin Lee, SiHun Lee, Younggeun Park, Seung-Hoon Kang, Kijoo Jang, Haeseong Cho, SangJoon Shin

DOI Number: N/A

Conference number: IFASD-2024-135

This paper presents an efficient nonlinear non-intrusive model order reduction (MOR) framework for the gust load analysis. The proposed method is based on artificial neural network (ANN), specifically a least-square hierarchical variational autoencoder (LSH-VAE). This approach will enable construction of nonlinear reduced-order model and allow accurate interpolation with regard to the parameters. The proposed method will be validated by applying for a high-altitude long-endurance (HALE) unmanned aerial vehicle (UAV). The accuracy and computational efficiency of the method will be compared against those by a full order model (FOM). It is found that the proposed method will construct accurate interpolated field with regard to the relevant parameters.

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In Categories: 1. IFASD 2024, 1.Events, Reduced Order Models
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