Updating multi-fidelity structural dynamic models for flexible wings with feed-forward neural network

• Published in: Proceedings of the Institution of Mechanical Engineers. Part G, Journal of aerospace engineering Journal of aerospace engineering, 2023-06, Vol.237 (7), p.1499-1510
• Main Authors: Huang, Yanxin, Su, Weihua
• Format: Article
• Language: English
• Subjects: Dynamic characteristics; Dynamic models; Flexible wings; Model accuracy; Model updating; Multidisciplinary design optimization; Neural networks; Structural models; Thin walled shells; Wing boxes; Wings (aircraft)
• ISSN: 0954-4100; 2041-3025
• DOI: 10.1177/09544100221128998

In multidisciplinary design optimization of aerospace structures (e.g., a flexible wing), it may be convenient and practical to break such a complex problem into multi-fidelity, multi-stage design problems. Structural model updating is needed in multi-fidelity, multi-stage optimizations to ensure the consistency of models with different fidelity. However, due to the inequality in structural parameters, there exists a fundamental difficulty in the model updating from a lower fidelity model to a higher fidelity model. In this paper, a feed-forward neural network is applied to determine the structural dynamic characteristics of a higher fidelity model based upon a lower fidelity model. The feasibility of this approach is demonstrated by updating beam-like wings to a thin shell-based model and a one-cell wing box model, respectively. The quality and accuracy of model updating using the proposed method are also discussed regarding the neural network structure and sample size.