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

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.

Automated summary

This paper investigates the issue of model updating in multidisciplinary design optimization of aerospace structures, and proposes a method using neural networks to determine the structural dynamic characteristics of a higher fidelity model. The feasibility of this approach is demonstrated through case studies.