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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.