Comparison of Artificial Neural Network-Based and Adaptive Quadratic Neural Network-Based Multi-Fidelity Algorithms for Buckling Load Prediction of Stiffened Panels

Date

2023-4-14

Author

Yaşar, Hüseyin Avni

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This thesis presents a novel approach for predicting the buckling load of stiffened panels using multi-fidelity modeling based on the quadratic neural networks (QNNs) with adaptive activation functions. The effectiveness of the proposed approach is demonstrated through a series of simulations on a range of stiffened panel configurations, and the results are compared to those obtained from traditional multi-fidelity modeling methods in terms of accuracy and computational efficiency. Numerical experiments demonstrate that the model can accurately and efficiently predict the buckling load of stiffened panels, while significantly reducing the computational cost of evaluating the surrogate model. This approach can significantly improve the design and optimization of aerospace structures by easily and quickly exploring various design configurations and finding stable and efficient configurations. Overall, this study highlights the potential of multi-fidelity modeling for predicting the buckling load of aerospace structures, and the effectiveness of using QNNs with the adaptive activation functions.

Subject Keywords

Multi-Fidelity Models, Surrogate Model, Adaptive Activation Functions, Artificial Neural Networks, Buckling, Stiffened Panels

URI

https://hdl.handle.net/11511/103028

Collections

Graduate School of Applied Mathematics, Thesis