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GNN for Airfoil Flow Prediction by Chidiebere Chukwuemeka is a document available to read on EtoBox.

This study explores the use of Graph Neural Networks (GNN) for predicting flow fields around airfoils and optimizing their geometry through machine learning methods. GNNs demonstrate higher accuracy and significantly reduced computation time compared to traditional methods, making them suitable for aerodynamic shape optimization. The research combines GNN with optimization techniques like Bayesian optimization and gradient-based methods to enhance performance in maximizing lift/drag ratios for airfoils.

Author

Chidiebere Chukwuemeka

Language

EN