Improving aircraft performance using machine learning: a review

Abstract

This review covers the new developments in machine learning (ML) that are impacting the multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics (experimental and numerical), aerodynamics, acoustics, combustion and structural health monitoring. We review the state of the art, gathering the advantages and challenges of ML methods across different aerospace disciplines and provide our view on future opportunities. The basic concepts and the most relevant strategies for ML are presented together with the most relevant applications in aerospace engineering, revealing that ML is improving aircraft performance and that these techniques will have a large impact in the near future.

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• Reconstructing three-dimensional bluff body wake from sectional flow fields with convolutional neural networks
• A Certifiable Machine Learning-Based Pipeline to Predict Fatigue Life of Aircraft Structures
• On the solution of Euclidean path integrals with neural networks
• A data-driven approach for modeling large-amplitude flow-induced oscillations of elastically mounted pitching wings