Data to 7 Oct 2026

Prediction of Aerodynamic Flow Fields Using Convolutional Neural Networks

arXiv 1905.13166 ↗First posted 30 May 2019physics.flu-dyn cs.CEcited by 144

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Abstract (arXiv)

An approximation model based on convolutional neural networks (CNNs) is proposed for flow field predictions. The CNN is used to predict the velocity and pressure field in unseen flow conditions and geometries given the pixelated shape of the object. In particular, we consider Reynolds Averaged Navier-Stokes (RANS) flow solutions over airfoil shapes. The CNN can automatically detect essential features with minimal human supervision and shown to effectively estimate the velocity and pressure field orders of magnitude faster than the RANS solver, making it possible to study the impact of the airfoil shape and operating conditions on the aerodynamic forces and the flow field in near-real time. The use of specific convolution operations, parameter sharing, and robustness to noise are shown to enhance the predictive capabilities of CNN. We explore the network architecture and its effectiveness in predicting the flow field for different airfoil shapes, angles of attack, and Reynolds numbers.

Key facts. Cited by 144 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 May 2019.

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Citing papers per year: 2021: 25; 2022: 36; 2023: 28; 2024: 15; 2025: 22; 2026: 18.

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