Bulletin of the American Physical Society
77th Annual Meeting of the Division of Fluid Dynamics
Sunday–Tuesday, November 24–26, 2024; Salt Lake City, Utah
Session X12: Low-Order Modeling and Machine Learning in Fluid Dynamics: Methods VI
8:00 AM–10:10 AM,
Tuesday, November 26, 2024
Room: 155 B
Chair: Fotis Sotiropoulos, Virginia Commonwealth University
Abstract: X12.00010 : Predicting steady-state flow over two-dimensional airfoils using graph neural networks*
9:57 AM–10:10 AM
Presenter:
Sankalp Jena
(Delft University of Technology, The Netherlands)
Authors:
Sankalp Jena
(Delft University of Technology, The Netherlands)
Gabriel D Weymouth
(Delft University of Technology, The Netherlands)
Artur K Lidtke
(Maritime Research Institute Netherlands (MARIN), The Netherlands)
Andrea Coraddu
(Delft University of Technology, The Netherlands)
The primary objective of this study is to provide fast and accurate predictions of surface pressure and friction by blending potential flow solutions (Panel Method) with viscous flow simulations (Reynolds-averaged Navier-Stokes). The study will progressively increase the complexity of the flow conditions that the GNN needs to predict, starting with potential flow and then moving on to viscous and separated flows. The accuracy of the GNN will be evaluated by comparing the integral quantities such as lift, drag, and moment with those from other models. It will be compared with NeuralFoil, a deep neural network surrogate trained on 7.5 million XFOIL simulations, and a convolutional neural network. We expect the GNN to provide more accurate predictions due to its implicit geometry consideration.
*This research is supported by the granting authority European Research Executive Agency (REA) under the Marie Skłodowska-Curie Actions funded project, SCALE (HORIZON-MSCA-2022 DN-01-0).
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