DOI: 10.5281/zenodo.17129849
title: Dataset for the paper "Graph-Convolutional Autoencoder Frameworks for Aerodynamic Shape Predictions of the Agard Wing"

description: This repository provides the CFD dataset of wing shape deformations used in:

D. Massegur, A. Da Ronch, G. Immordino, A. Vaiuso, M. Righi."Graph-Convolutional Autoencoder Frameworks for Aerodynamic Shape Predictions of the Agard Wing."AIAA SciTech 2025 Forum.https://doi.org/10.2514/6.2025-XXXX

The dataset consists of steady Reynolds-averaged Navier–Stokes (RANS) simulations of the AGARD 445.6 wing subjected to structural deflections derived from finite-element (FEM) mode shapes. These data were used to train and validate the graph-convolutional autoencoder frameworks for predicting distributed aerodynamic surface fields on parametrically deformed wing shapes.

Test Case





Geometry: AGARD 445.6 wing (sweep, NACA 65A004 airfoil).




CFD mesh: Unstructured type, 45,943 surface nodes, y+≈1..




Flow conditions: Mach number=0.96, Reynolds number=4.51×105.




Solver: SU2 v7.5, Spalart–Allmaras turbulence model, JST scheme with viscous damping, BiCGStab with ILU preconditioner, multigrid V-cycle.




Surface outputs: pressure coefficient (CP), shear-stress components (CFx,CFy,CFz).




Integrated outputs: lift (CL), drag (CD), pitching moment (CMy).



Wing Shape Dataset





Six structural deflection modes included (first/second bending, first/second/third torsion, in-plane bending).




Mode amplitudes scaled to max ±75 mm (~10% semi-span).




Latin Hypercube Sampling (LHS) used to generate a design of experiments (DOE).




Total of 251 wing shapes simulated (including undeflected baseline).




CFD solutions provided for all configurations.



Geometry Generation WorkflowFor each sample in the dataset, the following pipeline was used to generate a deformed geometry prior to CFD analysis:





Mode amplitude definition: assign a set of amplitudes m=[m1,m2,...,m6] to the six FEM deflection modes.




Structural deformation: compute the displacement of the FEM structural nodes based on the chosen modal amplitudes.




Interpolation to CFD mesh: use the Moving Weighted Least Squares Interpolation (MWLSI) method to map FEM node displacements onto the CFD surface mesh nodes.




Deformation file generation: write a displacement file containing the deformed coordinates of the CFD surface mesh.




Mesh morphing: apply the deformation to the full CFD volume mesh using SU2_DEF.




CFD simulation: run SU2 RANS simulations on the deformed geometry to obtain aerodynamic fields.



PurposeThis dataset supports research in geometric deep learning and reduced-order modelling for aerodynamic shape prediction. It enables benchmarking of machine learning-based architectures against traditional CFD, demonstrating efficient surrogate models for optimisation workflows.

Contents





CFD snapshots of pressure and shear-stress fields on the Agard wing for 251 deflected geometries.




Associated mode amplitude parameters for each sample.




Mesh coordinates and connectivity for graph-based learning.



KeywordsAGARD 445.6; transonic aerodynamics; shape deformation; geometric deep learning; graph neural networks; autoencoder; reduced-order modelling; CFD; SU2.