Surrogate Modeling of the Aerodynamic Performance for Airfoils in Transonic Regime

The method

Neural surrogates for the hardest flight regime

The transonic regime — where shock waves form and flow becomes exquisitely sensitive to geometry and Mach number — is among the most challenging domains in aerodynamics. This work trains artificial neural networks on OpenFOAM-generated data spanning a wide range of transonic conditions to predict airfoil aerodynamic coefficients near-instantly, replacing expensive RANS evaluations in early design.

Transonic flow over an airfoil: shock formation makes this regime uniquely difficult to model.

Contributions

What the paper delivers

Transonic CFD dataset

A systematically generated OpenFOAM corpus covering wide ranges of transonic flow conditions over airfoil geometries.

ANN coefficient surrogate

Networks predict lift and drag coefficients directly from flow conditions and geometry, at negligible cost.

Open-sourced pipeline

Data generation and training code released for the community.

Reference

Citation

@inproceedings{elrefaie2024transonic,
  title     = {Surrogate Modeling of the Aerodynamic Performance for
               Airfoils in Transonic Regime},
  author    = {Elrefaie, Mohamed and Ayman, Tarek and Elrefaie, Mayar and
               Sayed, Eman and Ayyad, Mahmoud and AbdelRahman, Mohamed M.},
  booktitle = {AIAA SCITECH 2024 Forum},
  doi       = {10.2514/6.2024-2220},
  year      = {2024}
}

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