BlendedNet++ provides a new dataset of 12,492 BWB geometries with RANS-derived Cp and Cf fields and benchmarks geometric deep learning for field prediction plus conditional diffusion models for inverse design achieving R^2 > 0.99 on lift-to-drag targets verified by CFD.
Artificial neural networks to predict aerodynamic coefficients of transport airplanes.Aircraft Engineering and Aerospace Technology, 89(2):211–230, Mar 2017
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BlendedNet++: A dataset and benchmark for field-resolved aerodynamics and inverse design of blended wing body aircraft
BlendedNet++ provides a new dataset of 12,492 BWB geometries with RANS-derived Cp and Cf fields and benchmarks geometric deep learning for field prediction plus conditional diffusion models for inverse design achieving R^2 > 0.99 on lift-to-drag targets verified by CFD.