Transferable Low-Dimensional Representations of Aircraft Surface Fields

Abstract

Analyzing flow fields on aerodynamic surfaces is critical to designing next-generation aircraft. Raw computational representations are high-dimensional and costly for complex three-dimensional bodies, while conventional low-dimensional representations apply only to the systems from which they derive. We introduce an approach that learns fluid flow representations from simple two-dimensional geometries and transfers them to complex real-world three-dimensional aircraft. Comparing the proposed Least Volume autoencoder (LVAE) to proper orthogonal decomposition (POD), we show both yield compact 28-41 dimensional representations of two-dimensional flow fields, with the nonlinear LVAE producing more accurate in-domain reconstructions at matched dimensionality. These models transfer zero-shot to three-dimensional systems, and counterintuitively the linear POD transfers more accurately than LVAE. Applied to extruded wings, POD-reconstructed lift and drag remain within 0.1% and 0.7% of true values, while transfer to complex blended wing body aircraft preserves lift and drag to within 0.3% and 0.7% without any three-dimensional training data. We further show that three-dimensional flow patterns missed by the pretrained model can be isolated to train a disentangled 3D latent space via a dual encoder-decoder transfer learning strategy, performing accurately in previously inaccessible data-scarce regimes. Using only five blended wing training cases, mean squared reconstruction error drops by 5x relative to the 2D pretrained models and 70x relative to models trained from scratch. These results establish that transfer learning from readily available two-dimensional datasets unlocks compact, interpretable representations of complex three-dimensional aircraft surface flows, enabling design insights in data-starved early phases where conventional approaches require hundreds to thousands of simulations.