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APTNet: A Condition-Sensitive Modulation Framework for Surface Pressure Prediction on Supersonic Aircraft
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DOI：10.1049/cit2.70149.png)
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Abstract
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Accurate surface-pressure prediction over broad operating envelopes is critical for supersonic aerodynamic analysis and design. To overcome the bottlenecks of traditional computational fluid dynamics (CFD) in real-time performance and computational efficiency, data-driven deep learning methods have emerged. However, existing data-driven models face two fundamental physical challenges: first, the pressure response to Mach number, angle of attack and altitude is strongly spatially heterogeneous, which cannot be adequately captured by traditional global condition fusion methods; second, the inherent strong anisotropy of supersonic flows renders standard isotropic graph construction based on Euclidean distance prone to spurious cross-shock connections, resulting in nonphysical smoothing of predictions. To address these issues, we propose aircraft pressure transformer network (APTNet), a physics-informed deep learning framework that explicitly encodes flow–geometry interactions and streamwise physical priors. Specifically, we first propose a condition-sensitive local modulation (CSLM) module, which utilises a surface-normal–freestream alignment term to distinguish windward and leeward regions, and learns per-point sensitivity fields to implement physically interpretable local pressure modulation via a feature-wise linear modulation (FiLM) mechanism. Furthermore, we introduce a flow-aligned anisotropic graph construction strategy, which reshapes the local receptive fields of graph nodes into ellipsoids elongated along streamlines, enforcing information to propagate preferentially along streamlines and suppressing cross-shock feature mixing. Experiments on diverse complex aircraft configurations demonstrate that APTNet consistently outperforms existing baseline models in terms of both accuracy and efficiency. Beyond quantitative improvements, the model provides interpretable intermediate signals, such as attention maps and feature responses, which can be directly integrated into the aerodynamic design workflow.
Keywords:
aerodynamic prediction
condition-sensitive local modulation
flow-aligned graph attention
physics-informed deep learning
surface pressure prediction
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