Scholarlink

<p>The low-altitude economy promises new options for urban transport and logistics but is constrained by gust-induced, highly unsteady aerodynamics that threaten flight safety. We present a Graph Transformer framework that fuses a surface-pressure graph with temporal attention to predict gust-induced unsteady aerodynamic loads. Sparse pressure taps are encoded as a full-link graph aligned with the airframe topology. Comparative studies show the necessity of a full-link graph, compared to streamwise or crosswise links, for precise gust modeling and highlight the complex gust flow patterns that include crosswise flows. The attention mechanism learns gust-onset-based attention patterns, enabling the model to identify and prioritize critical temporal phases of gust events. By correlating the complex spatiotemporal scales, the unified framework demonstrates robust performance on various challenging gust scenarios, delivering consistent and accurate multi-output predictions. Our framework provides a practical path for robust gust modeling, contributing to flight safety, thereby advancing the low-altitude economy.</p><p></p>

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Bridging spatial and temporal surface pressure dynamics for gust aerodynamic modeling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月2日

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  • Dashuai Chen,
  • Aoming Liang,
  • Boai Sun,
  • David E. Rival,
  • Dixia Fan

摘要

The low-altitude economy promises new options for urban transport and logistics but is constrained by gust-induced, highly unsteady aerodynamics that threaten flight safety. We present a Graph Transformer framework that fuses a surface-pressure graph with temporal attention to predict gust-induced unsteady aerodynamic loads. Sparse pressure taps are encoded as a full-link graph aligned with the airframe topology. Comparative studies show the necessity of a full-link graph, compared to streamwise or crosswise links, for precise gust modeling and highlight the complex gust flow patterns that include crosswise flows. The attention mechanism learns gust-onset-based attention patterns, enabling the model to identify and prioritize critical temporal phases of gust events. By correlating the complex spatiotemporal scales, the unified framework demonstrates robust performance on various challenging gust scenarios, delivering consistent and accurate multi-output predictions. Our framework provides a practical path for robust gust modeling, contributing to flight safety, thereby advancing the low-altitude economy.