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<p>Aerodynamic modeling of 3D aircraft under varying flight conditions is essential for aircraft optimization design. This study formulates the task as condition-guided regression of the surface pressure field using a point cloud-based deep surrogate model, in which surface mesh points, geometric attributes, and flight-condition parameters are jointly represented. The dataset covers Mach number from 3.0 to 10.0 and angle of attack from −10° to 10°. Within this framework, the key challenge is how to fuse condition and geometry features and how to aggregate local geometry while preserving generalization across configurations and data scales. To address this issue, we construct task-specific datasets, design representative static and dynamic fusion modules, and perform systematic ablation experiments on geometric aggregation backbones. The obtained results lead to two practical guidelines. First, condition information should be introduced through progressive static fusion, whereas late concatenation and attention-based reweighting require caution; modulation inspired by deep operator networks is more promising when sufficient data are available. Second, a hybrid aggregation backbone that uses dynamic aggregation in dense point cloud stages and static aggregation in sparse stages offers a better balance between accuracy and computational cost.</p>

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Feature engineering for pressure field modeling of general aircraft under multi-condition scenarios

  • 期刊论文

  • 发表日期：

    2026年7月16日

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  • Hao Zhang,
  • Yang Shen,
  • Hao Wang,
  • Wei Huang,
  • Yaobin Niu,
  • Shuangxi Liu,
  • Chaoyang Liu

摘要

Aerodynamic modeling of 3D aircraft under varying flight conditions is essential for aircraft optimization design. This study formulates the task as condition-guided regression of the surface pressure field using a point cloud-based deep surrogate model, in which surface mesh points, geometric attributes, and flight-condition parameters are jointly represented. The dataset covers Mach number from 3.0 to 10.0 and angle of attack from −10° to 10°. Within this framework, the key challenge is how to fuse condition and geometry features and how to aggregate local geometry while preserving generalization across configurations and data scales. To address this issue, we construct task-specific datasets, design representative static and dynamic fusion modules, and perform systematic ablation experiments on geometric aggregation backbones. The obtained results lead to two practical guidelines. First, condition information should be introduced through progressive static fusion, whereas late concatenation and attention-based reweighting require caution; modulation inspired by deep operator networks is more promising when sufficient data are available. Second, a hybrid aggregation backbone that uses dynamic aggregation in dense point cloud stages and static aggregation in sparse stages offers a better balance between accuracy and computational cost.