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To reduce the environmental impact of civil aviation, future aircraft configurations will be equipped with Very High Aspect Ratio Wings. Such wings enable drag reduction but their structural sizing aiming at weight minimization is a challenge due to high structural constraints. To reduce these constraints, different load alleviation strategies can be employed to reduce the loads for critical sizing cases. Positive and negative gusts are two of these critical cases but they require the measurement of the flow field in front of the aircraft in order to apply relevant load alleviation strategies. This can be done using a wind lidar. To determine the 3D wind field, the lidar is addressed along different axes to obtain the projections of the wind along each of them. In this paper, we present a methodology to incorporate a priori knowledge of the turbulence structure into the estimation procedure.

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Physics-Informed Machine Learning for Reconstruction of Wind Turbulence with Wind Lidar

  • 会议论文

  • 发表日期：

    2026年

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  • Amaury Capmas-Pernet,
  • Christian Musso,
  • Frédéric Dambreville,
  • Matthieu Valla,
  • Tomline Michel

摘要

To reduce the environmental impact of civil aviation, future aircraft configurations will be equipped with Very High Aspect Ratio Wings. Such wings enable drag reduction but their structural sizing aiming at weight minimization is a challenge due to high structural constraints. To reduce these constraints, different load alleviation strategies can be employed to reduce the loads for critical sizing cases. Positive and negative gusts are two of these critical cases but they require the measurement of the flow field in front of the aircraft in order to apply relevant load alleviation strategies. This can be done using a wind lidar. To determine the 3D wind field, the lidar is addressed along different axes to obtain the projections of the wind along each of them. In this paper, we present a methodology to incorporate a priori knowledge of the turbulence structure into the estimation procedure.