Morphing wings have garnered widespread attention due to their superior aerodynamic efficiency. However, in the design process, accurately and efficiently obtaining the three-dimensional flow field of morphing wings remains a challenging issue. This paper proposes a Deep Learning-based method for predicting the flow field of a Biomimetic Morphing Wings to address this problem. Firstly, a Coordinate Transformation Mechanism is established for the studied Biomimetic Morphing Wing to ensure the consistency of grid point coordinates between different wing shapes. Secondly, a two-level Flow Field Prediction Model is constructed, consisting of grid point prediction level and physical quantity continuity adjustment level. Using this method, the flow field of the Biomimetic Morphing Wing was predicted, and the predication result were similar to those of numerical simulations. This indicates that the proposed method maintains high prediction accuracy while reducing computation time, thereby enhancing the analysis efficiency of the morphing wing's flow field.

中文翻译：

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基于深度学习的三维变形机翼空气动力学分析方法


变形机翼因其卓越的空气动力学效率而受到广泛关注。然而，在设计过程中，准确有效地获得变形机翼的三维流场仍然是一个具有挑战性的问题。本文提出了一种基于深度学习的方法来预测仿生变形翅膀的流场来解决这个问题。首先，为所研究的仿生变形机翼建立坐标变换机制，以保证不同机翼形状之间网格点坐标的一致性;其次，构建了两级流场预测模型，由网格点预测水平和物理量连续性调整水平组成;利用这种方法，预测了仿生变形翼的流场，预测结果与数值模拟的结果相似。这表明所提方法在减少计算时间的同时保持了较高的预测精度，从而提高了变形翼流场的分析效率。