论文标题
从2D超临界机翼转移到3D透射扫式翅膀的研究
Study of transfer learning from 2D supercritical airfoils to 3D transonic swept wings
论文作者
论文摘要
机器学习已被广泛用于流体力学研究和空气动力学优化。但是，大多数应用，尤其是流场建模和逆设计，都涉及二维流和几何形状。三维问题的维度如此之高，以至于准备足够的样品太困难和昂贵。因此，转移学习已成为重用训练有素的二维模型的一种有希望的方法，并大大减少了对三维问题的样品的需求。本文提议重复使用在超临界机翼上训练的基线模型，以预测有限跨度的超临界翅膀，其中简单的扫描理论被嵌入以提高预测准确性。研究了两个用于转移学习的基线模型：一个通常称为基于几何形状预测压力系数分布的正向问题，另一个是基于压力系数分布来预测几何形状的反问题。比较了两个基线模型的两种转移学习策略。然后，根据完整机翼的预测对传输的模型进行测试。结果表明，转移学习仅需要大约500个机翼样品，即可在不同的机翼平面和不同的自由流条件上实现良好的预测准确性。与两个基线模型相比，转移的模型分别将预测误差降低了60％和80％。
Machine learning has been widely utilized in fluid mechanics studies and aerodynamic optimizations. However, most applications, especially flow field modeling and inverse design, involve two-dimensional flows and geometries. The dimensionality of three-dimensional problems is so high that it is too difficult and expensive to prepare sufficient samples. Therefore, transfer learning has become a promising approach to reuse well-trained two-dimensional models and greatly reduce the need for samples for three-dimensional problems. This paper proposes to reuse the baseline models trained on supercritical airfoils to predict finite-span swept supercritical wings, where the simple swept theory is embedded to improve the prediction accuracy. Two baseline models for transfer learning are investigated: one is commonly referred to as the forward problem of predicting the pressure coefficient distribution based on the geometry, and the other is the inverse problem that predicts the geometry based on the pressure coefficient distribution. Two transfer learning strategies are compared for both baseline models. The transferred models are then tested on the prediction of complete wings. The results show that transfer learning requires only approximately 500 wing samples to achieve good prediction accuracy on different wing planforms and different free stream conditions. Compared to the two baseline models, the transferred models reduce the prediction error by 60% and 80%, respectively.
