用神经网络加速机翼设计，可快速预测气动压力分布与机翼形状。
Accelerated Airfoil Design Using Neural Network Approaches
- 用CNN和DNN模型双向预测机翼形状与压力分布。
- 在雷诺数1万至90万、攻角0到15度条件下，模型预测精度高。
- 适合航空设计优化，尤其对大型飞翼结构有应用前景。
本文展示了利用卷积神经网络（CNN）和深度神经网络（DNN）技术，实现机翼形状与目标压力分布（吸力面和压力面）之间的相互预测。数据集包含1600个机翼形状，模拟条件覆盖雷诺数（Re）从10,000到900,000、攻角（AoA）从0°到15°的广泛范围，确保涵盖多样的气动工况。根据输入输出参数的不同，开发了五种不同的CNN和DNN模型。结果表明，优化后的模型效率显著提升，对于包含复杂变化的机翼、雷诺数和攻角的数据集，DNN模型相比CNN模型实现了训练时间的数倍减少。预测出的机翼形状和压力分布与目标值高度吻合，验证了深度学习框架的有效性。尽管如此，CNN模型在性能上仍优于DNN模型。最后，以展长超过10米的飞翼飞机模型为例，预测沿弦向的压力分布，所提出的CNN和DNN模型表现良好。研究凸显了深度学习在加速气动优化和提升高性能机翼设计方面的潜力。
原文摘要 · Abstract (English)
In this paper, prediction of airfoil shape from targeted pressure distribution (suction and pressure sides) and vice versa is demonstrated using both Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) techniques. The dataset is generated for 1600 airfoil shapes, with simulations carried out at Reynolds numbers (Re) ranging from 10,000 and 90,00,000 and angles of attack (AoA) ranging from 0 to 15 degrees, ensuring the dataset captured diverse aerodynamic conditions. Five different CNN and DNN models are developed depending on the input/output parameters. Results demonstrate that the refined models exhibit improved efficiency, with the DNN model achieving a multi-fold reduction in training time compared to the CNN model for complex datasets consisting of varying airfoil, Re, and AoA. The predicted airfoil shapes/pressure distribution closely match the targeted values, validating the effectiveness of deep learning frameworks. However, the performance of CNN models is found to be better compared to DNN models. Lastly, a flying wing aircraft model of wingspan >10 m is considered for the prediction of pressure distribution along the chordwise. The proposed CNN and DNN models show promising results. This research underscores the potential of deep learning models accelerating aerodynamic optimization and advancing the design of high-performance airfoils.
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