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.

翻译：本文展示了利用卷积神经网络（CNN）和深度神经网络（DNN）技术，根据目标压力分布（吸力面和压力面）预测翼型形状，以及反之由翼型形状预测压力分布的方法。数据集基于1600种翼型形状生成，模拟的雷诺数（Re）范围从10,000到90,000,000，攻角（AoA）范围从0到15度，确保了数据集涵盖了多样化的气动条件。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，经过优化的模型展现出更高的效率，对于包含不同翼型、雷诺数和攻角的复杂数据集，DNN模型的训练时间相比CNN模型实现了数倍的缩减。预测的翼型形状/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。然而，研究发现CNN模型的性能优于DNN模型。最后，研究以一个翼展大于10米的飞翼飞机模型为例，预测了其沿弦向的压力分布。所提出的CNN和DNN模型均显示出良好的预测结果。本研究凸显了深度学习模型在加速气动优化和推进高性能翼型设计方面的潜力。