Abstract The efficient and accurate access to the aerodynamic performance is important for the design and optimization of supercritical airfoils. The aerodynamic performance is usually obtained by using computational fluid dynamics (CFD) methods or wind-tunnel experiments. But the computations of CFD are very time intensive and expensive, and the prior knowledge in wind-tunnel experiments plays a decisive role in engineering. Though many surrogate methods were proposed to alleviate the costs of these traditional approaches, most of them can only calculate the low-dimensional aerodynamic performance, and is not able to provide the accurate prediction of transonic flow fields for supercritical airfoils. Since the flow fields are equipped with its own discipline as a physical system in fluid dynamics, it is therefore possible to learn this discipline via data-driven machine learning approaches. Deep learning is witness to expansive growth into diverse applications due to its immense ability to extract essential features from complicated physical systems. Generative adversarial networks (GANs) as a recent popular method in deep leaning are capable of efficiently capturing the distribution of training data. In this work, we proposed a surrogate model, ffsGAN, which leverage the property of GANs combined with convolution neural networks (CNNs) to directly establish a one-to-one mapping from a parameterized supercritical airfoil to its corresponding transonic flow field profile over the parametric space. Compared with the most existing surrogate models, the ffsGAN is superior in efficiently and accurately predicting the high-dimensional flow field rather than the low-dimensional aerodynamic characteristics. The ffsGAN method is first trained using 500 airfoils that sampled based on RAE2822. The flow fields are then predicted for unseen airfoils to evaluate the generalization of the model in terms of prediction accuracy. An investigation of the effects of various hyper-parameters in the network architectures and loss functions is performed. The experimental results show that ffsGAN is a promising tool for rapid evaluation of detailed aerodynamic performance. The elaborate flow field predicted by ffsGAN is possible to be considered in airfoil design to further improve the design and optimization quality in the future.

中文翻译：

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一种高效准确评估超临界翼型流场的深度学习方法

摘要 高效、准确地获取气动性能对于超临界翼型的设计和优化具有重要意义。空气动力学性能通常通过使用计算流体动力学 (CFD) 方法或风洞实验获得。但是 CFD 的计算非常耗时且昂贵，并且风洞实验中的先验知识在工程中起着决定性的作用。尽管提出了许多替代方法来降低这些传统方法的成本，但大多数只能计算低维气动性能，无法提供超临界翼型跨音速流场的准确预测。由于流场作为流体动力学中的物理系统具有自己的学科，因此，可以通过数据驱动的机器学习方法来学习这门学科。由于其从复杂物理系统中提取基本特征的强大能力，深度学习见证了其在各种应用中的广泛增长。作为最近流行的深度学习方法，生成对抗网络 (GAN) 能够有效地捕获训练数据的分布。在这项工作中，我们提出了一个替代模型 ffsGAN，它利用 GAN 的特性与卷积神经网络 (CNN) 相结合，直接建立从参数化超临界翼型到其相应跨音速流场剖面的一对一映射。参数空间。与大多数现有的代理模型相比，ffsGAN 在高效准确地预测高维流场方面优于低维空气动力学特性。ffsGAN 方法首先使用基于 RAE2822 采样的 500 个翼型进行训练。然后针对看不见的翼型预测流场，以评估模型在预测精度方面的泛化。对网络架构和损失函数中各种超参数的影响进行了调查。实验结果表明，ffsGAN 是一种用于快速评估详细空气动力学性能的有前途的工具。ffsGAN 预测的精细流场可以在翼型设计中加以考虑，以进一步提高未来的设计和优化质量。ffsGAN 方法首先使用基于 RAE2822 采样的 500 个翼型进行训练。然后针对看不见的翼型预测流场，以评估模型在预测精度方面的泛化。对网络架构和损失函数中各种超参数的影响进行了调查。实验结果表明，ffsGAN 是一种用于快速评估详细空气动力学性能的有前途的工具。ffsGAN 预测的精细流场可以在翼型设计中加以考虑，以进一步提高未来的设计和优化质量。ffsGAN 方法首先使用基于 RAE2822 采样的 500 个翼型进行训练。然后针对看不见的翼型预测流场，以评估模型在预测精度方面的泛化。对网络架构和损失函数中各种超参数的影响进行了调查。实验结果表明，ffsGAN 是一种用于快速评估详细空气动力学性能的有前途的工具。ffsGAN 预测的精细流场可以在翼型设计中加以考虑，以进一步提高未来的设计和优化质量。对网络架构和损失函数中各种超参数的影响进行了调查。实验结果表明，ffsGAN 是一种用于快速评估详细空气动力学性能的有前途的工具。ffsGAN 预测的精细流场可以在翼型设计中加以考虑，以进一步提高未来的设计和优化质量。对网络架构和损失函数中各种超参数的影响进行了调查。实验结果表明，ffsGAN 是一种用于快速评估详细空气动力学性能的有前途的工具。ffsGAN 预测的精细流场可以在翼型设计中加以考虑，以进一步提高未来的设计和优化质量。