Low order models can be used to accelerate engineering design processes. Ideally, these surrogates should meet the conflicting requirements of large design space coverage, high accuracy and fast evaluation. Within the context of aerospace applications at transonic conditions, this can be challenging due to the associated non-linearity of the flow regime. Different methods have been investigated in the past to predict the flow-field around shapes such as airfoils or cylinders. However, they usually have reduced spatial resolution, limiting the prediction capabilities within the boundary layer which is of interest for transonic wall-bounded flows. This work proposes a novel Point-Enhanced Convolutional Neural Network (PCNN) method that combines the advantages of the well-established PointNet and convolutional neural network approaches. The PCNN model has relatively low memory requirements in the training process, preserves the spatial correlation in the domain and has the same resolution as a traditional computational method. The architecture is used for the flow-field prediction of civil aero-engine nacelles in which it is demonstrated that the flow features of peak isentropic Mach number (Mis), pre-shock isentropic Mach number and shock location (X/Lnac) are captured within ΔMis = 0.02, ΔMis=0.04, ΔX/Lnac=0.007, respectively. The PCNN model successfully predicts the integral parameters of the boundary layer, in which the incompressible displacement thickness, momentum thickness and shape factor are typically within 5% of the CFD. Overall, the PCNN method is demonstrated for transonic wall-bounded flows for a range of flow physics that include shock waves and shock-induced separation.

中文翻译：

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点增强卷积神经网络：一种用于跨音速壁有界流的新型深度学习方法


低阶模型可用于加速工程设计流程。理想情况下，这些替代项应满足大设计空间覆盖率、高精度和快速评估的冲突要求。在跨音速条件下的航空航天应用中，由于流态的相关非线性，这可能具有挑战性。过去已经研究了不同的方法来预测形状（如翼型或圆柱体）周围的流场。然而，它们通常具有较低的空间分辨率，限制了边界层内的预测能力，而这对于跨音速壁有界流来说是感兴趣的。这项工作提出了一种新的点增强卷积神经网络 （PCNN） 方法，该方法结合了成熟的 PointNet 和卷积神经网络方法的优点。PCNN 模型在训练过程中对内存的要求相对较低，保留了域中的空间相关性，并且具有与传统计算方法相同的分辨率。该架构用于民用航空发动机机舱的流场预测，证明了峰值等熵马赫数 （Mis）、冲击前等熵马赫数和冲击位置 （X/Lnac） 的流动特征分别在 ΔMis = 0.02、ΔMis=0.04、ΔX/Lnac=0.007 范围内捕获。PCNN 模型成功预测了边界层的积分参数，其中不可压缩位移厚度、动量厚度和形状因子通常在 CFD 的 5% 以内。总体而言，PCNN 方法适用于一系列流动物理场的跨音速壁有界流，包括激波和激波诱导分离。