论文标题
Fournetflows：稳定机翼流量预测的有效模型
FourNetFlows: An efficient model for steady airfoil flows prediction
论文作者
论文摘要
Fournetflows是用于机翼流的傅立叶神经网络的缩写，是一个有效的模型，可快速准确地预测稳定的机翼流量。我们选择傅立叶神经操作员（FNO）作为骨干结构，并利用OpenFoam生成翼型流量的数值解决方案进行培训。我们的结果表明，Fournetflow与与Spalart-Allmaras湍流模型集成的压力链接方程（简单）的半密度方法的准确性是数值算法之一。 Fournetflows还用于预测围绕椭圆形的流，该椭圆形的形状绝对与训练集中的样品不同。我们注意到，定性和定量结果都与数值结果一致。同时，Fournetflows在几秒钟内解决了数千种解决方案，比经典数值方法快的数量级。令人惊讶的是，Fournetflows在较低的分辨率下进行训练时，以零射击超级分辨率实现模型流动。当解决方案的分辨率增加时，推断时间几乎是恒定的。
FourNetFlows, the abbreviation of Fourier Neural Network for Airfoil Flows, is an efficient model that provides quick and accurate predictions of steady airfoil flows. We choose the Fourier Neural Operator (FNO) as the backbone architecture and utilize OpenFOAM to generate numerical solutions of airfoil flows for training. Our results indicate that FourNetFlows matches the accuracy of the Semi-Implicit Method for Pressure Linked Equations (SIMPLE) integrated with the Spalart-Allmaras turbulence model, one of the numerical algorithms. FourNetFlows is also used to predict flows around an oval whose shape is definitely different from samples in the training set. We note that both qualitative and quantitative results are consistent with the numerical results. Meanwhile, FourNetFlows solves thousands of solutions in seconds, orders of magnitude faster than the classical numerical method. Surprisingly, FourNetFlows achieves model flows with zero-shot super-resolution when it is trained under a lower resolution. And the inferring time is almost constant when the resolution of solutions is increasing.
