论文标题
使用物理知情神经网络（PINN）准确的近壁稳定流场预测
Accurate near wall steady flow field prediction using Physics Informed Neural Network (PINN)
论文作者
论文摘要
在本文中，探索了物理知识的神经网络（PINN），以便在距离壁上的测量（或采样点）准确地获得墙壁区域附近的流动预测。通常，在流体力学实验中，很难准确地在墙壁附近进行速度测量。因此，本研究揭示了一种新的优雅方法，可以恢复墙附近的流动解决方案。为了探索Pinn准确预测流场的能力，考虑了本研究的层状边界层流动。这项研究的所有必需的采样数据都是从CFD模拟获得的。已经研究了从RE = 500到100000的广泛雷诺数案例。首先，使用PINN，通过三种不同类型的边界条件获得边界层解决方案。此外，分析了采样点的位置对精度的影响。从速度曲线和皮肤摩擦系数分布中，很明显，Pinn的结果在壁附近相当准确，仅几个采样点远离墙壁。该方法在实验中具有潜在的应用，以准确地获得近壁溶液，并从距离壁上进行测量。
In this paper, Physics Informed Neural Network (PINN) is explored in order to obtain flow predictions near the wall region accurately with measurements (or sampling points) away from the wall. Often, in fluid mechanics experiments, it is difficult to perform velocity measurements near the wall accurately. Therefore, the present study reveals a new and elegant approach to recover the flow solutions near the wall. Laminar boundary layer flow over a flat plate case is considered for this study in order to explore the ability of PINN to accurately predict the flow field. All the required sampling data for this study is obtained from CFD simulations. A wide range of Reynolds number cases from Re=500 to 100000 has been investigated. First, using PINN, the boundary layer solution is obtained with three different types of boundary conditions. Further, the influence of the location of the sampling points on the accuracy is analysed. From the velocity profiles and the skin friction coefficient distribution, it is clear that PINN results are reasonably accurate near the wall with only a few sampling points away from the wall. This approach has potential application in experiments to obtain the near wall solutions accurately with measurements away from the wall.
