Investigation of Physics-Informed Neural Networks Based Solution Techniques for Internal Flows

Pascal Post, Benjamin Winhart, Francesca di Mare
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引用次数: 3

Abstract

In this work, we explore for the first time the possibility and potentials of employing the emerging PINNs approach in internal flow configurations, solving the steady state Euler equations in two dimensions for forward and inverse problems. In addition to a simple bump test case, the PINNs results of a highly loaded transonic linear turbine guide vane cascade are presented. For forward problems, we investigate different formulations of the transport equations and boundary conditions. Overall, PINNs approximate the solution with acceptable accuracy; however, conventional CFD methods are far superior in forward settings. Finally, we demonstrate the capabilities and the tremendous potentials of PINNs regarding hidden fluid mechanics in two distinct inverse settings, intractable for conventional CFD methods. Firstly, we infer complete flow fields based on partial, possible noisy, solution data, e.g., partial surface pressure and velocity field data; even approximating the exit condition of the cascade using only the measured blade pressure distribution is possible. Secondly, we also infer an unknown parameter of the governing equations.
基于物理信息神经网络的内部流动求解技术研究
在这项工作中,我们首次探索了在内部流动配置中使用新兴的pinn方法的可能性和潜力,在二维中解决稳态欧拉方程的正反问题。除了一个简单的碰撞试验案例外,还给出了高负荷跨声速线性涡轮导叶叶栅的PINNs结果。对于正问题,我们研究了输运方程和边界条件的不同形式。总的来说,pin以可接受的精度近似解;然而,传统的CFD方法在正向设置中要优越得多。最后,我们展示了pinn在两种不同的逆设置中隐藏流体力学的能力和巨大潜力,这对于传统的CFD方法来说是难以解决的。首先,我们根据部分的、可能有噪声的解数据,例如部分的表面压力和速度场数据,推断出完整的流场;甚至仅使用测量的叶片压力分布就可以近似地求得叶栅的出口状态。其次,我们还推导了控制方程的一个未知参数。
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