物理
涡流
跟踪(教育)
粒子跟踪测速
粒子(生态学)
经典力学
涡流环
粒子图像测速
机械
统计物理学
湍流
心理学
教育学
海洋学
地质学
作者
Ben Steinfurth,Abdelrahman Hassanein,Nguyen Anh Khoa Doan,Fulvio Scarano
摘要
Phase-resolved volumetric velocity measurements of a pulsed jet are conducted by means of three-dimensional particle tracking velocimetry (PTV). The resulting scattered and relatively sparse data are densely reconstructed by adopting physics-informed neural networks (PINNs), here regularized by the Navier–Stokes equations. It is shown that the assimilation remains robust even at low particle densities (ppp<10−3) where the mean particle distance is larger than 10% of the outlet diameter. This is achieved by enforcing compliance with the governing equations, thereby leveraging the spatiotemporal evolution of the measured flow field. Thus, the PINN reconstructs unambiguously velocity, vorticity, and pressure fields, enabling a robust identification of vortex structures with a level of detail not attainable with conventional methods (binning) or more advanced data assimilation techniques (vortex-in-cell). The results of this article suggest that the PINN methodology is inherently suited to the assimilation of PTV data, in particular under conditions of severe data sparsity encountered in experiments with limited control of the seeding concentration and/or distribution.
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