基于物理信息的神经网络的非定常流平均流重建

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2023-01-25 DOI:10.1017/dce.2022.37
Lukasz Sliwinski, Georgios Rigas
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引用次数: 3

摘要

流量测量数据同化是流体动力学问题中提取信息的重要工具。最近的研究表明,如果给定足够的流量测量值,并在时间和空间上适当分布,那么物理信息神经网络(pinn)能够重建由Navier-Stokes方程控制的非定常流体流动。然而,在许多实际应用中,实验测量只涉及时间平均量或它们的高阶统计量,这些量由欠定的reynolds -average Navier-Stokes (RANS)方程控制。在本研究中,我们利用稀疏速度数据对非定常流的时间平均量进行了基于ppin的重建。应用技术利用时间平均速度数据来推断未知的闭合量(非定常RANS强迫旋度),以及从稀疏测量中插值场。此外,该方法的功能进一步扩展到雷诺兹应力的同化,其中pinn成功地插值数据以完成速度和应力场,并深入了解所研究流动的压力场。
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Mean flow reconstruction of unsteady flows using physics-informed neural networks
Abstract Data assimilation of flow measurements is an essential tool for extracting information in fluid dynamics problems. Recent works have shown that the physics-informed neural networks (PINNs) enable the reconstruction of unsteady fluid flows, governed by the Navier–Stokes equations, if the network is given enough flow measurements that are appropriately distributed in time and space. In many practical applications, however, experimental measurements involve only time-averaged quantities or their higher order statistics which are governed by the under-determined Reynolds-averaged Navier–Stokes (RANS) equations. In this study, we perform PINN-based reconstruction of time-averaged quantities of an unsteady flow from sparse velocity data. The applied technique leverages the time-averaged velocity data to infer unknown closure quantities (curl of unsteady RANS forcing), as well as to interpolate the fields from sparse measurements. Furthermore, the method’s capabilities are extended further to the assimilation of Reynolds stresses where PINNs successfully interpolate the data to complete the velocity as well as the stresses fields and gain insight into the pressure field of the investigated flow.
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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