Affiliation:
1. Laboratory for Flow Instabilities and Dynamics, Technische Universität Berlin, Berlin 10623, Germany
2. Chair of Fluid Dynamics, Technische Universität Berlin, Berlin 10623, Germany
Abstract
Physics-informed neural networks (PINNs) can be used to solve partial differential equations (PDEs) and identify hidden variables by incorporating the governing equations into neural network training. In this study, we apply PINNs to the assimilation of turbulent mean flow data and investigate the method's ability to identify inaccessible variables and closure terms from sparse data. Using high-fidelity large-eddy simulation data and particle image velocimetry measured mean fields, we show that PINNs are suitable for simultaneously identifying multiple missing quantities in turbulent flows and providing continuous and differentiable mean fields consistent with the provided PDEs. In this way, consistent and complete mean states can be provided, which are essential for linearized mean field methods. The presented method does not require a grid or discretization scheme, is easy to implement, and can be used for a wide range of applications, making it a very promising tool for mean field-based methods in fluid mechanics.
Subject
Condensed Matter Physics,Fluid Flow and Transfer Processes,Mechanics of Materials,Computational Mechanics,Mechanical Engineering
Cited by
15 articles.
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