Physics-Informed Neural Networks for the Reynolds Equation with Transient Cavitation Modeling

被引:1
|
作者
Brumand-Poor, Faras [1 ]
Barlog, Florian [1 ]
Plueckhahn, Nils [1 ]
Thebelt, Matteo [1 ]
Bauer, Niklas [1 ]
Schmitz, Katharina [1 ]
机构
[1] Rhein Westfal TH Aachen, Inst Fluid Power Drives & Syst IFAS, D-52074 Aachen, Germany
关键词
hydrodynamic lubrication; physics-informed neural networks; average Reynolds equation with transient cavitation; physics-informed machine learning; elastohydrodynamic; machine learning; AVERAGE FLOW MODEL;
D O I
10.3390/lubricants12110365
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Gaining insight into tribological systems is crucial for optimizing efficiency and prolonging operational lifespans in technical systems. Experimental investigations are time-consuming and costly, especially for reciprocating seals in fluid power systems. Elastohydrodynamic lubrication (EHL) simulations offer an alternative but demand significant computational resources. Physics-informed neural networks (PINNs) provide a promising solution using physics-based approaches to solve partial differential equations. While PINNs have successfully modeled hydrodynamics with stationary cavitation, they have yet to address transient cavitation with dynamic geometry changes. This contribution applies a PINN framework to predict pressure build-up and transient cavitation in sealing contacts with dynamic geometry changes. The results demonstrate the potential of PINNs for modeling tribological systems and highlight their significance in enhancing computational efficiency.
引用
收藏
页数:18
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