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Volume 12
Issue 11
10.3390/lubricants12110365
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Article

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

by
Faras Brumand-Poor
*,
Florian Barlog
,
Nils Plückhahn
,
Matteo Thebelt
,
Niklas Bauer
and
Katharina Schmitz
Institute for Fluid Power Drives and Systems (ifas), RWTH Aachen University, 52074 Aachen, Germany
*
Author to whom correspondence should be addressed.
Lubricants 2024, 12(11), 365; https://doi.org/10.3390/lubricants12110365
Submission received: 11 October 2024 / Revised: 21 October 2024 / Accepted: 22 October 2024 / Published: 23 October 2024
(This article belongs to the Special Issue Intelligent Algorithms for Triboinformatics)
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Abstract

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.
Keywords: hydrodynamic lubrication; physics-informed neural networks; average Reynolds equation with transient cavitation; physics-informed machine learning; elastohydrodynamic; machine learning hydrodynamic lubrication; physics-informed neural networks; average Reynolds equation with transient cavitation; physics-informed machine learning; elastohydrodynamic; machine learning

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MDPI and ACS Style

Brumand-Poor, F.; Barlog, F.; Plückhahn, N.; Thebelt, M.; Bauer, N.; Schmitz, K. Physics-Informed Neural Networks for the Reynolds Equation with Transient Cavitation Modeling. Lubricants 2024, 12, 365. https://doi.org/10.3390/lubricants12110365

AMA Style

Brumand-Poor F, Barlog F, Plückhahn N, Thebelt M, Bauer N, Schmitz K. Physics-Informed Neural Networks for the Reynolds Equation with Transient Cavitation Modeling. Lubricants. 2024; 12(11):365. https://doi.org/10.3390/lubricants12110365

Chicago/Turabian Style

Brumand-Poor, Faras, Florian Barlog, Nils Plückhahn, Matteo Thebelt, Niklas Bauer, and Katharina Schmitz. 2024. "Physics-Informed Neural Networks for the Reynolds Equation with Transient Cavitation Modeling" Lubricants 12, no. 11: 365. https://doi.org/10.3390/lubricants12110365

APA Style

Brumand-Poor, F., Barlog, F., Plückhahn, N., Thebelt, M., Bauer, N., & Schmitz, K. (2024). Physics-Informed Neural Networks for the Reynolds Equation with Transient Cavitation Modeling. Lubricants, 12(11), 365. https://doi.org/10.3390/lubricants12110365

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