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

Faras Brumand-Poor; Florian Barlog; Nils Plückhahn; Matteo Thebelt; Niklas Bauer; Katharina Schmitz

doi:10.20944/preprints202410.1000.v1

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preprints.org > computer science and mathematics > artificial intelligence and machine learning > doi: 10.20944/preprints202410.1000.v1

Preprint Article Version 1 This version is not peer-reviewed

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

^* ,

Version 1 : Received: 12 October 2024 / Approved: 13 October 2024 / Online: 14 October 2024 (11:30:14 CEST)

How to cite: 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. Preprints 2024, 2024101000. https://doi.org/10.20944/preprints202410.1000.v1 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. Preprints 2024, 2024101000. https://doi.org/10.20944/preprints202410.1000.v1

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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. Preprints 2024, 2024101000. https://doi.org/10.20944/preprints202410.1000.v1

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. Preprints. https://doi.org/10.20944/preprints202410.1000.v1

Chicago/Turabian Style

Brumand-Poor, F., Niklas Bauer and Katharina Schmitz. 2024 "Physics-Informed Neural Networks for the Reynolds Equation with Transient Cavitation Modeling" Preprints. https://doi.org/10.20944/preprints202410.1000.v1

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

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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