Lubricants (Oct 2024)

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
https://doi.org/10.3390/lubricants12110365
Journal volume & issue
Vol. 12, no. 11
p. 365

Abstract

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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.

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