Detailed Program of the 22nd ISC 2024

Advancing Lubrication Calculation: A Physics-Informed Neural Network Framework for Transient Effects and Cavitation Phenomena in Reciprocating Seals

Summary

In numerous technical applications, gaining insights into the behavior of tribological systems is crucial for optimizing efficiency and prolonging operational lifespans. Experimental investigations of such systems require considerable costs and time investments, particularly in the field of sealing, notably reciprocating seals for fluid power systems. A more feasible method is the application of elastohydrodynamic lubrication (EHL) simulation models, such as the dynamic description of sealings (DDS) model, which compute friction of seals by the hydrodynamics within the sealing contact according to the Reynolds equation, the seal’s deformation, and the contact mechanics. The main drawback of these distributed parameter simulations is the necessity of a time-intensive resolution process. Given these experimental and computational constraints, machine learning algorithms offer a promising solution.

Physics-informed machine learning (PIML) represents a noteworthy advancement in machine learning in tribology, extending traditional models with physics-based rules and enhancing accuracy in determining phenomena such as friction, wear, and lubrication. Within this field, physics-informed neural networks (PINN) emerge as a powerful class of hybrid solvers, combining data-driven and physics-based approaches to solve partial differential equations, the governing equations in EHL simulations. By integrating physical principles into the neural network’s parameter optimization, PINNs provide an accurate and accelerated solution.

In this contribution, a PINN framework is applied to predict pressure build-up and cavitation in sealing contacts with housing. The capability of PINNs to determine transient and cavitation effects is thoroughly investigated and validated by the solution of the Reynolds equation obtained by the DDS. The results demonstrate the potential of PINNs for modeling tribological systems and highlight their significance in enhancing computational efficiency.