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.

All major rules and guidelines include fatigue design (FAT) classes for flush ground butt-welded joints. These FAT classes vary between FAT110 and FAT155; however, in the majority of cases, the underlying database and specimen-related details are unclear or unknown. This study evaluates 1003 fatigue test results gathered from various literature sources and tries to relate the fatigue strength improvement to typical specimen types and test conditions. To this goal, statistical methods based on correlation analysis are employed. Next, proposals for updates of rules and guidelines for flush ground butt-welded joints made of steel are established by determining new FAT classes and a suitable slope exponent. In addition, an overview of design standards and recommendations is given and main influencing factors are discussed.

In many technical applications, understanding the behavior of tribological contacts is pivotal for enhancing efficiency and lifetime. Traditional experimental investigations into tribology are often both costly and time-consuming. A more profound insight can be achieved through elastohydrodynamic lubrication (EHL) simulation models, such as the ifas-DDS, which determines precise friction calculations in reciprocating pneumatic seals. Similar to other distributed parameter simulations, EHL simulations require a labor-intensive resolution process. Physics-informed neural networks (PINNs) offer an innovative method to expedite the computation of such complex simulations by incorporating the underlying physical equations into the neural network’s parameter optimization process. A hydrodynamic PINN framework has been developed and validated for a variant of the Reynolds equation. This paper elucidates the framework’s capacity to handle multi-case scenarios—utilizing one PINN for various simulations—and its ability to extrapolate solutions beyond a limited training domain. The outcomes demonstrate that PINNs can overcome the typical limitation of neural networks in extrapolating the solution space, showcasing a significant advancement in computational efficiency and model adaptability.

This contribution presents an elastohydrodynamic lubrication (EHL) model for pneumatic spool valves. For an accurate estimation of the transient friction of this tribological sealing system, the surface topography of the cylindrical sealing counterfaces of the valve housings are measured and analyzed with an optical surface measurement instrument. Based on the surface topography data, tribological properties and flow factors of the system are derived. It has been found that the consideration of the surface topography has a significant influence on the simulation results of the EHL model, lowering the calculated friction force by up to 20 %.

The numerically stable simulation of cavitation effects is mandatory for predicting the friction and wear behavior of translational hydraulic seals. This contribution provides a comparison of two different implementations of the Jakobsson-Floberg-Olsson (JFO) cavitation model, an investigation of their properties and possible options for their stabilization. These methods are tested and compared both within a simple divergent gap test case as well as within an EHL simulation of a rubber metal contact. Based on these comparisons and theoretical investigations, the strengths and weaknesses of the different methods are summarized and discussed with respect to an application in EHL simulations of translational hydraulic seals.

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.