Modeling in tribology: recent advances, applications, and open questions

Recent advances in modeling have enhanced our ability to make quantitative predictions for tribological phenomena, thereby unraveling relevant mechanisms. Algorithmic innovations, including those based on multiscale methods and machine learning, have been especially impactful, for example in overcoming long-standing bottlenecks that hinder simulations of systems with strong coupling across disparate scales. However, traditional modeling approaches, such as boundary-element techniques, have also progressed and continue to yield new insights. This article reviews developments from the past decade, examining how both new and established methods have deepened our understanding of experimental results and have furthered theoretical approaches in key tribological areas, including contact mechanics, lubrication, metal friction, and tribo-chemistry. Selected applications, such as tunable interfaces and energy harvesting, illustrate the broad influence of recent developments on fields beyond tribology itself.