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Lubrication plays a pivotal role in modern society, given its significant economic and environmental implications, particularly in relation to friction, wear and the failure of moving mechanical systems. With recent breakthroughs in computational architectures, the development of advanced simulation frameworks has been greatly accelerated, facilitating the study of surfaces, lubricants and additives at unprecedented scales. However, the inherently multiscale nature of lubricated contacts necessitates a delicate balance between computationally efficient continuum descriptions and detailed atomistic accuracy for addressing the complex physiochemical phenomena spanning vastly different spatiotemporal scales. This review explores the dilemma of modelling inherently multiphysics tribological interactions, which drive the evolution of lubricated interfaces and shape tribosystem performances across the scales as accurately and simultaneously as efficiently as possible. It critically examines state-of-the-art modelling tools, their applications and limitations across spatiotemporal domains. Moreover, the capacity for machine learning to aggregate extensive datasets, address multi-physical complexities ranging from atomic dimensions to macroscopic scales and accelerate simulation workflows is explored, offering transformative perspectives for the future of lubrication modelling. • Recent progress and innovative trends in methods for modelling interfacial phenomena in lubricated contacts are analysed. • Intricate interfacial interactions that underpin friction, wear and failure in lubrication systems are examined. • Macro-, micro- and nano-scale approaches are bridged to offer a cohesive understanding of the interfacial processes. • Perspective research paths centred on interfacial behaviour and essential for advancing lubrication modelling are identified.
Ardah et al. (Thu,) studied this question.