A comprehensive review and trends in lubrication modelling

Abstract

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.