Computational and experimental study reveals accurate thermal modeling in wet friction components, indicating effective cross-scale prediction of lubrication and temperature dynamics.
This study proposes a cross-scale coupling method integrating a microscopic mixed lubrication model and a macroscopic temperature field model. The microscopic mixed lubrication model fully considers the elastic deformation of rough surfaces and the dynamic variation of oil film thickness. It can dynamically update the friction coefficient according to local parameters such as pressure, speed, and temperature. The macroscopic temperature field model adopts the particle swarm optimization (PSO) algorithm to optimize the heat partition coefficient, so as to ensure the accuracy of the simulation. The dynamic heat partition coefficient evolves through three stages: an inert stage, a time-varying stage, and a steady-state stage. Its variation reflects the dynamic equilibrium between heat exchange and heat partition across the friction pair. The two models establish a cross-scale interaction mechanism. The microscopic model provides friction coefficient data at various radii and time instants for the macroscopic temperature field, while the macroscopic model modifies the lubrication parameters by feeding back the variation of the oil film temperature. UMT (Universal Micro-Tribotester) experiments and sliding friction bench experiments were performed to verify the model. Based on comprehensive statistics over all tested operating conditions, the mean errors of the friction coefficient and the final temperature are 4.29% and 5.36%, respectively. Meanwhile, this study investigates the effects of operating parameters on the evolution of the friction coefficient and temperature characteristics.
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Zhang et al. (2026) studied this question.
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