Abstract
The efficiency of lubrication in rolling bearings is a key topic in industry and research, particularly for computational fluid dynamics (CFD) analysis. This review critically examines 56 studies on CFD applications in rolling bearing lubrication, identified across three databases, evaluating not only how the literature can be categorised but where its modelling assumptions and validation practices constrain current use. Oil remains the dominant lubricant studied, especially in ball bearings, while grease lubrication is comparatively underrepresented; this gap is shown to stem mainly from limited CFD adoption for grease (7 of 32 grease-related studies) rather than weaker validation within the grease-CFD studies that do exist. Among mesh dynamics strategies, the multiple reference frame (MRF) and rotating moving mesh (RMM) approaches are the most frequently used for roller bearings, but this frequency coincides with commercial-software availability rather than demonstrated accuracy, since quantitative data exist almost exclusively for MRF and no study directly compares the two. The review identifies specific research gaps, including benchmark comparisons of mesh dynamics strategies, transferable grease rheology models, empirical loss correlations derived from existing CFD data, and open validation datasets needed to move CFD from a research tool toward reliable bearing design practice.