Abstract
In recent years, the importance of energy efficiency has steadily increased across multiple industrial sectors. For example, the automotive industry faces growing demands to develop drivetrains that are not only cost-effective but also environmentally sustainable and dependable. Accurately predicting both efficiency and lubricant behaviour in gearboxes remains a central engineering challenge. Existing models in literature, often based on empirical correlations and dimensional analyses, tend to offer precise results only within limited operating conditions. A comprehensive methodology capable of accurately forecasting lubricant flow patterns and power losses in gear systems could significantly advance the state of the art. Recent advancements in computing technology have facilitated the application of Computational Fluid Dynamics (CFD) as a vital tool for engineers exploring gear lubrication and efficiency. Nevertheless, the adoption of CFD has historically been limited by the substantial computational demands required for simulations. The introduction of computationally efficient mesh-handling strategies, combined with solvers designed to address complex phenomena such as cavitation, aeration, oil mist, and non-Newtonian fluid behaviour, has paved the way for wider industrial application. Compared to a decade ago, the computational load has been reduced by as much as 97%, enabling simulations of intricate systems within minutes. This paper presents case studies across different gear types, demonstrating the flexibility of the developed approach. It also includes real-world examples, such as an industrial multi-stage gearbox and a planetary gearbox. The presented methodology not only enhances understanding of the physical processes involved in gearbox lubrication but also offers theoretical explanations for experimental observations that previously lacked clear interpretation.