OPEN-SOURCE CFD FOR LUBRICATED COMPONENTS: MODELING AERATION, CAVITATION, AND NON-NEWTONIAN EFFECTS WITH COMPUTATIONAL EFFICIENCY

In recent years, energy efficiency has become a key priority across multiple industrial domains. Within the automotive sector, in particular, there is increasing pressure to design drivetrains that balance economic viability with environmental sustainability and operational reliability. Accurately forecasting both the efficiency and lubrication behavior of gearboxes continues to pose a significant engineering challenge. Existing analytical models, largely grounded in empirical correlations and dimensional analysis, typically offer precise predictions only within limited operating conditions. A robust, physics- based methodology capable of reliably simulating lubricant flow patterns and quantifying power losses in geared systems would represent a major step forward. With the rapid advancements in computational technologies, Computational Fluid Dynamics (CFD) has emerged as a powerful resource for investigating lubrication processes and drivetrain efficiency. However, its widespread industrial adoption has been constrained by the high computational demand of such simulations. The introduction of a more efficient mesh-handling framework, combined with the creation of advanced solvers that can address complex phenomena—such as cavitation, aeration, oil mist generation, and non-standard lubrication regimes (e.g., involving non-Newtonian fluids)—has significantly broadened CFD’s practical applicability. Compared to methods from a decade ago, computational time has been reduced by up to 97%, allowing even highly intricate systems to be analyzed within minutes. This paper presents a variety of application cases across different gear geometries, demonstrating the flexibility of the proposed method. Real-world scenarios, including a multi-stage industrial gearbox and a planetary gearbox, are discussed in detail. The developed approach not only proves computationally efficient but also deepens the comprehension of the underlying physical mechanisms, offering theoretical insights into experimental results that were previously difficult to interpret.