CFD-simulation of no-load power losses and the oil distribution of spur gears

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No-load gear power losses account for a considerable proportion of the main losses in high-speed gears. Currently, there is no efficient method to quantify these losses. At present, em-pirical equations are used, despite these formulas could just provide roughly approximated values. No-load gear losses are affected by the current flow conditions in the gearbox. Again, there is no efficient method to predict these conditions. Consequently, the development orthe optimization of geared transmissions is impeded. The efficiency optimization in gear en-gineering could be remarkably improved by a precise and yet efficient predicting method. As an alternative to empirical equations, the most promising method is the use of computational fluid dynamics. The main aim of the proposed project is the development of an optimized and validated CFD simulation model based on the SPH method in order to predict the no-load gear losses and the oil distribution in geared transmissions. The results for the no-load losses will be validated by the measurements, whereas the simulated oil distribution is compared to the recordings made by a high-speed camera.In collaboration with the Institute of Machine Elements and the Institute of Aerodynamics and Fluid Mechanics of the Technical University of Munich, the interaction based SPH method will be optimized and compared to the Finite-Volume Method. Both methods have already been used by both institutes for similar applications.It has already been shown that the SPH method has great potential from a numerical point of view. By comparing the results with the Finite-Volume Method at the end of the project, an established base of evaluation will be created. The possibility of applying the SPH method for the prediction of no-load losses and the oil distribution in geared transmissions will be explored.In this project, potentials of CFD applications in the research field of gearbox simulation will be detected, which can reduce the time consuming and expensive experimental investigations in the future.