
doi: 10.1002/ls.1380
handle: 11311/1045941
AbstractThanks to the recent developments in the computer science, simulations are becoming an increasingly widespread approach that can help the designers in the development of new products. In the specific field of gearboxes, simulations are used mainly for structural evaluations. However, while for the structural design beside the simulations, many analytical methods and international standard are available; for the prediction of the power losses and the efficiency of gears, neither accurate analytical methods nor automated simulation tools are available. The authors work on this topic since years and have developed new methodologies based on computational fluid dynamics. With respect to general purpose commercial software, these techniques allow a significant reduction of the computational effort and have the capability to take into account particular physical phenomena that occurs in gears, such as cavitation, and for which no information are available in literature. The purpose of this paper is to introduce a new automated mesh‐partitioning strategy implemented to extend the applicability of the previously developed computational effort reduction method to complex gearboxes getting over the geometrical limitations adopted in the past. To show the capabilities of this new strategy, we simulated a planetary gearbox that represents at the same time one of the most complicated kinematic arrangements of gears and the configuration for which the numerical fluid dynamics simulation can give the major contribution both with planar simplified models as well as with complete 3D models.
CFD; churning; gear; meshing; planetary; squeezing; Surfaces, Coatings and Films; Materials Chemistry2506 Metals and Alloys
CFD; churning; gear; meshing; planetary; squeezing; Surfaces, Coatings and Films; Materials Chemistry2506 Metals and Alloys
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 55 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
