In the framework of the H2020 project IDERPLANE, aimed at providing innovative, effective, and validated criteria for the design and assessment of more reliable planet bearings for aerospace application analyzing the problem from a damage tolerance perspective, the present paper presents the numerical study and optimization of a test rig specifically designed for the experiments on the full-test article. Specifically, for the first time ever, an entire system including shafts, gears and bearings with all the rolling elements have been studied with a Finite Volume Computational Fluid Dynamics approach. This ambitious challenge was addressed with the implementation of a new mesh handling technique, namely the Global Remeshing Approach with Mesh Clustering (GRAMC). The aim was to optimize the lubrication of the test article to avoid unexpected failures during the experimental campaign. Three different oil jet directions have been studied and the most effective one, namely the axial one, was selected for the final test rig design.
The correct prediction of ductile fracture of mechanical components requires the knowledge of physical quantities that are in the plastic field. This region is characterized by non-linearities, and the classical yield criteria cannot be applied since they work only in the elastic field. It has been observed that parameters such as stress triaxiality and plastic strain play a determinant role in failure mechanisms. Thanks to simulation software, it is possible to implement the virtual models capable of calculating these parameters numerically by solving partial differential equations. These parameters can then be used to describe the fracture locus of a material that, in turn, allows to predict failure of a component. In this work, the Rice and Tracey damage model was calibrated for an aluminum alloy and validated on a punch test exploiting Finite Element Analysis. Good agreement between experimental observations and numerical results was obtained, demonstrating the capability of the considered model to predict failure on a real test case.
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