Numerical Investigation of Gas Dynamic Foil Bearings Conjugated with Contact Friction by Elastic Multi-Leaf Foils

With increasing demand for load capacity and dynamic responses, the new generation of Gas Dynamic Foil Bearings (GDFB), assembled from preloaded elastic multi-leaf foils, has been put forward. It exhibits steady and dynamic characteristics that are more sensitive to the contact friction produced by foil–foil bearings and the housings of foil bearings, distinguishing its response to rigidity from those of classic bearings. This study aims to employ numerical simulations to explore GDFBs conjugated with contact friction using elastic multi-leaf foils. A numerical method with Bidirectional Fluid–Structure Interactions (BFSIs), which strengthens the steady and dynamic characteristics of GDFBs, has been developed. The results showed that the distribution of steady stiffness was closely associated with the configuration of the foil assembly and the state of contact friction. The maximum steady stiffness occurred near the key blocks and was accompanied by the support of bump foils. The contact friction increased the stiffness of the foil assembly, which was unfavorable to its operational stability, and the GDFB was prone to rigidity. The contact friction hindered the pull-back of the top foils near the negative pressure area. An increase in the rotational speed and eccentricity increased the load capacity while decreasing the operational stability. The maximum pressure of the gas film was inconsistent with the maximum deformation along the circumferential direction because of the configuration of the foil assembly. The contact friction induced energy dissipation, whereas increases in the integrated stiffness inhibited loss. The fixed constraint of the key block increased the dynamic cross-stiffness more than the contact constraint did, which was unfavorable to operational stability. The results obtained in this study are significant because they provide a physical basis for GDFBs and can be used to guide their operational optimization.