Optimizing Load Capacity Predictions in Gas Foil Thrust Bearings: A Novel Full-Ramp Model

Abstract

Gas film thickness significantly influences the performance prediction of Gas Foil Thrust Bearings (GFTB). However, the Classical Model (CM) for GFTBs exhibits inaccuracies in describing gas film thickness. In this paper, we explore the differences in the details of gas film thickness modeling and propose a Parallel Segmentation Model (PSM), which fixes the errors of the CM in describing the gas film thickness in the ramp section, and a Full-Ramp Model (FRM), to which a more realistic description of the gas film in the flat section is also added. Comparative analysis, utilizing a publicly available test dataset based on the open-source GFTB structure, establishes that the FRM surpasses the CM and PSM in accurately predicting load capacity. In-depth analysis shows that the location of the minimum gas film thickness for determining the load capacity is located at the innermost circle of the free end of the top foil, whereas the FRM is subjected to the same load with a larger film thickness at this location, which may be due to the unique geometry of the top foil of the FRM. Subsequently, employing the FRM, a parametric study explores load capacity in GFTB, considering variables such as ramp height, top foil thickness, bump foil stiffness, ramp section extent, and top foil area. The results demonstrate that GFTB load capacity exhibits a linear increase with the expansion of the top foil area. Moreover, the load capacity increases with augmented top foil thickness and bump foil stiffness, albeit at a decreasing rate. Additionally, an increase in ramp section extent initially enhances load capacity, reaching a maximum value before declining. Similarly, an increase in ramp height initially augments load capacity, attaining a maximum before subsequent diminution.

Design and Study of Composite Film Preparation Platform

“…We constructed linear regression (LR), a random forest model (RFM), K-nearest neighbors (KNNs), decision tree (DT), and bagging and boosting models using Python in the PyCharm IDE. The performance of these models in terms of film thickness prediction was evaluated using metrics such as explained variance score (EV), mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), and the coefficient of determination R2 score [34][35][36][37].…”