| dc.description.abstract | Abstract. Due to their low damping and nonlinear characteristics, gas foil bearings are susceptible to an imbalanced amount, so accurately predicting their dynamic response is crucial. The current time-domain-based rotor orbit prediction method has two problems. First, it is computationally expensive and time-consuming. Second, the simplified model for the complex foil structure results in low accuracy. This study combines the artificial neural network method with a novel fully aeroelastic coupling model of a multi-leaf journal foil bearing (MLJFB), which considers assembly preload, friction, and the interaction between the rotor, top foil, bump foil, and sleeve, to construct a rapid prediction model for gas film load capacity. Based on this prediction model, a nonlinear rotordynamic model is developed to enable efficient estimation of nonlinear responses, including the rotor orbit. A test rig for a high-speed MLJFB was designed and built to verify the accuracy of the theoretical model. The effects of load and rotational speed on the rotor orbits were then analyzed using a combination of theoretical and experimental approaches, with the results showing good agreement. This study provides a rapid method for predicting rotor orbits, which offer a valuable reference for the efficient optimal design and practical application of MLJFB. | |
