Dynamic Analysis of a Rotor System Supported on Squeeze Film Damper with Air Entrainment
Squeeze film dampers (SFDs) are widely used in compressors and turbines to suppress the vibration while traversing critical speeds. In practical applications, air ingestion from the outside environment and cavitation may lead to a foamy lubricant that weakens oil film damping and dynamic performance of rotor system. In this paper, a rigid rotor model is established considering both lateral and pitching vibration under different imbalance excitations to evaluate the effect of air entrainment on rotor system. Tests with three different imbalances are carried out on a rotor-SFD apparatus. Volume controlled air in mixture ranging from pure oil to all air are supplied to the SFD. The transient response of rotor is measured in the experiments. The results show that two-phase flow produces significant influence on the system stability and dynamical response. The damping properties are weakened by entrained air, such as the damping on high frequency components of rolling ball bearing. Super-harmonic resonance and bifurcation are observed, as well as the low frequency components due to air entrainment.
Deep-Learning-Based Information Fusion Methodology for Oil Film Coefficient Identification of Squeeze Film Dampers
Squeeze film damper (SFD) is one of the key components in the sophisticated rotating machines in the high-end aerospace industry. Rolling bearings in the aeroengine spindle system are often used in combination with elastic supports and SFDs to reduce rotor vibrations. Accurate coefficient identification of oil films in SFDs is critical to ensure safe and reliable operations of the complex equipment. However, this task remains a challenging issue in the past decades due to the difficulties in real-time measurement and computation during operations. The highly nonlinear relationship between the measurement and oil film coefficient cannot be effectively captured using traditional methods. This article proposes a novel deep-learning-based information fusion methodology for oil film coefficient identification of SFDs. An end-to-end deep neural network model structure is established, which explores heterogeneous SFD data simultaneously with fusion architecture and directly outputs the identification results. The high-dimensional data can be automatically processed, and high identification accuracy can be achieved. Experiments on the SFD test rig are carried out for validation. The experimental results demonstrate that the proposed method can well identify the oil film coefficients in an intelligent manner and is robust against environmental noise and different parameter settings. The proposed methodology is, thus, promising for applications in real industries.
Measurements of Pressure Distributions and Force Coefficients in a Squeeze Film Damper. Part 2: Partially Sealed Configuration
In Part I, a squeeze film damper (SFD) test rig and measurement procedures were explained, arid the experimental results obtained from an open ended damper were presented. In this paper, the experimental results measured from a partially sealed SFD test rig executing a circular centered orbit are presented and discussed. A serrated piston ring is installed at the damper exit. This device involves a new sealing concept which produces high damping values while allowing for oil flow to cool the damper. In the partially sealed damper, large cavitation regions are observed in the pressure fields at orbit radii e = 0.5 and e = 0.8. The cavitated pressure distributions and the corresponding force coefficients are compared with a cavitated bearing solution. The experimental results show the significance of fluid inertia and vapor cavitation in the operation of squeeze film dampers. Squeeze film Reynolds numbers tested reach up to Re = 50, spanning the range of contemporary applications. Presented as a Society of ...