In modern turbine rotors, tip-leakage flow is a common phenomenon that accounts for about 1/3 of the stage loss. Studies show that as the imposed load increases, a shock wave appears in the tip region, which causes a significant interference on the leakage vortex. In the present study, numerical simulations are carried out to investigate the influence of the shock wave on the loss and breakdown of the tip-leakage vortex. The obtained results indicate that with no effective control on the flow, the loss of the leakage vortex has an approximate exponential growth up to about 10 times as the outlet Mach number increases from 0.67 to 1.15 and the corresponding proportion in the total loss increases sharply to 30.2%. It is found that the stagnation position of the breakdown changes with the backpressure and the amplitude of variation along the axial direction is up to 0.13 C_x. It is inferred that the breakdown of the leakage vortex core may be affected by the periodical passing of downstream blade and the induced pressure fluctuation may result in additional vibration in this rotor blade. The leakage vortex is unstable in supersonic flow with a shock wave and it may transfer to a flow with a low-velocity bubble in its core region. It is concluded that the leakage vortex breakdown mainly originates from interferences of the shock wave, while the internal cause of such breakdown is the centrifugal instability of the vortex.

Inertial loads induced by base motion excitation introduce significant complexities in equilibrium point determination and linearization of systems incorporating squeeze film dampers (SFDs). The coupled effects of base motion excitation and SFD characteristics on periodic stability have received limited attention in previous investigations. This study investigates the dynamic characteristics and periodic stability of a rotor system with SFD subjected to base motion excitation. A finite element model of the rotor-SFD-support system under non-inertial motion is established. The periodic responses are solved using the harmonic balance method with alternating frequency/time technique (HB-AFT) and the arc-length continuation algorithm incorporating the predictor–corrector method, while system stability is analyzed using Floquet theory and the Newmark method. A systematic parametric study is conducted to investigate the effects of base motion parameters, mass unbalance, and SFD parameters on the system’s periodic response. Results demonstrate that base pitching motion enhances system stability, suppresses bistable responses and jump phenomena, and reduces unstable vibration regions. However, under specific parameter combinations, pitching motion can trigger secondary Hopf bifurcations, leading to quasi-periodic and chaotic motions, among other complex nonlinear behaviors. This research provides theoretical foundations for stability-oriented design optimization of rotor systems with SFDs under base motion excitation.

When operating at high speed, aeroengines mounted on the aircrafts also perform complicated maneuvering flights along with the aircrafts. The engine rotor systems will undergo forced motions excited by base movements and mass unbalance, which may lead to severe vibrations. Squeeze film damper (SFD) is generally used for vibration damping in aeroengine, but it also introduces structural nonlinearity like bistable jump. Moreover, the reliability of SFD is also affected by base excitations. Therefore, it is necessary to investigate the nonlinear dynamic behavior of rotor-SFD-support system excited by base motions. Taking a muti-disk rotor shaft supported by two SFDs with squirrel cages as the research object to simulate a gas generator in a turboshaft engine, the steady-state responses of the rotor system were calculated by multidimensional harmonic balance combined with alternating frequency/time domain method (MHB-AFT). The effects of base harmonic rotations and mass unbalance on steady-state responses of rotor system were investigated. The results indicate that the time-varying parametric excitations of base motions have strong effects on amplitude–frequency response of transverse displacement of rotor system. The critical speeds and resonant amplitudes of responses change with the magnitude and frequency of several base harmonic rotations. The variation of the frequency of base harmonic motion has the most significant impact on the amplitude–frequency response of transverse displacement of rotor system. The increase of base harmonic frequency will lead to multiple local peaks in the response curves, especially in the case of base harmonic rolling motion. In addition, the combination of base harmonic rotation around pitching and yawing axes results in significantly different response characteristics. Therefore, the influence of base harmonic motions should be considered during the structural design and damping optimization of SFDs.

Multi-hole pressure probes are crucial for turbomachinery flow measurements, yet conventional data processing methods often lack generalization for complex flows. This study introduces an innovative approach by integrating machine learning techniques with the structural risk minimization (SRM) principle, significantly enhancing the generalization capability of regression models. A comprehensive framework has been developed, combining SRM-based machine learning regression methods, such as ridge regression and kernel ridge regression, with hyperparameter optimization and S-fold cross-validation, to ensure robust model selection and accuracy. Validated using the McCormick function and applied to VKI-RG transonic turbine cascade measurements, the SRM-based methods demonstrated superior performance over traditional empirical risk minimization approaches, with lower error ratios and higher $R^2$ values. Novel insights from SHAP analysis revealed subtle but significant differences in aerodynamic parameters, including a $0.63122°$ discrepancy in exit flow angle predictions, guiding the probe design and calibration strategies. This study presents a holistic workflow for improving multi-hole pressure probe measurements under high-subsonic conditions, representing a meaningful enhancement over traditional empirical methods and providing valuable references for practical applications.

This study introduces a novel predictive model for atomization droplet size, developed using comprehensive data collected under elevated temperature and pressure conditions using a twin swirl airblast nozzle. The model, grounded in flow instability theory, has been meticulously parameterized using the Particle Swarm Optimization (PSO) algorithm. Through rigorous analysis, including analysis of variance (ANOVA), the model has demonstrated robust reliability and precision, with a maximum relative error of 19.3% and an average relative error of 6.8%. Compared to the classical atomization model by Rizkalla and Lefebvre, this model leverages theoretical insights and incorporates a range of interacting variables, enhancing its applicability and accuracy. Spearman correlation analysis reveals that air pressure and the air pressure drop ratio significantly negatively impact droplet size, whereas the fuel–air ratio (FAR) shows a positive correlation. Experimental validation at ambient conditions shows that the model is applicable with a reliability threshold of We₁/Re₁ ≥ 0.13 and highlights the predominance of the pressure swirl mechanism over aerodynamic atomization at higher fuel flow rates (q > 1.25 kg/h). This research effectively bridges theoretical and practical perspectives, offering critical insights for the optimization of airblast nozzle design.

The accurate prediction of blade loading is crucial to designing efficient turbomachinery, but traditional methods often neglect the impact of compressibility, leading to inaccuracies at high speeds. This study investigates the effect of compressibility on the blade loading parameters, particularly the Zweifel coefficient (Z_w) and lift coefficient (C_L), in turbine cascades. A novel intermediate method (IM), with averaged flow properties derived from both inlet and outlet conditions, is proposed to enhance the accuracy of Z_w and C_L calculations in compressible flow regimes. This method is based on the extended Kutta–Joukowski theorem for compressible flow and incorporates the Mach number directly into the modified definitions of Z_w and C_L. The analysis reveals that the averaged flow angle (α_m), calculated by using a velocity-weighted approach, serves as a crucial parameter for blade similarity studies. The proposed correction method is applied and validated based on CFD simulations of the VKI-RG turbine cascade. The IM and modified definitions provide a robust framework for accurately predicting blade loading at high speeds, enabling improved design and analysis of turbomachinery.