Turbine tip clearance of aero-engine is important to engine performance. Proper control of rotor tip clearance contributes to engine efficiency improvement and fuel consumption reduction. Therefore, accurate tip clearance measurement is essential. The inductive measurement method is one of the non-contact distance measurement methods, which has the characteristics of high sensitivity, fast response speed and strong anti-interference ability. Based on the principle of inductive sensor measuring tip clearance, the ambient temperature change will cause the material electromagnetic performance change for the conductivity and permeability varies with temperature. The calibration experiment was conducted to obtain the sensor resolution and sensing range. The effect of temperature on sensor parameters was extracted from high temperature experiment data. Results show the resolution of planar coil made of platinum wire can be 10 μm and the maximum sensing range can reach 5 mm. At temperature from 500 ℃ to 1100 ℃, coil inductance almost does not change with temperature while coil resistance varies exponentially with temperature, that means the coil inductance variation can reflect the tip clearance change and resistance can indicate the measuring temperature.

Oil/air two-phase flow distribution in the bearings is the basis for bearing lubrication status identification and precise thermal analysis of the bearing. In order to understand the fluid behavior inside the under-race lubrication ball bearing and obtain an accurate oil volume fraction prediction model. A numerical study of ball bearing with under-race lubrication is carried out to study oil-gas two-phase distribution inside the bearing, and the influence of several parameters is quantified, like bearing rotating speed, oil flow rate, oil viscosity, and oil density. The results indicate that the oil fraction in the bearing cavity between the inner and outer ring shows a periodic distribution along the circumference direction, and the period is the same as the number of under-race oil supply holes. Oil distribution alone radial direction is affected by the outer-ring-guiding cage and centrifugal force, leading to oil accumulation near the outer ring. Different bearing running conditions and oil characteristics do not change the oil distribution trend alone in circumference and radial direction, but the difference ratio. Finally, based on the numerical simulation results, a formula for the average oil volume fraction prediction in the bearing ring cavity is constructed.

Turbine tip clearance of aero-engine is important to engine performance. Proper tip clearance can reduce the gas leakage over turbine blade tips and improve the engine efficiency of turbo machinery and reduce the fuel consumption. Therefore, accurate tip clearance measurement is essential. The inductive measurement method is one of the non-contact distance measurement methods, which has the characteristics of high sensitivity, fast response speed, and strong anti-interference ability. Based on the principle of inductive sensor measuring tip clearance, the ambient temperature change may cause the material electromagnetic performance change for the conductivity and permeability varies with temperature. In order to verify the temperature effect on the sensor performance, the repeated calibration experiments were carried out to obtain the sensor repeatability error of 5.4%. Then, the sensor was calibrated in the range of 0 mm–4 mm clearance at temperature from 600 °C to 1000 °C and obtained the measurement error of 4.6%. Results indicate when the temperature ranged from 600 °C to 1000 °C, clearance measurement error is smaller than the sensor repeatability error so the temperature effect on the sensor characteristics can be ignored. This conclusion makes the sensor promising for monitoring the blade tip clearances at various temperature environment.

The increasing rotational speed and temperature levels in modern aeroengines impose higher demands on the lubrication and cooling design of the roller bearings supporting the main shaft. To design a compact and efficient under-race lubrication system, a comprehensive understanding of the characteristics of oil–air two-phase flow and the variations in oil capture efficiency is necessary. This study experimentally investigated the oil capture efficiency and employed numerical simulations to elucidate the mechanisms of oil–air two-phase flow. The results reveal a favorable consistency between the numerical simulations and experimental findings, both in terms of the oil capture efficiency and the oil–air distribution. The decreasing trend of oil capture efficiency gradually stabilizes when the jet distance exceeds 10 mm. To ensure stable and effective testing results for oil capture efficiency, the jet distance should be maintained within the range of 10–14 mm during experiments. Under different operating conditions, there exists an optimal jet angle that maximizes the oil capture efficiency. The corresponding optimal jet angle becomes smaller with lower oil supply pressure and higher rotational speed of the radial oil scoop. Within a momentum flux ratio range of 5–870, an innovative predictive correlation for the optimal jet angle is derived, with a maximum relative error between predicted and experimental values being 4.0%. The proposed correlation provides theoretical support and methodological basis for refining the design of radial under-race lubrication in aeroengines.

To understand the variations in pressure drop and heat transfer characteristics within the scavenge pipe of aero-engines, studying and attempting to discriminate the flow patterns of two-phase flow inside the scavenge pipe is of great significance. To achieve this, this paper establishes a flow pattern identification model. High-speed photography was utilized to capture images of four distinct flow patterns inside the scavenge pipe under typical operating conditions. Through image preprocessing, feature extraction, and Relief-F feature selection, the primary texture and shape features are obtained as inputs for the identification model. Four machine learning methods, namely unsupervised learning K-means, supervised learning backpropagation neural network (BP), radial basis function neural network (RBF), and support vector machine (SVM), are selected for flow pattern identification. For the optimization of hyperparameters in supervised learning methods, this paper utilizes the particle swarm optimization (PSO) algorithm. Consequently, PSO-BP, PSO-RBF, and PSO-SVM models are further established. After inputting the two types of features, texture and shape, into the mentioned models, a comparison of the classification accuracy and generalization ability of the four models is conducted. The results indicate that, for the flow pattern identification problem of oil–air two-phase flow inside the scavenge pipe studied in this paper, the most suitable identification model is the PSO-SVM model.

To study the influence of orifice spacing on the oil–air two-phase flow and the oil capture efficiency of an oil scoop in an under-race lubrication system, an experimental platform for under-race lubrication was built, and a calculation model for the oil–air two-phase flow field was established. The rationality of the experiment and the validity of the numerical model were verified by comparing the experimental and numerical results. The results showed that under the same oil supply pressure, the captured oil mass flow rate of the double-orifice structure was much higher than that of the single-orifice structure, though it was still less than twice that of the single-orifice structure. When applying a tandem layout structure of double orifices to an under-race lubrication system, the orifice spacing of the tandem layout structure should be determined based on a full evaluation of the influence of the orifice spacing and working condition parameters on the oil capture performance. Otherwise, it may lead to a decrease in oil capture efficiency, with the maximum reduction even reaching 12%.