Based on a type of three-dimensional slice model of a brush seal combined with the commercial CFD software FLUENT, the study calculated the leakage flow of the brush seal. The aerodynamic forces applied on upstream and downstream bristles are analyzed and reduced to a smaller amount of point forces for analysis convenience. The frictional coefficient between the bristle material Haynes 25 and rotor material 1Cr14Mn14Ni are tested. Tip forces including normal reaction and frictional forces caused by aerodynamic forces are quantitatively investigated under conditions with and without frictions using the torque balance principle and nonlinear beam theory (by ANSYS simulations), respectively. Torques, frictional heats, and the temperature distributions of the rotor and bristle pack are studied further. Details and characteristics of the flow and temperature distributions inside the bristle pack are presented. In the experiments, besides traditional tests, such as leakage and torque tests, an infrared camera is employed to capture temperature distributions at the interface of the rotor, bristle pack and nearby zones under various pressure differentials and rotation speeds. The three-dimensional slice model is firstly verified by calculating the leakages, torques and temperature distributions of the brush seal and confirmed via experimentation. The influence of various frictional coefficients and pressure differentials on tip forces, torque and temperature distributions are also examined.

The investigation of the failure envelope of control moment gyroscopes (CMGs) under simulated dynamic and thermal vacuum conditions on the ground is crucial for ensuring the reliability of CMGs. In this study, a test rig is employed to simulate the combined stresses in a vacuum environment, including the temperature, CMG gimbal rotating speed, and satellite rotating speed. The objective is to obtain high-fidelity running status data of the CMG. The particle swarm optimization and BP neural network (PSO-BP) model is utilized to learn from these test data and subsequently predict the running status for other stress combinations, ultimately enabling the determination of the failure envelope surface of the CMG. The results demonstrate that this method significantly reduces the cost of testing to detect the CMG failure envelope while achieving high prediction accuracy and adaptability under combined stress situations. By employing the k-fold cross-validation method, the PSO-BP model demonstrates superior generalization performance to the BP model in predicting the running status of CMGs. The optimized hidden layer size and learning rate of the PSO-BP model are also discussed. Furthermore, the method presented in this paper can effectively incorporate the hidden experience data from the test. Finally, the PSO-BP method, in comparison to the BP method, yields a smooth and stable initial predicted value, effectively preventing the prediction result from falling into local optimization.

In order to accurately obtain the contact stiffness of rough joint surfaces machined by turning and grinding, a research simulation is carried out by using the finite element method. Based on the surface modeling method under the combined machining mode, the three-dimensional (3D) solid model is constructed. Then, the finite element results of the normal contact stiffness were obtained through contact analysis. The comparative analysis was carried out with the analytical results of the KE model and the experimental results. The comparison results show that three results have the same trend of change. However, the maximum relative error of the finite element results is 6.03%, while that of the analytical results for the KE model is 60.07%. After that, the finite element results under different machining parameters are compared. The normal contact stiffness increases with the increase in the turning tool arc radius, grinding depth, and fractal dimension, but decreases with the increase in the turning feed rate and scale coefficient. The rationality of the results is explained by the distribution of the asperities and the contact deformation law of the asperities on the rough surface.

Thermal vacuum and swivel table tests of a control moment gyroscope (CMG) are designed and the start-up hesitation and rotation break of the CMG are analyzed. In order to study the fault mechanism, a thermo-structural coupling simulation was performed on the CMG under the temperature conditions of 60°C and approximately 20°C, as well as the static and dynamic working conditions of the swivel table and gimbal. The simulation results show that the main reason for the start-up hesitation and rotation break of the gimbal is an elliptical deformation in different circumferential positions of the gimbal shell and the gimbal bearing. Under certain combinations of temperature and dynamic conditions, the shrinkage of the radial and axial clearances of the gimbal bearing causes the faults, which is consistent with the test results. The test and simulation results are also helpful in determining the temperature limits of the CMG under different conditions and can help improve their thermal design.

A type of fiber thermocouple is applied to a brush seal in order to obtain the temperature of the bristle-rotor friction zone. Using a brush seal test rig, the temperature measurements utilizing a fiber thermocouple, infrared thermometer, and common thermocouple are compared and studied. The fiber thermocouple is studied under various operational conditions consisting of different pressure and speed variations, rotor-bristle interferences, and eccentricities. Some interesting phenomena and characteristics of brush seals are revealed during these experiments. Some preliminary wear results of a fiber thermocouple are also presented. The results demonstrate the superiority of fiber thermocouples when used in narrow spaces, high temperature, and pressure environments within the heart of an aero-engine.

Fluid loop pumps, critical to spacecraft thermal control subsystems, are more prone to failures than other spacecraft components. Timely fault diagnosis is therefore crucial to ensure operational reliability. This paper proposes a multi-neural network fusion model (MNN) to improve the fault diagnosis accuracy for spacecraft fluid loop pumps. The proposed model integrates four neural network algorithms—back propagation neural network (BPNN), particle swarm optimization-back propagation neural network (PSO-BPNN), genetic algorithm-back propagation neural network (GA-BPNN), and fuzzy neural network (FNN)—through a model scoring and weighting mechanism. Additionally, a dedicated software has also been developed and implemented for the intelligent fault diagnosis of fluid loop pumps in Chinese spacecraft. By analyzing a dataset derived from on-orbit telemetry and expert knowledge, the proposed model demonstrates superior performance over individual models, achieving significant improvements in key metrics such as mean squared error (MSE), prediction stability, correlation coefficient (R), Accuracy, Precision, Recall, and F1-score. Furthermore, validation using both on-orbit telemetry data and ground test data confirms that the model can accurately diagnose both normal operations and various types of faults, making it a reliable and practical tool for on-orbit fault detection. The study provides an efficient, stable, and practical solution for intelligent fault diagnosis of spacecraft fluid loop pumps, with significant engineering application value.