With the widespread application of high-speed motors, electro-hydrostatic actuator impose high-speed requirements on piston pumps. We focus on a 60 ml/r swashplate variable-displacement axial piston pump and conduct a high-speed redesign. We address issues such as large pressure pulsations in the piston chambers, poor suction performance, and severe cavitation at high rotational speeds. An integrated booster impeller is designed inside the pump to increase the suction pressure of the piston chambers and to solve the insufficient oil intake problem. A pre-depressurization chamber is added to reduce negative pressure overshoot during high-to-low pressure transitions thereby mitigating cavitation risks. The port plate relief grooves are optimized by configuring the pressurization zone with damping holes and triangular grooves and the depressurization zone with damping holes and U-shaped grooves. The optimization of the port plate damping grooves reduces pressure pulsation during pressure transitions. The proposed design is validated through CFD flow-field simulations. A prototype of the high-speed piston pump is developed and experimentally tested. Testing results show good agreement with the simulation results. The prototype reaches a maximum rotational speed of 5000 r/min, and its volumetric efficiency exceeds 96% under load pressures of 5~15 MPa within the speed range of 1000~5000 r/min.

In axial piston pumps, fluctuations in operating conditions can cause the oil film to break down, leading to abrasive wear, adhesive wear, and even disc burning, which severely affects equipment reliability and service life. To improve their tribological properties, five groups of composite nano-lubricants are prepared using No.15 aviation hydraulic oil as the base oil, with hexagonal boron nitride (h-BN) and silicon dioxide (SiO₂) nanoparticles modified by dehydrated sorbitan monooleate and hexadecyltrimethoxysilane, respectively. The success of the modification is confirmed using scanning electron microscopy. Fourier-transform infrared spectroscopy, and Raman spectroscopy, which also demonstrates a significant improvement in the dispersion stability of the nano-additives within the lubricant. Tribological tests using high-speed steel and tin bronze as friction pairs show that, compared to the base oil, the lubricant containing the modified h-BN/SiO₂ composite nano-additives exhibits significantly reduced friction coefficients and wear rates. Further analysis using scanning electron microscopy, optical profilometry, and X-ray photoelectron spectroscopy reveals the mechanism by which the modified nanoparticles enhance the anti-friction and anti-wear performance of the distribution plate through the formation of a stable lubricating transfer film. This provides an effective strategy and theoretical basis for the development of novel nano-lubricants for high-performance hydraulic components.

Water-glycol, an eco-friendly and flame-retardant hydraulic fluid, sees growing use in special working conditions. However, traditional oil-based hydraulic locks show poor compatibility with it. We theoretically investigate a dedicated bidirectional hydraulic lock for water-glycol media. It uses AMESim to simulate viscosity effects (0~80 mPa·s at 20 ℃, 0%~100% water content) on its performance. For water-glycol with 0%~75% water content, flow capacity correlates negatively with viscosity. Pressure loss exhibits distinct direction dependence and viscosity sensitivity. Leakage increases with rising water content. Pure water (100% water content) behaves differently from water-glycol mixtures. The designed hydraulic lock shows good compatibility in the target viscosity range. It provides theoretical support for water-glycol hydraulic systems in deep-sea operations and lays a foundation for efficiency optimization under multi-viscosity fluid conditions.

To address the problems of scarce fault samples and insufficient cross-condition generalization in hydraulic pump fault diagnosis, we propose a meta-learning-based few-shot cross-domain adaptation method. First, different pressure-flow combinations are defined as operating-condition domains. Then, a multi-scale one-dimensional convolutional neural network is employed as the feature extractor to learn highly discriminative multi-scale features directly from raw vibration signals. During meta-training, the bi-level optimization mechanism of model-agnostic meta-learning enables the model to rapidly adapt to unseen operating conditions using only a few support samples. To further improve robustness, a dual-objective loss function is constructed by combining a label-smoothed classification loss with an L2 regularization term, thereby alleviating overfitting and enhancing cross-domain stability. Experiments are conducted on two unseen target operating-condition domains with multi-episodic evaluation. After five adaptation steps, the proposed method achieves average accuracies of 98.90% and 96.80%, respectively. Compared with existing methods, the proposed method reduces dependence on training samples while maintaining high diagnostic accuracy, demonstrating its effectiveness and practical potential in few-shot cross-condition fault classification.

Underwater actuators are key drive units for deep-sea equipment. The energy efficiency of underwater actuators directly affects the endurance and operating costs of deep-sea equipment. Asymmetric underwater electro-hydrostatic actuators show great potential in deep-sea equipment due to their high power density, large output force, and compact structure. A dual-displacement design improves energy efficiency under wide-range load variations. However, displacement switching may cause pressure fluctuation and reduce motion control performance. To address this problem, this paper proposes a nonlinear deterministic robust control method for an asymmetric dual-displacement underwater electro-hydrostatic actuator. The method employs a two-layer control structure. The outer layer switches pump displacement according to load identification. The inner loop regulates motor speed in a closed loop. The pump operates in the large-displacement mode during the high-speed and low-pressure stage, whereas it switches to the small-displacement mode during the high-pressure and low-speed stage to reduce energy consumption. Based on the system model, a nonlinear robust feedback motion controller is designed to improve energy efficiency and maintain accurate trajectory tracking. Simulation results show that the proposed method achieves higher trajectory tracking accuracy than conventional proportional-integral control under cyclic high low-pressure operating conditions.

To address the insufficient fatigue life of polytetrafluoroethylene diaphragms in ultra-clean valves for semiconductor manufacturing equipment, we combine experimental characterization with molecular dynamics simulations to systematically reveal the fatigue rupture mechanism and structural evolution during the forming process. Scanning electron microscope and small-angle X-ray scattering in-situ observations elucidate the entire process of silver streak's initiation, propagation, and fracture under cyclic loading, as well as the coupled failure mechanism with shear bands. Molecular dynamics simulations construct polytetrafluoroethylene models with different molecular weights to investigate molecular chain motion, crystallinity, and entanglement degree during thermal decomposition, cooling crystallization, and uniaxial tension. The results establish the relationship between microstructure and macroscopic fatigue. These findings provide a theoretical basis for improving diaphragm fatigue life through microstructure regulation via forming processes and hold significant engineering value for advancing high-performance ultra-clean fluidic components.

To address the problems of end-face uneven wear, sealing failure, and efficiency degradation caused by oil film instability in the piston pair of a swashplate axial piston pump under complex operating conditions, a full-cycle transient hydrodynamic lubrication model considering the eccentric tilting of the piston is established. Through kinematic analysis of the piston, the oil film thickness equation and the Reynolds equation for the piston pair are derived, and a steady-state hydrodynamic lubrication model is developed. The finite difference method combined with the successive over-relaxation iteration method is used to discretize the lubrication model to obtain the oil film pressure field distribution. Based on the dynamic force balance equations, the Newton-Raphson method is applied to solve the model. The variation laws and underlying mechanisms of the minimum oil film thickness field and the peak pressure field of the piston pair during one full revolution of the cylinder block are analyzed. On this basis, the influences of rotational speed, outlet pressure, and inlet pressure on the minimum oil film thickness and the peak pressure field of the piston pair are investigated. The simulation results show that the hydrodynamic moment is dominant during the delivery stroke, where the oil film thickness distribution is uniform. The overturning moment is dominant during the suction stroke, where the oil film thickness reaches a minimum value of 0.42 μm at a cylinder block rotation angle of 310°, and the peak pressure decreases from 83 MPa to 10 MPa, resulting in deteriorated lubrication that is liable to cause wear and sealing failure. Increasing in both rotational speed and outlet pressure leads to deterioration of the lubrication state: the minimum oil film thickness decreases from 0.49 μm to 0.33 μm and from 0.42 μm to 0.29 μm respectively, while the peak pressure increases from 68 MPa to 120 MPa and from 83 MPa to 130 MPa respectively. The decrease in oil film thickness and the increase in pressure jointly increase the risk of contact uneven wear and the fatigue load on the bushing, thereby reducing sealing reliability. The increase in inlet pressure causes the peak pressure to rise from 80 MPa to 86 MPa; the minimum oil film thickness exhibits a slight overall decreasing trend, but it rebounds during the middle and late stages of the delivery stroke. An appropriate increase in inlet pressure can improve the oil film support and sealing performance.

To address the hysteresis and slow response often observed in conventional solenoid-driven pneumatic proportional valves during dynamic pressure regulation, a pilot-operated pneumatic proportional valve driven by a voice coil motor is proposed. A coupled electro-magneto-mechanical-pneumatic dynamic model is established to characterize spool motion, chamber pressure variation, and throttling flow characteristics. Key model parameters are determined through structural dimensions and experimental calibration. The Stribeck friction model is introduced to describe the nonlinear friction of the spool. A co-simulation platform is built in AMESim to reproduce the dynamic hysteresis. To quantify the dynamic process, dead time and response hysteresis time are defined as evaluation indices. Sensitivity analysis is conducted to identify critical structural parameters. A hybrid optimization strategy combining global search via genetic algorithm and constrained fine tuning via non-linear programming by quadratic lagrangian is applied to reduce hysteresis and improve dynamic response while satisfying steady-state error constraints. Simulation and experimental comparisons show that the model accurately reflects system dynamic behavior with small errors. After optimization, the dead time decreases by 30.4% under the 0~0.5 MPa pressurization condition and by 33.3% under the 0.5~0 MPa depressurization condition. The proposed model and structural parameter optimization method effectively enhance the dynamic performance of pneumatic proportional valves.

Offshore hydraulic wind turbines are subject to multi-source disturbances from wind, waves, and the power grid, which degrade power control performance. To address this issue, an intelligent anti-disturbance control strategy integrating radial basis function sliding mode control and disturbance-observer-based feedforward compensation is proposed. A nonlinear model of the hydraulic transmission and energy storage system is established to describe the strong coupling and time-varying characteristics of the system. Based on this model, a coordinated control architecture combining "anti-disturbance tracking on the main transmission side" and "fluctuation suppression on the energy storage side" is developed. On the main transmission side, a radial basis function neural network sliding mode controller is designed to adaptively compensate for system uncertainties and achieve accurate power tracking under aerodynamic torque fluctuations. On the energy storage side, a feedback linearization controller based on a nonlinear disturbance observer is introduced to estimate and compensate for grid-side disturbances in real time, thereby suppressing active power fluctuations. Simulation and semi-physical experimental results show that, compared with the dual-layer integral feedback linearization control strategy, the proposed strategy reduces the power tracking settling time to 0.01 s and limits the steady-state error to less than 2%. Under grid voltage sag conditions, the power overshoot is reduced by more than 50%, and the power deviation during fluctuation suppression remains within 0.02 kW. The proposed method provides an effective intelligent control solution for high-performance power regulation of offshore hydraulic wind turbines under complex marine operating conditions.

We propose an active suppression scheme for the longitudinal vibration of wire ropes during hoist operation using a three-chamber hydraulic cylinder. The research object is a mine ground-mounted friction hoist. Considering the time-varying length of the wire rope, a coupled partial differential equation-ordinary differential equation dynamic model of the hoist's longitudinal vibration is established using Hamilton principle. Model order reduction and discretization are achieved through coordinate transformation. The resulting equations are solved numerically with the finite difference method. A floating wheel actuated by a three-chamber hydraulic cylinder serves as the active control actuator. A PD control law with displacement and velocity feedback is designed to actively suppress the system's boundary vibration. Simulation results show that the PD control strategy effectively attenuates the longitudinal distributed vibration displacement of the wire rope. The average vibration amplitude is reduced by more than 89%. The system converges within 43 s. This significantly improves the dynamic performance and safety of the hoisting system. Furthermore, compared with conventional hydraulic cylinders, the three-chamber hydraulic cylinder reduces energy consumption by 32.5% and enables partial energy recovery.

Inconsistent bolt preload and stress relaxation are critical factors causing armature null offset in electro-hydraulic servo valve torque motors. We establish a quantitative link between bolt preload and armature null offset using a joint ANSYS-COMSOL structural-electromagnetic simulation. Firstly, an ANSYS finite element model simulates five bolt relaxation modes to analyze the variation in air gap asymmetry. This deformation data is then imported into a COMSOL magneto-structural coupling model. This model quantifies the armature deflection angle for each case and defines an explicit predictive relationship between the bolt relaxation rate and the deflection angle. The results show that air gap asymmetry depends on the spatial distribution of bolt relaxation. Relaxation of bolts on the same lateral side produces the largest deflection angle. In contrast, simultaneous relaxation of bolts on the lower side or in diagonal positions has a negligible impact on the null position. In these cases, the structural deformations cancel each other in the lateral directions.

To improve the accuracy of valve-controlled cylinder models under the strong nonlinearity of compressed air, we propose a data-driven modeling method for pneumatic systems. The method collects time-series data during system operation and uses cylinder displacement as the prediction target. It builds a feedforward neural network with six hidden layers, uses Smooth L1 Loss as the loss function, and combines the ADAM optimizer with an adaptive learning rate strategy. For comparison, we also develop a traditional physical model of the pneumatic system in Simulink and build an experimental testbed for the valve-controlled cylinder. The study validates displacement prediction under different load masses and supply pressures. The traditional method gives an root mean square error of 0.035 m and a coefficient of determination of 0.9532 for displacement. In contrast, the neural network model reduces the root mean square error to 0.003985 m, and its coefficient of determination exceeds 0.999169. These results show that the data-driven method predicts cylinder displacement much more accurately than the traditional physical model. This study demonstrates the clear advantage of data-driven modeling for high-precision pneumatic system modeling and shows strong potential for engineering applications.

To address hydraulic shock and terminal residual vibration caused by the coupling of valve pressure saturation and mechanical flexible modes, a multi-physics coupling simulation platform integrating mechanical flexibility, permanent magnet synchronous motor electromagnetics, and hydraulic dynamics is constructed. A universal trapezoidal velocity planning is applied to the entire system for flexible start-stop. The motor system utilizes extended state observer-based model predictive control to suppress perturbations, while the hydraulic system employs a robust PI controller for trajectory tracking. Comparative simulations show that this strategy avoids controller saturation, reducing the starting pressure from 16 MPa to a load-matched 3 MPa and eliminating velocity back-rebound. Extended state observer-based model predictive control reduces the current oscillation by 50%. Under full-load, the terminal offset decreases from 88 mm to 50 mm, achieving 43% anti-sway efficiency. The strategy significantly improves stability and operational accuracy in heavy-load scenarios.