Hydraulically operated solenoid valves, serving as critical actuating components in control systems for nuclear power plants, are consistently subjected to complex environmental stresses such as intense vibrations, extreme temperatures, high humidity, and electromagnetic interference. These conditions impose significant challenges on hydraulically operated solenoid valve reliability and service life. The working principle, structural type, and typical failure forms of solenoid valves were systematically summarized. Current research status and development trends in reliability prediction methodologies were analyzed, with specific focus on modeling approaches, data fusion techniques, and adaptive failure prediction algorithm applications observed in domestic and international studies. Adaptive failure prediction algorithm for solenoid valves was investigated primarily through single working conditions and multi-physics coupling. Secondly, the challenges and problems encountered in the design, prediction, and optimization of solenoid valve reliability within the country in question were further highlighted. Finally, based on the current status and shortcomings of China’s solenoid valve research and development capabilities, relevant suggestions and prospects for the research on solenoid valve reliability prediction technology were put forward.

Erosional damage to sand control screens from sand-laden fluids is a critical issue affecting well integrity and service life. Existing research has predominantly focused on screen plugging conditions, while the erosion mechanism under unplugged conditions remains poorly understood. Laboratory experiments, numerical simulation, and a multiple regression model was integrated to systematically deconstruct the effects of flow velocity, particle size, and sand flow rate on screen erosion. The experimental results indicate that screen mass loss increases with all three factors, with the influence of flow velocity being the most significant and exhibiting an accelerating trend. Numerical simulations revealed that erosion is concentrated in narrow flow passages, such as screen slots, which suffer severe scouring due to localized flow acceleration. The regression analysis quantified the contribution of each factor: flow velocity had the highest impact at 56.1%, followed by sand flow rate at 35.7%, and particle size having the lowest at 8.2%. The study confirms that flow velocity is the primary controlling factor for erosion in unplugged screens and should be given special consideration in sand control operations. The findings provide an experimental basis and theoretical reference for the optimization of sand control measures and screen structural design.

To address the steering performance loss resulting from the inability to maintain preset magnitude and direction of guiding force due to single-wing-rib failure in directional drilling tools, a steering performance optimization model was developed for single-rib failure scenarios. A periodic guiding force-borehole trajectory model was established, along with trajectory calculation method under unsteady guiding forces. A minimization model of trajectory endpoint deviation within sleeve rotation cycles was developed, accompanied by a guiding performance evaluation criterion for unsteady guiding conditions. An improved sparrow search algorithm(ISSA) dynamically adjusted thrusts of functional ribs, effectively enhanced guiding force efficiency and minimizied trajectory deviations during sleeve rotation cycles. Results show that the model essentially eliminates trajectory deviations and fully restores steering performance under trip-free conditions when the guiding force is ≤50% of the maximum value. Beyond this threshold, it still significantly mitigates performance loss, but the mitigation effect gradually diminishes with increasing guiding force. This optimization model effectively reduces the steering performance loss of drilling tools under single-wing-rib failure conditions and demonstrates significant engineering application value.

In order to identify the core demand elements of smart desk users, the mapping relationship between product design elements and user demand perception was explored,which provides a scientific basis for the design optimization of smart desks. A total of 30 015 online reviews of smart desks were collected from Taobao and Jingdong platforms. After data cleaning and word segmentation, the latent dirichlet allocation (LDA) model was applied to cluster user needs into four major categories and thirty-two subcategories. These demand categories were treated as exogenous latent variables and the subcategories as observed variables. Questionnaire data were collected using a five-point Likert scale, and a hybrid model combining structural equation modeling and artificial neural networks (SEM-ANN) was established. The SEM analysis indicated that the positive effect of “experience of use” on “satisfaction” was the most significant. The R² values of the SEM-ANN model increased by about 0.15~0.18 compared with the SEM model, while the RMSE(root mean square error)decreased significantly, showing enhanced nonlinear fitting ability. The comprehensive weight analysis identified odor and environmental friendliness, surface touch, lift safety, and self-assembly installation as the key design optimization points. By integrating online review mining with SEM-ANN hybrid modeling, the mapping between micro design elements and macro user perception of smart lifting desks was revealed. The proposed method provides a prioritized improvement list for design optimization and can be extended to user requirement analysis of other smart hardware and complex consumer products.

To investigate the damage evolution mechanism of fractured sandstone under freeze-thaw cycles, an integrated research framework combining numerical simulation, machine learning, and decision logic analysis & interaction effect analysis was established. Numerical models were constructed using PFC software, through which freeze-thaw cycle tests and uniaxial compression simulations were conducted. A deconstruction formulation was developed to isolate the contributions of freeze-thaw damage and pre-existing fissures. The XGBoost algorithm was employed to build a multi-objective prediction model achieving a test set R² exceeding 0.97, while the SHAP method was applied to interpret decision logic and variable interactions. Results indicate a transition from brittle to ductile failure modes under freeze-thaw action, with mechanical property deterioration rates gradually decelerating and showing damage accumulation saturation. The micro-macro chain damage evolution mechanism is summarized as frost heaving forces driving particle bond degradation, triggering crack propagation and force chain network failure. The cumulative effect of this progressive damage manifests macroscopically as a significant reduction in load-bearing capacity. SHAP analysis further reveals three interaction effects on compressive strength: synergistic deterioration between freeze-thaw cycles and upper temperature/fissure length/fissure thickness; synergistic compensation between cycle count and high dip angles (>45°); and antagonistic effects between cycles and fissure quantity. Finally, an integrated XGBoost-SHAP platform was developed, providing an intelligent tool for rock mass stability assessment in cold regions.

To explore the evolution law of mechanical properties of disintegrating mudstone under dry-wet cycles, the disintegrating mudstone in the northern part of Guangxi was taken as the research object. Uniaxial compression tests and scanning electron microscopy tests at the same position were carried out under different dry-wet cycle numbers. The deterioration law of physical and mechanical parameters, energy evolution law and microstructure change characteristics of mudstone under different dry-wet cycle numbers were analyzed, and a statistical damage constitutive model considering the residual strength of mudstone was established. The results show these as follows. After 5 cycles, the mass loss rate of mudstone reaches 12.9%, the longitudinal wave velocity, uniaxial compressive strength and elastic modulus decrease by 77.9%, 82.1% and 76.2% respectively, and the failure mode changes from shear failure to ductile failure with multiple shear bands. Microstructure analysis shows that the cementation material softens and fractures due to the water-hydration-drying cycle, and the development of micro-pores and particle shedding in mudstone are positively correlated with the number of dry-wet cycles. Based on the energy dissipation evolution law, a four-stage damage development model is proposed, revealing that the elastic strain energy storage capacity of mudstone decreases exponentially with the number of dry-wet cycles. Based on the Weibull distribution and the maximum tensile strain criterion, a damage statistical model considering the residual strength correction is constructed, achieving high-precision characterization of the post-peak residual strength stage (R²>0.9). The research results provide a theoretical basis for the long-term stability study of disintegrating mudstone slopes in the northern part of Guangxi.

Grouted mixture exhibits excellent anti-rutting performance, but its low-temperature crack resistance is inadequate, and the influence of the interfacial contact parameters between the aggregate skeleton and the grouting material on the crack resistance is not yet clear. To clarify the low-temperature fracture characteristics of grouted mixture and the influence of the interfacial micromechanical parameters between the aggregate skeleton and the grouting material on crack resistance, a discrete element numerical simulation model of grouted mixture was established using discrete element method. The micromechanical parameters were calibrated through real low-temperature bending tests. The interfacial failure mechanisms and characteristics during the fracture process were analyzed. Subsequently, the interfacial micromechanical contact parameters between aggregate skeleton-aggregate skeleton or aggregate skeleton-grouting material were adjusted to investigate the changes in crack resistance performance indicators of grouted mixture under different parameter conditions. Results indicate that the majority of cracks in grouted mixture are tensile cracks (accounting for 87.0%), predominantly occurring at the interface between aggregate skeleton and grouting material. Reducing the bond elastic modulus both between aggregate skeleton and between the aggregate skeleton and grouting material, increasing the interfacial bond tensile strength between the aggregate skeleton and grouting material, can enhance the crack resistance of the grouted mixture. However, reducing the bond elastic modulus leads to an increase in the number of microcracks within the grouted mixture. Among these parameters, the interfacial bond tensile strength between aggregate skeleton and grouting material has the most significant impact on crack resistance. When the interfacial bond tensile strength is increased by 50.0%, the flexural tensile strength of grouted mixture increases by 25.1%, the maximum flexural tensile strain increases by 29.3%, and the number of cracks decreases by 20.0%. The study provides a reference for optimizing the crack resistance performance of grouted mixture.

A reliability analysis method based on an improved dynamic Bayesian network was proposed to address the reliability issues of aircraft braking systems. Firstly, within the framework of dynamic Bayesian networks, combining dynamic logic gates with an improved conditional probability table modeling method, the failure rate of nodes was dynamically adjusted by introducing interference factors to determine the conditional probability table and marginal probability table of each node. Secondly, based on the dynamic operating characteristics of the aircraft braking system, a dynamic Bayesian network model of its normal braking system was constructed, and the reliability functions of each module component were derived based on the dynamic Bayesian network inference algorithm. Finally, the method was validated using simulated data generated by the Monte Carlo method. By comparing the reliability curve changes of each subsystem module before and after introducing interference factors, and combining the reverse inference ability of dynamic Bayesian networks, the posterior probability distribution of each module unit was analyzed to achieve system fault assessment. The experimental results show that the proposed method can effectively characterize the dynamic characteristics of aircraft brake systems, identify potential fault hazards, and provide theoretical support for the diagnosis and maintenance strategy formulation of normal brake systems.