Fan nozzle is an important part of high pressure water descaling system, and its internal structure parameters directly affect the performance of jet. The nozzle flow field was analyzed by using fluid simulation software FLUENT, and the nozzle exit diameter, cone section contraction angle and exit contraction angle were selected as reference factors. The jet impact force was used as evaluation index, and the nozzle structure parameters were optimized by response surface method. The results show that the velocity distribution of the internal flow field is affected by the single factor of the diameter of the outlet section and the conical contraction angle, but the flow rate of the nozzle is little affected. The pressure drop of nozzle is affected by the change of the diameter of outlet section and the contraction angle of nozzle outlet, and the influence of the contraction angle of nozzle conical section on the pressure drop is negligible. When the diameter of the outlet section is 3.15 mm, the taper shrinking angle is 26.17°, and the outlet shrinking angle is 40.93°, it is found that the nozzle striking force is increased from the original 94.91 N to the optimized 143 N by establishing the outflow field and applying the gas-liquid two-phase flow model simulation calculation. The research results provide theoretical guidance for optimizing nozzle structure to enhance jet impact force.

In order to solve the technical problem of insufficient reliability and life of rotary seal under the coupling effect of high pressure and high speed and medium corrosion, experimental research on rotary lip seal was carried out, a high-speed rotary lip seal test bed with pressure was designed and built, leakage and friction torque characteristic tests of lip seal were carried out, simulation calculation of lip seal was carried out, and a new type of seal with low friction coating was innovatively proposed. The temperature rise and torque comparison test of traditional seal and spraying new seal at different speeds were carried out. The experimental results show that the lip spraying tetrafluoroethylene (PTFE) material can effectively reduce the temperature rise of the seal. When the rotational speed increases by 1 000 r/min, after reaching the heat balance temperature, the temperature rise of the spray lip seal is 2.4 °C lower than that of the traditional lip seal. Under the pressure condition, the temperature of the coated lip seal is about 3.48 °C lower than that of the traditional lip seal. Under the condition of high speed and poor lubrication, the temperature of the spray lip seal is about 4.61 °C lower than that of the traditional lip seal, but the sealing torque increases by about 0.03 N·m. The sealing performance is evaluated from two aspects of sealing temperature rise and sealing friction torque, which provides theoretical support for solving the technical problems of high parameter rotary seal.

Quality distribution detection is regarded as a crucial basis for regulating the rare earth extraction process. The variation of the lanthanum (La), cerium (Ce), praseodymium (Pr), and neodymium (Nd) solution system during the saponified P507-kerosene extraction is significant, showing obvious nonlinear characteristics such as time-varying and strong coupling. Moreover, the solution system exhibits no apparent color change. Existing soft measurement methods based on machine vision and artificial neural networks have shown unsatisfactory application effects. To address these issues, a mechanistic modeling method was first employed to construct a mathematical model for the quantitative analysis of the four components. Subsequently, by measuring the characteristic gamma-ray intensity information of the natural radioactive isotope ¹³⁸La@1.436 MeV (auxiliary variable), the mass distribution information of the four components (main variable) was predicted simultaneously. Finally, experiments were conducted based on a high-purity germanium detector. The results show that the minimum detectable mass concentration of La is 1.70 g/L. Compared with the experimental results, the relative errors of the predicted mass concentrations of La, Ce, Pr, and Nd are -3.11%~4.23%, -5.81%~3.74%, -8.16%~6.68%, and -19.87%~14.20%, respectively. The relative errors of the sensitivity prediction are 1.33%, 5.56%, -3.20%, and -0.41%, respectively. The proposed “gamma ray-soft measurement” method enables high-accuracy and high-sensitivity prediction of the four components La, Ce, Pr, and Nd. The detection process is not affected by changes in extraction system density, acidity, temperature, and other parameters, providing new technical references for rare earth extraction quality distribution detection.

At present, there are fewer studies on the macroscopic mechanical hysteresis model of magnetically controlled smart magnetorheological elastomer (MRE), which is not conducive to the application control of MRE materials. To improve the control effect of MRE materials, an experimental study on MRE’s magnetostrophic shear mechanical properties based on the Bouc-Wen model was conducted. The influence of magnetic field strength on the mechanical parameters of MRE was analyzed. Firstly, with the assistance of Simulink to establish the Bouc-Wen simulation model was established for MRE parameter fitting. Then, the stiffness, damping, and other material parameters were analyzed with the magnetic field strength change rule. Finally, through the experiments, the model validity was examined. The results show that with the increase of magnetic field strength, the parameters of MRE energy storage and energy consumption have different degrees of increase, in which the equivalent stiffness and maximum damping force increase significantly, respectively, increase 210.61%, 205.41%. In the range of 0.5~0.7 T magnetic field parameter growth rate is faster in the range of 0.7~1.0 T growth tends to be saturated. The dynamic mechanical properties of MRE are better described by the Bouc-Wen model, and the maximum error of the characteristic parameters is 4.42%. The research results can provide theoretical and experimental references for the optimal preparation and algorithmic control of MRE materials.

The rapid development of new energy vehicles has made the industry’s requirements for high-performance automotive motors continue to increase. Hybrid excitation motor is a new type of motor. The hybrid excitation motor combines the advantages of permanent magnet excitation and electric excitation, and has excellent regulation and reliability. A parallel claw-pole hybrid excitation motor structure was proposed. The magnetic circuit of the hybrid excitation motor was optimized by means of parallel axial excitation of permanent magnet and electric excitation device. On this basis, the parametric simulation of the thickness of the claw pole side plate and the large end of the excitation bracket of the motor was carried out respectively, and the average torque of the motor before and after optimization was simulated according to the optimal structure. Finally, the mechanical characteristic curves of the parallel claw-pole hybrid excitation motor and the permanent magnet synchronous motor were compared and analyzed. The results show that the parallel claw-pole hybrid excitation motor broadens the operating range of the motor by 17.24% on the basis of maintaining the torque performance, and improves the mechanical properties and adaptability of the hybrid excitation motor.

In order to mitigate the impact of wind power fluctuations on the power grid, a hybrid energy storage system (HESS) control strategy was proposed, which optimized the parameters of variational mode decomposition (VMD) using the mayfly algorithm (MA). Firstly, the sliding average algorithm was employed to determine the wind power grid connected power that met the grid standards. Then, a fitness function was constructed by combining two evaluation criteria, and the optimal parameters of the VMD algorithm were determined using the mayfly algorithm. The optimal parameters were then introduced into the VMD algorithm to decompose the hybrid energy storage power, realizing the initial allocation of the hybrid energy storage power. Finally, fuzzy control was utilized to optimize the state of charge (SOC) of the energy storage devices, adjusting the power commands of supercapacitors and lithium batteries. The results indicate that the proposed strategy not only enables adaptive decomposition and rational allocation of hybrid energy storage power, effectively mitigating wind power fluctuations but also ensures the SOC of the energy storage devices remains within a reasonable range, achieving safe and stable operation of the HESS.

The stability of power cyber physical system(CPS) is easily affected by stochastic uncertainty from both the information and physical sides. A stability analysis method for power CPS based on stochastic uncertainty model and a robust wide area feedback frequency control method were proposed. Taking into account the essential differences between the discreteness of the information side and the continuity of the physical side, as well as the mutual influence in terms of functionality, a dynamic model of power CPS in stochastic environment was established from both the information and physical sides. According to the definition of mean square exponential stability of stochastic differential equations, small signal stability analysis was conducted for dynamic model of power CPS. The critical variance based on mean square norm calculation was used to describe the impact of stochastic uncertainty on small signal stability of power CPS. Focusing on the norm optimization problem based on linear matrix inequality constraints, a robust wide area feedback controller was designed based on the distributed control method. Finally, simulation analysis was conducted on an IEEE 39 bus system, and the results verified the correctness and effectiveness of the proposed method.

Dynamic graph link prediction aims to predict the formation or disappearance of links between nodes in a graph based on their historical interactions. To address the issue of high energy consumption associated with modeling dynamic networks using recurrent neural networks at fine-grained temporal graphs, a dynamic graph link prediction model optimized by spiking neural networks was proposed. By the node memory updater incorporated spiking neural networks and the spiking update process of node memory, the evolving dynamics of dynamic graphs were learned by graph neural networks and the model achieved link prediction. The results on three publicly available classic datasets show that the proposed model exhibits improved runtime efficiency while maintaining accuracy, showcasing favorable performance in dynamic graph link prediction tasks.

In the contemporary digital healthcare setting, the dissemination and sharing of medical imagery are integral to routine medical operations. However, medical images often contain sensitive patient information, and without adequate protection, there is a risk of illegal acquisition or leakage, which brings unnecessary troubles. To address this issue, an encryption algorithm based on Zigzag scrambling and a new four-dimensional hyperchaotic system was proposed. Firstly, the Zigzag algorithm was used to scramble the image once, roughly hiding the obvious contours of the image. Then, an improved cat mapping algorithm was used to perform secondary scrambling on the image, removing obvious texture features. Finally, the scrambling factor generated from the plaintext image was applied to the initial value generation process of the hyperchaotic system. The generated hyperchaotic sequence was transformed into a hyperchaotic matrix for the subsequent diffusion process of the encryption algorithm. The simulation results show that the proposed algorithm can effectively conceal plaintext information based on the characteristics of medical images and resist common types of attacks. The robustness of the proposed algorithm has been demonstrated through testing, confirming its capability to address the issue of image interference in remote healthcare.

To solve the problem of insufficient extraction of sport features by dual stream networks in current action recognition, which leads to low recognition accuracy, a action recognition method based on sport feature enhancement two-stream networks was proposed to improve accuracy. The network was divided into spatial stream and temporal stream, with the same structure but different inputs. The input of the spatial stream network was a video frame sequence, while the input of the temporal stream network was a video frame difference sequence. The network structure used Resnet50 as the backbone network, replacing the 3×3 convolution with the proposed global sport feature module and local sport feature module, fully extracting video sport information, and finally combining spatial and temporal stream to output the results. The results show that the accuracy of the model on the UCF101 and HMDB51 datasets reaches 96.8% and 75.3%, which is superior to traditional algorithms.