In recent years, as an effective positioning method, electromagnetic wave time reversal (EMTR) technology has begun to be applied in the field of partial discharge. Compared with traditional ultra high frequency (UHF) positioning methods, EMTR technology requires only a single sensor, offering significant advantages and promising application prospects. However, the commonly used maximum field strength and minimum entropy criteria struggle to accurately determine the focusing time and position of time reversal in complex structures. To address these problems, this paper proposes an EMTR location method based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm leveraging the distinct characteristics of waveforms at signal and non-signal sources, and verify the location of partial discharges in gas-insulated switchgear (GIS) using CST Studio Suite simulation software. To verify the field feasibility of EMTR, it develops a laboratory model to conduct partial discharge experiments. The results show that the average positioning error of the EMTR method is less than 20 cm, which can realize accurate positioning of partial discharge sources in GIS. Compared with traditional methods, the EMTR method reduces the number of sensors and improves the anti-interference performance, which has certain advantages.

Multiple regional power grids in China have adopted the control performance standard (CPS) to assess the performance of inter-provincial tie-line operations, which has played a significant role in the reduction of both power deviation on inter-provincial tie lines and frequency deviation across the grid. However, in recent years, during peak electricity consumption months, the provinces in recipient regions often face a collective shortage of frequency regulation resources, while inter-provincial electricity spot market prices remain high. The current CPS assessment mechanism, which calculates CPS penalties based on a lower fixed price, has become ineffective as a safe guard for short-term power and frequency balance. Therefore, a master-slave game model between the regional dispatch center and provincial dispatch centers is proposed to determine the CPS assessment price that ensures sufficient reservation of automatic generation control (AGC) adjustable capacity. In this model, the regional dispatch center sets the CPS assessment price, and the provincial dispatch center responds by formulating day-ahead dispatch plan based on this price. The regional dispatch center adjusts the assessment price based on the dispatch optimization results of the provincial center until the adjustable capacity or frequency deviation of the province meets certain requirements. Case study results show that when the assessment price is set at eight times the highest value of various market prices, the assessment mechanism effectively incentivizes provincial dispatch centers to purchase inter-provincial spot electricity and demand response resources in face of generation shortage. This approach enables the system to meet the maximum predicted load and potential forecast errors, thereby maintaining the frequency deviation of the interconnected power grid within acceptable limits.

To address the difficulties in adjusting new energy resources along electrified railways, uneven load distribution, and waste of potential energy in loading and unloading operations after trains arrive at stations, a design optimization strategy is proposed for the energy storage traction system of cross-regional freight railways. A time-sharing zoning electricity price model and an energy storage traction system capacity optimization allocation model are developed to guide load time shifting to achieve peak shaving and valley filling in different time domains and geographical spans by analyzing the spatiotemporal characteristics of mobile energy storage charging and discharging. Then, an energy management strategy is proposed aiming at maximizing the daily operating efficiency. The calculation example verifies that the designed energy storage traction system and its operation strategy can effectively improve the economic benefits during the operation cycle of the train, promote energy consumption along the line, and have important reference value for the low-carbon operation of electrified railways.

In the context of energy transition and “carbon peak and carbon neutrality” goal, active distribution networks in the new power system can achieve scalable energy conservation and emissions reduction by increasing the penetration of renewable energy and leveraging demand-side management. To this end, a two-stage low-carbon energy management strategy for active distribution networks based on dynamic carbon entropy theory is proposed, using carbon price as a price signal to guide flexible loads in participating in low-carbon demand response. First, the carbon entropy model is analyzed, and a carbon entropy model considering energy storage is established to refine the carbon emission characteristics on the demand side. Then, an evaluation index of node carbon potential is proposed to evaluate the cleanliness of carbon emission of the system. Next, a two-stage low-carbon optimization scheduling model is developed for active distribution networks based on the dynamic carbon entropy. By utilizing the time-of-use electricity prices and node carbon prices as guiding signals, the model promotes the renewable energy consumption, reduces system carbon emissions, and achieves a certain peak-shaving and valley-filling effect. Finally, multiple scenarios are set up in the IEEE 33-node system to validate the effectiveness and superiority of the proposed low-carbon energy management model. The results show that the proposed strategy can achieve low-carbon energy management in active distribution networks.

The parameter uncertainty of LCL type grid-connected inverter can significantly affect the power quality of renewable energy, making it essential to analyze the stability of the inverter under the parameter inception. To solve these problems, this paper establishes a state space model of LCL type single-phase grid-connected inverter and proposes a neural network modeling method based on the parameter model. Through the parameter characterization, a training dataset based on the parameter distribution is obtained. This dataset is then trained in the neural network to produce the stability discrimination results. Finally, the effectiveness of the proposed method is verified by MATLAB/Simulink simulation and the experimental platform.

Marine pipelines are widely used in offshore engineering and are highly vulnerable to accidental damage caused by underwater structures such as ship anchors and deep-sea submersibles, especially in the dark and unpredictable marine environment. Research on configuration monitoring of marine pipelines is essential to ensure their operational safety. This paper develops a displacement field inversion model for marine pipelines under the influence of three-dimensional background ocean currents, based on the inverse finite element method. The model consists of an input parameter module, a coordinate conversion module, and a displacement reconstruction function module. It takes into account key characteristics such as large curvature, three-dimensional coupling with large displacements, and local flipping behavior. The proposed approach addresses the technical challenges associated with low-order deformation modes and irregular displacement patterns. The impact of the number and layout of monitoring points on the accuracy of displacement field inversion is studied. The results show that the layout with a monitoring point spacing of 100 m and an angle of 30° can meet the engineering accuracy requirements. The findings of this paper can provide valuable insights and methods for the design of marine pipeline health monitoring systems.

To develop a maneuvering model for a typical unconventional ship, a decoupled modeling approach was adopted as the foundation to construct a numerical calculation based maneuvering model framework. To determine the hydrodynamic derivatives for the maneuvering model, static oblique towing test (OTT) and dynamic circular motion test (CMT) were performed by using numerical simulations to obtain the forces on ship body. To improve the accuracy of nonlinear hydrodynamic derivatives, a hybrid method combining cubic spline interpolation with least squares fitting was proposed. The zigzag and rotation tests of the scaled ship model verified the accuracy of the maneuvering model. The results show that the maneuvering motion model established by numerical calculation and the hybrid method has good accuracy and can be used for modeling unconventional ship maneuvering motion.

The re-entrainment of droplets in the inlet filtration components of marine gas turbines severely affects the gas intake quality and the safe operation of gas turbines. To address this issue, the formation and influence mechanisms of droplet re-entrainment in the wire mesh filter were studied, and a comparative analysis was conducted on the effects of different inlet air velocities and droplet diameters on the liquid film thickness on the surface of the mesh and the mass of re-entrainment. The results show that incoming droplets tend to deposit upstream of the mesh segment and form a liquid film due to velocity gradients and inertia effects. Liquid film stripping is the main form of re-entrainment under marine operating conditions, occurring at a critical inlet air velocity between 4 and 4.5 m/s. The compact arrangement of adjacent wire mesh layers accelerates the airflow, and intensifies shear effect on the liquid film, which in turn increases the stripping film mass. This should be avoided during mesh fabrication. With the increase of the inlet air velocity, the overall thickness of the liquid film decreases, while the mass of stripping film increases. The increase in droplet diameter leads to easier blockage of the mesh pores and the local increase in film thickness, eventually leading to the overall decrease in film thickness but the increase in stripping film mass, which seriously affects the filtration efficiency. At a droplet diameter of 20 μm, the filter fails.

The discrimination and recognition of underwater targets has been the focus of active sonar detection. The structural characteristics of the target echo arrival in the bottom bounce area are analyzed when the target depth is greater than the transmit-receive depth. A 9-path arrival structure model is established. The relationship between the time delay of the bottom reflected/ surface-bottom reflected path and the target depth is analyzed. A method is proposed to estimate the time delay by searching for target arrival structure in bottom bounce area, and to estimate the target depth by using the relationship between the time delay and the target depth. The algorithm is verified based on explosion sound source data. The estimated depth of the underwater target obtained by a single hydrophone is in good agreement with the real target depth. The depth estimation of 20 targets within the range of 2.0—25.6 km is conducted, among which the depth estimation error of 17 batches is less than 10 m, and the average depth estimation error of 20 batches is approximately 7 m.

By modeling the soil as a three-dimensional axisymmetric medium and considering the three-dimensional wave effects within it, a theoretical study is conducted on the frequency domain characteristics of the longitudinal vibration of threaded piles in viscoelastic foundations. Based on the three-dimensional wave theory, the wave equation of the soil surrounding the pile under axisymmetric conditions is established. The vibration response solution of the screw pile under fully coupled pile soil conditions is obtained using Laplace transformation and variable separation methods. The results show that compared to the models that neglect radial displacement, the solution incorporating the three-dimensional wave effect of soil captures both longitudinal and shear waves in the soil, offering higher accuracy. In the low-frequency range, longer screw piles exhibit greater dynamic stiffness and damping, with the lateral friction along the pile shaft playing a more prominent role. Additionally, reducing the spacing between screw threads enhances the restraining effect of the surrounding soil, significantly improving vertical bearing capacity of the pile foundation. The existence of the screw threads has a substantial influence on the vibration of the screw pile.

Cracks are one of the most common pavement diseases, which will affect road traffic safety. To address the high cost and time-consuming challenges associated with manual investigation and determination of pavement cracks, a method based on image processing is proposed to intelligently detect the maximum crack width. The DAUNet framework is optimized, the attention mechanism is integrated, and the accuracy of crack segmentation is improved. Then, the segmented cracks are processed through corrosion iteration, connected domain discrimination, and quadrant division, so that the maximum width of cracks with different directions can be calculated more accurately. Experimental results show that the optimized DAUNet has improved the evaluation index sOIS by 3.15%, increased the accuracy of calculating the maximum crack width by 3.09 percentage points in comparison with the current optimal maximum crack width calculation method, and shortened the time by 89.06%.

In large-scale surveillance systems, the lack of positive cross-camera pedestrian samples in cross-regional scenes limits the performance of person re-identification (Re-ID) models. To tackle this challenge, an unsupervised domain adaptive person re-identification method incorporating multi-granularity feature mining and domain-invariant feature learning is proposed. The method consists of a multi-granularity feature learning module and a domain distribution alignment module. Within the multi-granularity feature learning module, global discriminant features of pedestrians are extracted through global features learning. To further enhance the discriminative capability of pedestrian features, a local consistency feature learning module is proposed to strengthen the interactions among local features. By jointly learning global and local features, the network is encouraged to extract multi-granularity discriminative features, thereby improving the performance of the person re-identification model. Additionally, a domain distribution alignment module is incorporated, leveraging style transfer to generate positive samples with diverse styles across cameras for target domain. This not only addresses the issue of the lack of positive samples across cameras in cross-regional scenes but also enhances the domain adaptation capabilities of the model. Extensive experiments conducted on the Market-1501, DukeMTMC, CUHK03, and MSMT17 datasets demonstrate the effectiveness of the proposed method compared to state-of-the-art person re-identification methods.

To address the attitude stabilization control problem of hexacopters under unknown disturbances, a nonlinear feedforward compensation backstepping control method for attitude stabilization of hexacopter is proposed. A nonlinear unknown input observer is employed to estimate the unknown external disturbance of the unmanned aerial vehicle. Then the Sigmoid tracking differentiator is introduced as a feedforward compensator to counteract the estimated disturbance, thereby enhancing the performance of the backstepping controller of the hexacopter. Furthermore, the stability of the attitude tracking deviation closed-loop system are established. Comparative simulation experiments verify the effectiveness and superiority of the proposed method.

As an important foundation of materials genome engineering, the establishment of materials database and the optimization of process parameter have an important impact on materials research and development. To address current challenges such as traditional relational databases being inadequate for storing multi-source, high-dimensional, and heterogeneous alloy data, and the urgent need to shift from the traditional trial-and-error experimentation to a data-driven research paradigm, this paper proposes to establish an alloy graph database based on ontology and graph data models. Based on this foundation, an improving dung beetle optimization (IDBO) algorithm is introduced to optimize combinations of process parameters. The algorithm enhances global search capability by initializing the population using inverse learning and updating dung beetle position using a variable spiral search strategy. To avoid local optima, Cauchy mutation perturbation and hybrid strategies are fused around the optimal solution to generate new candidates. To verify the performance of the proposed IDBO algorithm, it was tested on 23 classical benchmark functions, with results showing that IDBO algorithm outperforms other algorithms and significantly improves the convergence speed and optimization accuracy. Furthermore, when applied to the optimization of alloy process parameters, the IDBO algorithm not only demonstrated superior performance compared to other optimization algorithms but also successfully identified the optimal parameter combinations, validating its superiority in process parameter optimization.

The non-axisymmetric deviation of blade stagger angle and tip clearance has significant impact on aerodynamic performance and stability in actual compressors. In this paper, three sets of compressor test, including the prototype with a uniform layout, as well as non-axisymmetric stagger angle and tip clearance layouts, were studied on a four-stage low-speed research compressor test rig. Experimental measurements and numerical simulations were conducted to obtain the overall performance, interstage aerodynamic parameters, and non-axisymmetric properties. The test results show that the two non-axisymmetric layouts reduce compressor efficiency compared with the prototype, but the non-axisymmetric clearance layout increases the prototype stall margin by 0.67%. Numerical simulation analysis reveals that in the near-stall condition, when the low-energy fluid from the large clearance sector passes through the small clearance sector, the high-entropy region is decomposed and the unstable fluid is reconstructed, thus alleviating the stall characteristics. The original measurement data obtained in this paper calibrates the numerical simulation tool and provides a reference for further clarifying the actual flow field characteristics of high-pressure compressors.

A numerical analysis model for progressive failure was constructed to address the prediction of quasi-static tensile strength and progressive damage analysis of fiber-reinforced composite laminates with open holes. The model focuses on the stress concentration relief effect caused by splitting, combines with the finite element analysis software Abaqus, introduces two columns of zero-thickness cohesive cells at the crack tip along the fibre direction to simulate splitting, and divides the mesh along the fibre direction, which effectively reduces the mesh dependency. This model is applied to quantify the stress-relief effect in the stress concentration area, predict the tensile strength of various open-cell laminates, and explore their progressive damage process. The model predictions are validated in comparison with experimental data. The results show that the progressive damage model considering the stress-relief effect in the stress concentration zone achieves higher accuracy than other finite element models in calculating the stress concentration factor of open-cell laminates, and has reasonable reliability and accuracy in strength prediction and progressive damage simulation.

Stable copper and copper oxide nanoparticles are prepared by supercritical hydrothermal synthesis using acidic etching waste solution as the raw material. The effect of different alkali additions on the preparation of copper oxide nanoparticles is investigated, and verification test of the effect of impurity removal is conducted. The control mechanism of copper oxide morphology is obtained, and the effect of corrosion on the purity of final products is analyzed. The results show that under optimized parameters, copper and copper oxide nanoparticles prepared from acidic etching waste liquid have average particle sizes of 82 nm and 52 nm, respectively, with purities of 99.41% and 99.2%, after the removal of impurities. The technological process for the pre-treatment of acidic etching waste is established, and the synthesis process of high-purity nano copper and nano-copper oxide using acidic etching waste as precursor is developed. Finally, the technical economy is accounted based on a pilot-scale continuous plant with a treatment capacity of 480 L/h.