Abstract:To enhance the aerothermal performance of squealer blade tips with minimal geometric modifications, a methodology was proposed in this study for the design of inclined pressure-side shoulder, based on the non-uniform tip profile offset. Utilizing the squealer tip of the first-stage rotor in the GEE3 engine as a foundation, two design schemes of outward and inward inclined pressure-side shoulders were developed. Through the modification of inclination amplitude and shoulder thickness, eight representative shoulder configurations were constructed. The influence on aerodynamic and heat transfer characteristics variation was investigated under turbine stage conditions. First, the reliability of the numerical method was validated against linear cascade experimental data and grid independence verification. Subsequently, computational fluid dynamics (CFD) models were solved to compare the differences in tip leakage flow and vortex structures near the blade tip between outward-inclined and inward-inclined shoulder configurations. Based on flow field characteristics, the mechanism by which inclined shoulder configurations influence aero-thermal performance was analyzed. Finally, the variations in the aero-thermal performance of the blade tip were quantified through total pressure loss, turbine efficiency, and heat transfer coefficient. The research findings indicate that the outward-inclined shoulder configuration reduces the tip leakage flow rate, leading to a decrease in total pressure loss and an improvement in turbine aerodynamic efficiency. The efficiency enhancement exhibits a positive correlation with the outward inclination amplitude, reaching a 0.15% efficiency gain observed at an inclination angle of 30°, while the average heat transfer coefficient at the blade tip increases by 0.58%. Thickened outward-inclined shoulders maintain aerodynamic performance without compromise while reducing the heat transfer coefficient at the cavity bottom by 11.06%. Conversely, the inward-inclined shoulder configurations cause a significant increase in leakage flow, resulting in a maximum turbine efficiency reduction of 0.29%, while the overall blade tip heat transfer coefficient decreases by 3.83%. Thickened inward-inclined shoulders effectively suppress further leakage flow increase and achieve a 12.81% reduction in the cavity bottom heat transfer coefficient.

Abstract:To improve the electromagnetic performance of the interior U-shaped permanent magnet synchronous motor and reduce the vibration noise of the motor body, a magnet-focusing type equal volume misalignment segmented interior U-shaped permanent magnet synchronous motor is proposed. First, the electromagnetic performance expression of the motor such as gap magnetic flux density, no-load back-emf, and output torque, and the expression of electromagnetic vibration such as radial electromagnetic force, vibration velocity and acceleration are derived. Second, the effects of the separate addition of magnetic barrier structure, misalignment structure, and Halbach magnetization structure on the electromagnetic performance of the motor are investigated. Finally, the structure parameters are analyzed and optimized, and the electromagnetic performance, electromagnetic vibration, and noise fluctuation of 5 types of U-shaped magnet pole permanent magnet synchronous motors are compared. The study shows that the four added structures have a significant effect on motor performance. In terms of electromagnetic performance, the addition of a magnetic barrier, misalignment structure, and auxiliary can improve the motor output torque and reduce the slot torque. The Halbach magnetization can improve the output torque, radial air-gap magnetic flux density distribution, and radial electromagnetic force distribution, and combination of the three can improve the motor output torque. The output torque becomes more smooth, and the slot torque and torque ripple are significantly reduced. In terms of vibration noise, the auxiliary slot structure is added to significantly suppress the 8th and 16th harmonic amplitude of the radial electromagnetic force; the magnetic barrier structure can suppress the low-frequency vibration of the motor, and the misalignment structure and Halbach magnetization can suppress the high-frequency vibration acceleration of the motor, and the four structures can significantly reduce the radial electromagnetic force the space of 8 and 16 times, and the vibration acceleration of 4 times and 6 times in time is significantly suppressed, the maximum sound pressure level and mechanical of the motor meet the requirements of motor operation, and the rotor sample of the permanent magnet motor is machined.

Abstract:To address the reliance on manual inspection and the low efficiency of alignment detection during the segmental precast bridge construction process, a method for detecting assembly alignment is proposed based on close-range photogrammetry. The principles of close-range photogrammetry are integrated and the rigorous forward intersection and bundle adjustment algorithms are employed to enhance the accuracy and efficiency of alignment detection in bridge construction. According to actual engineering scenarios, simulation software and an indoor experimental setup were used to conduct both simulated and real indoor experiments, exploring the feasibility of the proposed method and the influence of shooting distance and the number of segmental beams on measurement accuracy. Experimental results show that the method can process captured image data in real time to obtain the bridge’s alignment data. Using bundle adjustment, the measurement accuracy of target points can be significantly improved to within 2 mm, and the resulting bridge alignment accuracy reaches within 1 mm, which verifies the effectiveness of the technique. Additionally, under various shooting distances, the method can still accurately identify marker points through sufficient pixel data while maintaining consistent measurement accuracy, ensuring the reliability and stability of the alignment detection results. The study demonstrates that this method not only improves the reliability of bridge construction but also significantly enhances the efficiency of alignment detection, providing strong support for the successful implementation of bridge construction projects.

Abstract:In response to the widespread issue of pollution in dead-end pipes within urban water supply networks, an innovative solution involving the installation of spoilers was proposed. This study systematically investigated the impact of spoilers on the dispersion of pollutants within dead-end pipes under low Reynolds number turbulent flow conditions and evaluates their control effectiveness using particle image velocimetry technology. Additionally, the design parameters of the spoilers were optimized. The experimental results indicated that spoilers can create a cavity structure at the junction between the dead-end pipe and the main pipe, significantly altering the flow path and pattern of the incoming flow, reducing the rotational intensity within dead-ends, and weakening the mixing effect. The design parameters of spoilers, including tilt angle, height, and width, as well as their installation position, have a significant impact on the control effectiveness. Specifically, spoilers with a tilt angle of 30°, larger height and width, and installed at the leading edge of the main pipe are more effective in suppressing the spread of pollutants into the main pipe. This study provides new ideas and methods for water quality assurance and operational management of water supply networks, which is of great significance for ensuring the safety of residential water usage.

Abstract:This study addressed the challenges of low efficiency and operational instability in decentralized flush toilet waste management in rural areas of Heilongjiang, a cold region in China. By introducing a microbial composite agent, we aimed to enhance the stability and quality of effluent. Through long-term monitoring of flush toilets in rural households, we analyzed the water quality changes in the treatment of "black water" and "gray water", focusing on parameters such as chemical oxygen demand (COD), ammonia nitrogen (NH₃-N), total nitrogen (TN), total phosphorus (TP), and fecal coliform content. The results showed that during black water treatment, COD removal rates reached as high as 91.53%, with ammonia nitrogen removal consistently above 80%, and total phosphorus removal rate fluctuating between 63.85% and 82.00%. In gray water treatment, COD removal rates ranged from 81.95% to 90.08%, and ammonia nitrogen removal peaked at 91.11%. Additionally, the composite agent significantly reduced fecal coliform counts in the effluent, achieving a removal rate over 90%. Bacterial community structure analysis revealed that the application of the composite agent optimized microbial composition, effectively enriching cold-tolerant microbes such asAcinetobacterandPsychrobacter. This study demonstrated that the use of microbial composite agents in rural toilet waste treatment in cold regions not only improved pollutant removal efficiency and operational stability but also provided a scientific basis and technical support for the renovation of rural toilets and fecal waste management in cold regions.

Abstract:Under strong vertical earthquakes near-fault lines, significant variable axial forces generated in bridge RC columns may weaken the shear resistance of RC column and even lead to shear failure. However, current research both domestically and internationally typically focuses the influence of vertical ground motion on seismic performance of RC columns based on fiber models, neglecting the shear degradation performance of the columns. Alternatively, studies based on shear models derived from fixed axial force assumptions investigate the effects of complex variable axial forces on the seismic performance of columns under strong vertical seismic actions. This leads to substantial differences in conclusions drawn from various shear models. In this context, based on quasi-static cyclic loading column model tests under constant and variable axial load, the accuracy of three representative shear models is evaluated, including one strength-based model and two deformation-based failure models, and a suggested model was provided. Moreover, a new numerical quasi-static loading test under constant and variable axial load is designed to study the effect of variable axial load on the seismic performance of RC column. Results indicate that the seismic performance of columns obtained by using different shear failure models show great disparity, especially for the case of variable axial load. The influence of variable axial loads focuses on the seismic performance of RC column mainly manifests in earlier initiation of shear failure and severer shear degradation. This paper provides valuable insights for simulating the shear performance of columns under variable axial loads, offering a theoretical basis for the seismic evaluation and design of RC columns located in the near-fault region.

Abstract:In response to the issues of significant dispersion in base shear results and inadequate consideration of near-field seismic characteristics when using the elastic time-history analysis method for checking calculations in current codes, a method for selecting seismic records based on dual parameters is proposed. Initially, the method employs the amplitude modulation parameters specified in the code to perform the first selection of seismic records. Subsequently, causal inference is utilized to identify the seismic ground motion parameters that have the most significant impact on structural base shear, serving as the screening parameters. Lastly, by specifying the value range for the screening parameters, a re-screening is conducted on the seismic records selected in the first stage. As a complement to existing seismic selection methods, a comparative analysis is conducted on the standard amplitude modulation method and the dual-parameter selection method for seismic records in elastic time-history analysis, using 300 natural earthquake records from a museum located on the Moxi Plateau in Luding County, Sichuan Province as a case study. The research results indicate that for seismic records with a fault distance less than or equal to 60 km, acceleration related seismic parameters have a significant impact on the base shear force of the structure, with the Park-Ang index having the greatest influence. The dual parameter method can effectively control the discreteness of the base shear force while ensuring that the structural safety margin meets or exceeds specification requirements. Compared with the specification method, the reduction in the standard deviation of the base shear force obtained through time history analysis using the dual parameter method can reach more than 25%. Due to the good control of the discreteness of the base shear force, when using the dual parameter method, for particularly irregular buildings in near-fault earthquake prone areas, artificial seismic motion records may not be necessasry in the elastic time history supplementary calculation.

Abstract:To investigate the influence of different experimental methods on the bond performance between corroded rebars and concrete, numerical simulations were conducted on pull-out tests and beam tests. Corresponding pull-out and beam models were established, and the effects of non-uniform corrosion of tensile rebars and confinement conditions (thickness-diameter ratio and stirrup confinement) on bond performance were analyzed. In the numerical models, mechanical interlocking forces were simulated by fine modeling of the tensile rebar and the surrounding concrete, while frictional forces were reflected through face-to-face contact ignoring chemical bond forces. The corrosion heterogeneity within the cross-section of tensile rebar was also considered. A two-stage numerical analysis method was adopted, in which the influence of corrosion on the bond behavior between the rebars and the concrete interface was simulated by applying enforced displacements and then the bond performance of corroded specimens was analyzed. The results show that the experimental methods have a significant impact on the failure mode and bond stress-slip curve of the corroded specimens. The existing corrosion-induced cracks are deepened and widened in both the beam and pull-out models when the bond failure occurs, but the beam model also generates diagonally traversing cracks throughout the bond area. The bond strength and descending segment slope in the pull-out model are significantly greater than those in the beam model. Stirrups barely influence the bond strength. As the thickness-diameter ratio increases from 2.0 to 3.5, the bond strength in the beam model and pull-out model increases by 16% and 32% respectively at 3% corrosion rate. After demonstrating the validity of the aforementioned numerical model through comparing numerical results with experimental results, a normalized model was established for bond strength applicable to different test methods. Subsequently, comparison between the results calculated by this normalized bond strength model and those obtained from numerical simulations reveals good agreement, thereby confirming the reliability of the proposed normalized model.

Abstract:In order to solve the problems of greediness and silent variable nodes in multiple dynamic scheduling residual belief propagation (RBP) decoding algorithms for low-density parity-check (LDPC) codes, a double decision residual belief propagation decoding algorithm (DD-RBP) is proposed by introducing the method of joint decision based on the check equation and probability residual value. Firstly, the variable nodes that need to be updated are selected according to the calculated probability residual value, which can reduce the number of silent variable nodes. Then, according to the results of the check equation, the residual values of the corresponding edges of the relevant check nodes are updated to further reduce the greediness. Finally, combined with the updated residual value, in the set of edges connected by the variable nodes that need to be updated, the edge with the largest residual value is selected locally or globally and updated, and the above process is repeated until the maximum number of settings is reached. Theoretical and simulation analyses demonstrate that, for low-density parity-check codes under IEEE802.16e standard and 5G NR standard, the proposed double decision residual belief propagation decoding algorithm performs better than other algorithms in decoding under additive Gaussian white noise channels and Rayleigh fading channels with increased complexity.

Abstract:To investigate the regional variations in site effects and establish empirical site amplification models, a large number of seismic ground motion recordings collected by the National Strong-Motion Observation Network System of China from 2007 to 2020 were utilized to separate the site effects at 211 strong-motion stations mainly distributed in the western part of the Southern Tianshan, Longmenshan fault zone, and Yunnan based on the generalized spectral inversion method. The empirical models of site effects associated with either the site category or V_S30 were established. Furthermore, the regional dependence of site effects was systematically investigated, along with the correlation between site amplification characteristics and the code-specified site classification indicators (overburden thickness H and equivalent shear-wave velocity V_Se). The results demonstrate that the empirical amplification models for Site Classes Ⅰ, Ⅱ, and Ⅲ effectively capture the systematic differences in site effects across the entire frequency spectrum. Significant regional variations in site amplification are observed within the same site classification category, with particularly pronounced differences between Class III sites in the western Southern Tianshan and Yunnan regions. The correlation between the site classification indicators (H and V_Se) and actual site amplification characteristics is found to be statistically insignificant, with potential regional discrepancies noted in this relationship. Meanwhile, the V_S30-based empirical models reliably represent the influence of local site conditions on ground motion amplification. These results provide a scientific basis for developing regionalized ground motion prediction models, improving the accuracy of seismic risk assessments and guiding the practical application of regional site amplification effects. Furthermore, the results offer valuable support for exploring more effective site classification indicators and establishing regionally differentiated site classification standards.

Abstract:To address the issues of single-category defect identification and low segmentation accuracy in current digital image-based methods for concrete bridge defect detection, a refined semantic segmentation model named HDNet, which is built upon an encoder-decoder architecture, was introduced. In terms of encoder design, a hierarchical window-based self-attention mechanism was implemented, which combinnes sliding window partitioning and cross-layer residual connections to enhance gradient propagation. A kernelized attention module was incorporated to strengthen gradient responses for local defects, such as erosion and cracks, while simultaneously reducing interference from the background texture of the bridge. A pixel-deformation dual-path architecture was adopted in the decoder, in which the pixel path employs pointwise feature mapping to capture the morphological details of cracks and the deformation path utilizes deformable convolutions to adaptively match the irregular geometric contours of spalling regions. A series of experiments were carried out on a high-resolution dataset of bridge defects including four categories of defects: cracks, erosion, exposed rebar, and spalling, which was captured by unmanned aerial vehicle(UAV). Comparisons with those dominant models such as DeepLabV3+ and SegFormer were performed, and then ablation study analysis, heatmap analysis and real-bridge validation were carried out. The results indicate that HDNet attains a mean Intersection over Union (mIoU) of 71.91% on the validation set, surpassing the suboptimal model SegFormer by 7.86%. Ablation studies validate the necessity of kernelized attention (which improves mRecall by 5.83%), hierarchical sliding-window attention (which boosts mIoU by 5.92%), and the synergistic design with the Dice loss function. Heatmap analysis demonstrates HDNet’s ability to accurately capture defect texture details and disentangle the semantic boundaries of co-occurring defects. In real-bridge testing, HDNet maintains the relative error of defect size measurement within ±5%, which confirms its practical applicability. By integrating encoder-decoder co-optimization and cross-resolution hierarchical enhancement mechanisms, HDNet substantially enhances the recognition accuracy and robustness for complex bridge defects, thereby offering a high-precision technology for the intelligent detection of bridge surface deterioration.

Abstract:To investigate the macroscopic strength changes and the underlying microscopic mechanisms of solidified sludge (SS) after crushing and remodelling, which enhances the engineering applications of crushed and remodeled solidified sludge (CRSS), a series of direct shear tests, scanning electron microscopy (SEM) analyses, and mercury intrusion porosimetry (MIP) tests were conducted on both SS and CRSS to examine the effects of curing agent content, pre-curing age, and post-curing age on the shear strength characteristics of CRSS. Furthermore, the microstructure and pore structure characteristics analyses were performed to explore the intrinsic mechanisms underlying the shear strength changes in CRSS. The results indicate that the shear strength of the initial CRSS increases with both curing agent content and pre-crushing curing age. The internal friction angle of the initial CRSS is comparable to that of SS, ranging from 25° to 50°, while cohesion decreases significantly, varying from 20 kPa to 125 kPa. The shear strength of CRSS primarily originates from the friction and interlocking between soil aggregates, where higher aggregate strength corresponds to a greater internal friction angle. As the post-curing age increases, soil aggregates and the pores between them in CRSS are cemented and filled with hydration products, resulting in a significant increase in cohesion while causing minimal changes in the internal friction angle. In SS, the pore structure is predominantly characterized by small pores with diameters ranging from 0.01 μm to 0.10 μm and medium pores from 0.1 μm to 1.0 μm. With increasing curing agent content and curing age, medium pores in SS are progressively transformed into small pores. In CRSS, the volume of small pores decreases significantly, accompanied by a notable increase in large pores with diameters ranging from 1 μm to 10 μm and fissure pores larger than 10 μm, while the volume of medium pores remains largely unchanged.

Abstract:In order to predict the remaining life of electrical connector, this paper proposes a life prediction method for the electrical connector based on a combination model of an adaptive Wiener and a discrete grey model (DGM(1,1)). First, based on the Arrhenius model, the stress level of the temperature cycle test is selected to determine experimental parameters such as test cycle, exposure time, and temperature rise rate, and contact resistance is chosen as the degradation characteristic quantity for the temperature cycle test. Second, the remaining life of the electrical connector under temperature cycles is predicted using both the adaptive Wiener model and the DGM(1,1) model seperately. By comparing the pseudo-life prediction results of the two models under different temperature stresses are compared, a combined model of the adaptive Wiener and DGM(1,1) is proposed. Finally, the parameters in the Arrhenius model are estimated by the least squares method, and the life of the electrical connector under normal temperature is extrapolated. The results show that the DGM(1,1) model provides more accurate predictions under lower temperature stresses, while the adaptive Wiener model provides more accurate predictions under higher temperature stresses. Considering the applicability of the model under different temperature stresses comprehensively, the combined model of adaptive Wiener and DGM (1,1) can predict the remaining life of electrical connectors more accurately. The research results can provide reference for maintenance decisions of electrical connectors and support for the reliability improvement of electrical equipment.

Abstract:To study the safety and reliability of electric vehicle battery packs under mechanical abuse conditions, and prevent a series of safety accidents such as short circuits, thermal runaway, even fires and explosions that may occur when battery packs are subjected to collisions, this paper builds a mechanical abuse conditions of lithium battery thermal safety testing experimental system. Then safety experiments on lithium-ion batteries were designed and implemented under 4 mechanical abuse conditions: flat plate compression, cylindrical compression, spherical compression, and nail penetration test. A lithium battery monomer homogenization model was built through extensive data from mechanical abuse experiments. A complete lithium battery pack data set was established combined with the experimental data and lithium battery monomer homogenization model. Building on this foundation, a fusion mathematical model comprising support vector machine (SVM), sparrow search algorithm (SSA), and back propagation (BP) neural network was designed to predict the failure states of lithium battery packs under different parameter conditions, as well as the displacement and maximum load following failure under various parameter conditions. The experimental results show that SVM-SSA-BP can accurately predict the failure state, failure displacement and maximum load of the battery pack system. A comparative validation was conducted against four other competing models, and the generalization ability of SVM-SSA-BP model was evaluated by adding 20% Gaussian noise to the dataset. The results show that the predictive performance of the SVM-SSA-BP fusion mathematical model is superior to four other competing models, and it demonstrates good accuracy and stability. The model proposed in this article can accurately predict the safety performance of lithium battery packs under mechanical abuse conditions.

Abstract:To quickly and accurately predict the critical flashover voltage of composite insulators in catenary systems and reduce the workload of artificial pollution tests, a prediction model for composite insulator pollution flashover voltage is proposed. First, the performance of the back propagation (BP) neural network is enhanced using the subtraction average based optimizer (SABO) algorithm improved by the golden sine algorithm (GSA) and piecewise linear chaotic map (PWLCM). Second, artificial pollution tests are conducted to obtain the flashover voltage of 10 different composite insulators, and relevant test parameters are collected. Third, the Obenaus model is used to analyze the pollution flashover behavior of composite insulators, and the Spearman correlation coefficient method is employed to select 4 parameters closely related to the critical flashover voltage of composite insulators as input features for the prediction model. Finally, the prediction model is comprehensively evaluated using five-fold cross-validation and compared with prediction results from commonly used intelligent optimization algorithms. The results show that the GSABO-BP model predicts the flashover voltage of composite insulators with an average absolute error of 1.244 kV, an average absolute percentage error of 2.25%, and a coefficient of determination consistently above 0.98. Compared to the original SABO-BP model, the average prediction error is reduced by 67.80%. The GSABO-BP model demonstrates high prediction accuracy for the flashover voltage of composite insulators, which is significant for the anti-pollution protection of electrified railway power supply systems.

Abstract:To explore architectural design methods for the renovation of old buildings within the context of existing stock, this study uses the renovation project of the old buildings in the Wutongshan Community of Luohu District, Shenzhen, as a case study. By integrating psychophysical theory with virtual reality(VR) technology, the research investigates the potential of VR as a design medium. A VR perception experiment was designed using digital models and a head-mounted VR interaction system. Subjective preference evaluations were collected for 16 renovation schemes of individual buildings within the site. Additionally, 10 design influencing factors were extracted across three dimensions: form composition, interface composition, and landscape composition. Using SPSS for correlation analysis, the study examines the relationship between design elements and public preferences to understand the factors that attract public attention in the renovation of old buildings. Results indicate that morphology elements such as landscape configuration, courtyard configuration, interface brightness, block richness, D/H value, and roof visual width in the scheme are significantly correlated with human spatial cognitive preferences. Based on the experimental results, the optimization strategy of the design scheme is proposed. Using VR technology in optimizing the external form design of buildings helps designers understand the spatial cognitive mechanisms of users. This paper proposes an interactive design optimization process through multi-party collaboration, which can be used as a reference for related design practices.

Abstract:To solve the problem of low accuracy of existing unmanned target detection algorithm caused by object occlusion and small-object information loss in complex road scenarios, this paper proposes an enhanced obstacle detection algorithm based on YOLOv8, named YOLOv8-EA (effectual accurate). The algorithm incorporates a lightweight backbone using partial convolution to preserve spatial feature integrity. A large-kernel depthwise convolutional layer is introduced to reconstruct the pyramid pooling structure, and the multi-scaled self-attention features are fused through parallel connections, which Enhances the feature extraction ability of the model in complex scenarios. Additionally, a multi-branch architecture with reparameterization suppresses noise interference and enhances feature fusion via stacked gradient flow. A small-object detection head based on partial convolution is also designed to improve pixel-level feature extraction for small targets. Experimental results show that YOLOv8-EA achieves notable improvement in detection accuracy compared to the original YOLOv8. On the KITTI dataset, mAP50 and mAP50-95 increased by 2.4% and 4.7%, respectively, while on the SODA10M dataset, gains of 1.4% and 1.1% were observed, which demonstrate the strong generalization ability of YOLOv8-EA. The proposed algorithm shows superior capability in handling occlusion and small-object detection, offering more reliable perception support for unmanned driving systems.