Advances in Engineering Research : Possibilities and Challenges
Advances in Engineering Research : Possibilities and Challenges ISSN Print: 3079-5192 ISSN Online: 3079-5206
Research on the Influence of Magnetron Sputtering parameters on the Structure and Properties of ITO Thin Films
This study systematically investigates the influence of key magnetron sputtering parameters—working pressure, sputtering power, and nitrogen doping—on the growth, microstructure, and functional properties of indium tin oxide (ITO) thin films. Through a designed experimental matrix, the deposition kinetics, crystallographic structure, chemical bonding states, and electrical resistivity were characterized to establish clear process-structure-property relationships. The results reveal that increasing the working pressure enhances oxygen vacancy concentration, which dominates the reduction in electrical resistivity despite a degradation in crystallinity. Higher sputtering power improves film crystallinity and dopant activation, synergistically optimizing carrier mobility and concentration. Nitrogen incorporation introduces complex doping effects, initially passivating oxygen vacancies to lower resistivity at low partial pressure but forming carrier-trapping nitride phases at higher levels, leading to increased resistivity and amorphization. This work provides a mechanistic understanding of parameter-controlled defect engineering and microstructure evolution in ITO films, offering practical guidance for tailoring their optoelectronic performance for advanced transparent electrode applications.
Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by/4.0/
Overview of Coalbed Methane Mining and Envi-ronmental Impacts
Coalbed methane extraction holds significant importance in the energy sector, yet its extraction process and potential environmental impacts cannot be overlooked. This paper reviews the current state of coalbed methane extraction technology and its environmental implications. Through analysis of coalbed methane formation, extraction techniques, resource distribution, and environmental effects, it summarizes the challenges facing coalbed methane extraction today and future development trends. Subsequently, it discusses potential environmental issues arising from CBM extraction, such as increased geological hazard risks and groundwater contamination. To address these concerns, the paper outlines common environmental protection measures and technical approaches, including water resource management and methane emission control. Subsequently, the environmental impacts of coalbed methane extraction are explored in depth, ex-amining both the consistency and discrepancies among different studies. Finally, future research directions are proposed, including improvements in environmental monitoring technologies, refinement of environmental management policies, and the development of novel eco-friendly technologies.
Numerical Solving Strategy for High-Energy Laser Thermal Management Based on the Nonlinear Heat Conduction Equation
High-energy laser systems are fundamental to modern advanced manufacturing and defense engineering, where effective thermal management is the key bottleneck limiting beam quality and operational longevity. This paper presents a robust numerical framework for simulating transient temperature fields in laser gain media, governed by a nonlinear parabolic partial differential equation (PDE). We first establish a comprehensive thermophysical model incorporating conductive heat transfer, convective boundary cooling, and crucially, the temperature-dependent thermal conductivity of the solid-state crystal (e.g., YAG or Sapphire). This results in a nonlinear heat conduction equation. For its numerical solution, an implicit Finite Difference Method (FDM) scheme is implemented, ensuring unconditional stability for the stiff problem arising from intense internal heat generation. A predictor-corrector iteration is designed to handle the nonlinearity introduced by the variable conductivity. Applied to a simplified cylindrical laser rod geometry, the model successfully simulates the evolution of temperature and thermal stress under pulsed operation. The numerical solution reveals a significant thermal lensing effect, with a maximum temperature rise of 152.3 K and a corresponding focal power of 0.85 m⁻¹. Comparative analysis demonstrates that neglecting the nonlinearity (assuming constant conductivity) leads to a 12.7% underestimation of the peak temperature and a mischaracterization of the stress profile. This work provides a reliable and adaptable computational tool for the design and optimization of thermal management systems in high-power laser engineering, highlighting the critical role of applied mathematics in tackling complex multiphysics challenges.
Research on Bolt-Cable Reinforcement Support Technology for Gob-Side Entry Retaining in "Three-Soft" Coal Seams
This paper focuses on the application of the gob-side entry retaining technology under complex surrounding rock conditions in "Three-Soft" coal seams. Numerical analysis was conducted using FLAC to study the ground pressure behavior before and after gob-side entry retention in such seams. The research shows that when applying gob-side entry retaining technology under complex roof conditions in "Three-Soft" coal seams, roof subsidence is difficult to control. By utilizing surrounding rock control technology and combined bolt-cable reinforcement support technology, the bearing capacity of the surrounding rock is fully mobilized. This enables the bolts, cables, and surrounding rock to jointly support the roof, thereby reducing excessive subsidence of the retained entry roof. Under reasonable roadway support conditions, the gob-side entry retaining technology proves to be effective and significantly improves mining efficiency.
Combined bolt-cable reinforcement support technology
Study on vibration isolation effect of different vibration isolation structures
This study investigates the effectiveness of open, EPS-filled, and sand-filled trenches in isolating ground vibrations using the finite element numerical sim-ulation method. A three-dimensional finite element model was established to analyze the attenuation of vibration acceleration induced by different barrier types. The isolation performance was evaluated based on the amplitude attenua-tion coefficient and the average amplitude attenuation coefficient. The results indicate that the vibration isolation effect of all three trench types decreases approximately linearly with increasing distance from the vibration source, with better performance observed in the near field compared to the far field. Under identical conditions, the open trench provides the best overall vibration isola-tion, followed by the EPS-filled trench, while the sand-filled trench performs the least effectively. The corresponding average amplitude attenuation coefficients are 0.823, 0.686, and 0.626, respectively. Further analysis reveals that filling material reduces the barrier's isolation efficiency, particularly in the far field, where vibration attenuation remains significant but the relative isolation effect is diminished.
Research on Optimization of Roadway Support in Metal Mines Based on Engineering Safety
Aiming at the problem of roadway support stability under complex geological conditions in deep metal mines, this study takes Ma cheng Iron Mine, one of the largest underground iron mines under construction in China, as the research object. It systematically analyzes the failure mechanism of the roadway support system during multi-level mining and proposes an optimization scheme based on the collaborative support theory. A hierarchical bolt-mesh-shotcrete + anchor cable + steel arch frame collaborative support system is established: low-prestress resin bolts (anchoring force 180kN) and high-strength steel mesh (mesh density 100×100mm) are introduced, anchor cable parameters are optimized (Φ21.8mm, prestress 250kN), and a dynamic support adjustment mechanism is constructed. The research results provide theoretical basis and technical support for the safe and efficient mining of deep metal mines.
Self-healing test of concrete mixed with permeable crystalline materials
Concrete is a core component of infrastructure. Owing to its low tensile strength and exposure to complex service environments, it is prone to cracking, which seriously impairs the durability and safety of structures. Traditional repair techniques suffer from drawbacks such as high cost and low efficiency, while the self-healing technology of concrete slabs provides a new approach by simulating biological self-healing mechanisms to achieve autonomous crack repair.
An Elliptical Envelope-Based Method for Casing Corrosion Diagnosis
Casing corrosion leading to wall thinning or perforation poses a major threat to wellbore integrity, highlighting the urgent need for accurate corrosion diagnosis technology. To address this, this study proposes a casing corrosion diagnosis method based on the EllipticEnvelope anomaly detection algorithm. Utilizing well logging data, the method establishes a robust covariance estimation model via the EllipticEnvelope algorithm to effectively identify corrosion-related anomalies. Taking the data from Well XX in the Ordos Basin as an example, the proposed method achieved a detection accuracy of 92.3%. Compared to the Isolation Forest and threshold methods, this represents an improvement of 12.42% and 31.48%, respectively. The results demonstrate that the method can identify casing corrosion more effectively, providing a reliable technical means for downhole condition assessment.
Copyright (c) 2025 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by/4.0/
The potential power of ecological value visualization for the sustainable development to industrialized cities by combing ArcGIS and GeoSOS-FLUS: A case study in Jingmen
Land-use change has an impact on the value of ecosystem services. To uncover the relationship that has guiding significance for achieving ecological protection and coordinated socio-economic development in industrialized City. Based on the interpretation results of land use data from 2000 to 2020, the land use transfer matrix was established to analyze the characteristics of land-use changes in Jingmen. The ecosystem service values (ESV) was estimated using the equivalent factor method, in order to illustrating the spatiotemporal characteristics of land use change and ESV, ArcGIS software and the natural break-point method were employed. Results showed that：(1) From 2000 to 2020, the amount of cultivated land, forest land and grass land decreased following the construction land increased. The conversion of cultivated land to construction land is the most important way of land-use transfer. (2) The change of ESV showed a slow upward trend to a gradual downward trend. Among the single ecosystem services, regulation services held the main position. The spatial distribution of ESV did present the scattered distribution in low, medium, and high ecological value service area, and high value areas mainly distributed in areas of river and lake. (3) The simulating results of the land use pattern by GeoSOS-FLUS model showed the ecological service system rises 9.85% compared to 2020.
Stability analysis of a landslide based on FLAC 3D
This paper focuses on the problem of the 1# and 2# aqueducts in the sliding section of the Huaipai Yellow River water diversion project, which were damaged due to the reactivation of the ancient landslide body. A stability analysis of the initial state of the slope was conducted. The Midas GTS NX and FLAC3D joint modeling method was adopted: the geological CAD drawings were sorted out, the surface and solid models were established through Midas, and after cutting the sliding body, it was imported into FLAC3D. Based on the Mohr-Coulomb criterion, boundary constraints and gravity loads were applied for numerical simulation. The results showed that the maximum horizontal displacement was 3.9 cm (in the middle-upper part) and the maximum vertical displacement was 4.95 cm (in the middle-upper part of the sliding body); the surface was mainly under tensile stress, and the absolute value of the bottom compressive stress increased with depth; the maximum shear strain increment was small; the slope stability coefficient was 1.48. The conclusion indicates that the slope as a whole was in a stable state in the initial state.
Research on Real time Acquisition System Based on Binocular Stereoscopic Vision
This paper presents the design and implementation of a real-time 3D acquisition system utilizing binocular stereoscopic vision. The primary objective is to develop a robust and efficient pipeline capable of capturing, processing, and reconstructing three-dimensional geometry of dynamic scenes with low latency. The core methodology integrates synchronized image capture from a calibrated stereo camera pair, followed by real-time stereo rectification and a dense stereo matching algorithm optimized for speed. The system successfully achieves real-time performance, generating dense depth maps at a frame rate sufficient for interactive applications. Experimental results demonstrate the system's accuracy in reconstructing static objects and its capability to track depth variations in moderately dynamic environments. The conclusion highlights that the implemented system provides a practical and effective solution for real-time 3D perception, establishing a reliable foundation for applications in robotics guidance, quality inspection, and augmented reality where immediate spatial feedback is critical.
Multifunctional Intelligent Water Curtain Air Cabinet
This project aims to develop an intelligent model-specific water curtain system, providing a compact, cost-effective, and high-performance comprehensive solution for model enthusiasts and small studios to address issues such as formaldehyde pollution, particulate diffusion, temperature and humidity, and noise during painting operations. The system integrates formaldehyde sensors, temperature and humidity sensors, and a display screen, and uses intelligent control algorithms to link formaldehyde concentration with fan speed, ensuring air quality remains within safe limits. Additionally, the system features real-time data display, alarm notifications, and remote control capabilities, enhancing user experience. Experimental results demonstrate that the system meets expected targets in purification efficiency, energy consumption control, and operational convenience. In the future, with further advancements in intelligent technology, this system is expected to play a greater role in model making and air purification, providing users with a healthier and more environmentally friendly working environment.
Architectural Choice in Inverse Design of THz Metamaterials: Local Priors vs. Global Attention under Data Scarcity
The inverse design of terahertz (THz) electromagnetically induced transparency (EIT) metamaterials is complicated by high-dimensional parameter spaces and complex spectral mappings. While deep learning has emerged as a potent tool for addressing these issues, the suitability of distinct neural architectures under data-constrained conditions remains underexplored. Focusing on EIT spectra characterized by long-range frequency dependencies, this study compares the inverse design performance of the Fully Connected Residual Network (FC-ResNet), which leverages local inductive biases, against the Transformer architecture, which relies on global self-attention mechanisms. We evaluated these models using a comprehensive dataset of 20,476 samples and a restricted dataset of only 500 samples. The results demonstrate that in data-rich environments, both architectures achieve exceptional accuracy (R² > 0.99), indicating that architectural differences do not constitute a bottleneck when data is abundant. However, in small-sample regimes—simulating scenarios with scarce experimental data—the FC-ResNet exhibits significantly superior generalization capabilities compared to the Transformer. Our findings suggest that while Transformers offer global modeling potential, they are prone to overfitting when data is limited; conversely, the structural priors of the FC-ResNet provide essential regularization in such contexts. This work offers empirical guidance for selecting architectures in the intelligent design of metamaterials where data availability is a limiting factor
Spatio-Temporal Simulation and Change Analysis of Land Use on the Qinghai-Tibet Plateau Under Future Scenarios
As a sensitive response zone to global change and a critical ecological barrier, the Qinghai-Tibet Plateau's land use and land cover change (LUCC) profoundly impacts regional ecological security and sustainable development. To systematically analyze its evolution patterns and project future trends, this study integrated three phases of remote sensing data from 2000 to 2020. coupled with multi-source drivers including topography, climate, vegetation, and socioeconomic factors. Employing the PLUS (Patch-generating Land Use Simulation) model, we simulated and revealed the spatiotemporal differentiation and transformation mechanisms of LUCC on the Tibetan Plateau over the past two decades. We further projected land use patterns for 2060 under three scenarios: Natural Development (ND), Water Resource Protection (WP), and Sustainable Development (SD). Results indicate: ①Between 2000 and 2020, the land use structure of the Qinghai-Tibet Plateau generally maintained a pattern of "grassland as the dominant type, with unutilized land as the secondary type." Grassland area increased from 132.26×10⁴ km² to 134.73×10⁴ km², and its proportion rose from 51.37% to 52.33%. Unutilized land decreased from 101.57×10⁴ km² to 98.57×10⁴ km², with its proportion declining from 39.45% to 38.28%; Forest land, water bodies, and constructed land all showed slight increases, while cultivated land decreased marginally. Overall changes were limited in magnitude but exhibited clear trends. ②Significant spatial differentiation in LUCC emerged, revealing a gradient pattern: concentrated forest land in the southeast, extensive grassland in the central region, and dominant unutilized land in the northwest. Constructed land expanded slowly along river valleys and transportation corridors in a "point-axis" pattern. ③Multi-scenario simulations indicate that by 2060, the SD scenario demonstrates the most optimal comprehensive performance in ecological conservation and construction control; the WP scenario significantly promotes the expansion of forest and water areas; while the ND scenario may delay ecological improvement processes. The land use evolution mechanisms clarified under these policy scenarios provide scientific basis for optimizing territorial space and advancing ecological conservation and restoration on the Qinghai-Tibet Plateau.
Accuracy and Challenges of Machine Learning in Predicting Carbon Emissions from Coal-Fired Power Plants: An Analysis Based on Load Variations
This study explores the application of machine learning (ML) techniques in predicting carbon emissions from coal-fired power plants, with a focus on the impact of load variations. The purpose of this research is to evaluate the accuracy and challenges associated with ML models when predicting carbon emissions in response to dynamic load changes, a critical factor for emission forecasting in power generation. The study employs a combination of time-series forecasting models, including regression techniques and advanced deep learning algorithms, such as Long Short-Term Memory (LSTM) networks, to analyze historical load and emission data from a selected coal-fired plant. The results show that while ML models can offer significant improvements in prediction accuracy compared to traditional methods, the accuracy decreases during periods of rapid load fluctuation, with prediction errors increasing by 10-15% under high load variations. Furthermore, model explainability and the integration of real-time data pose considerable challenges. The study highlights the importance of robust feature engineering, including the integration of fuel composition and environmental factors, as well as the need for real-time data processing to enhance model performance. In conclusion, the research demonstrates that machine learning can be a pow-erful tool for carbon emission prediction in coal-fired power plants, but it also faces limitations in terms of accuracy under fluctuating load conditions. To overcome these challenges, future work should focus on improving model ro-bustness through hybrid approaches that combine ML techniques with physical modeling and real-time data analytics. This would enable more accurate and reliable carbon emissions forecasting, helping to optimize emissions reduction strategies and support regulatory compliance in the power sector.
Research on System Optimization and Decision-Making for Building Industrialization Chain Focused on Carbon and Waste Reduction
Aiming at the "dual carbon" goals, this paper treats the industrial chain of building industrialization as a complex system and focuses on the synergistic reduction of carbon and waste. By analyzing the key contradictions within the system, a multi-dimensional indicator system encompassing policy drivers, technical capabilities, and environmental performance is constructed. An evolutionary game model between the government and core enterprises is then established to explore their strategic interaction mechanisms. Simulation results show that under current parameters, the system stabilizes in a "regulatory dilemma" state of "strict government supervision and traditional enterprise practices," revealing that excessively high transformation costs are the key bottleneck suppressing market-driven dynamics. Sensitivity analysis further identifies a priority sequence for breaking the dilemma: the primary task is to significantly reduce transformation costs, followed by establishing synergistic incentives combining subsidies and long-term benefits, while punitive mechanisms primarily serve as a safety net. Based on this, the study proposes a collaborative decision-making system from a system optimization perspective, prioritizing cost reduction, with incentives as the core and supervision as the safeguard, providing theoretical foundations and pathways for advancing the green and low-carbon transformation of the building industrialization industrial chain.
Multivariate Data Fusion and Health Analysis of Coal Processing Facilities
This paper presents a comprehensive health diagnosis and predictive maintenance management system based on multi - source data fusion. Firstly, the research constructs a data acquisition framework covering multi - source sensors such as vibration, temperature, and pressure. Wavelet transform and empirical mode decomposition are employed for signal processing, and multi - dimensional features in the time domain, frequency domain, and time - frequency domain are extracted. Subsequently, the local preserving projection algorithm is introduced to achieve effective dimensionality reduction and visualization of high - dimensional features. At the diagnostic level, a fuzzy clustering - fuzzy integral fusion diagnostic algorithm based on fuzzy measure theory is innovatively proposed to address the challenge of composite fault identification. At the predictive level, by constructing a degradation feature evaluation system encompassing correlation, monotonicity, and robustness, the self - organizing map network is used to fuse multiple features into a comprehensive health index, and the long - short - term memory network model is utilized to accurately predict the evolution trend of the equipment's health status. Finally, based on the health status assessment and remaining useful life prediction, a full - life - cycle predictive maintenance strategy is formulated, and an intelligent maintenance decision - support system is designed.
Data-Driven Inverse Design of THz Metamaterials: A Deep Learning Approach for Small-Data Regimes
Inverse design based on deep learning offers a revolutionary paradigm for ac-celerating the development of novel terahertz (THz) metamaterials. However, its application is often constrained by the high cost of acquiring large-scale sim-ulation datasets. This work focuses on the high-accuracy inverse design of THz electromagnetically induced transparency (EIT) metamaterials under small da-taset conditions, systematically evaluating the effectiveness of different deep learning architectures. To achieve precise inversion from a target spectrum to its physical structure, we constructed a representative dataset through parametric electromagnetic simu-lation. On this foundation, we built, trained, and compared three neural network models: a Multi-Layer Perceptron (MLP) as the baseline, a residual fully con-nected network (FC-ResNet) for enhanced deep network training, and a one-dimensional convolutional neural network (1D-CNN) designed for sequen-tial data. The results reveal a key finding: for this inverse design task, the FC-ResNet demonstrated superior predictive performance, achieving a coefficient of de-termination (R²) of 0.9794 on an independent test set. This significantly out-performed the baseline MLP (0.9438) and surprisingly surpassed the theoreti-cally more suitable 1D-CNN. Further analysis suggests that for the EIT inverse problem investigated here, the prediction relies more on the global morphology and correlation features of the spectrum than on localized characteristics. The FC-ResNet, with its deep architecture and effective residual learning mecha-nism, successfully captured this complex, non-local mapping relationship. The core contribution of this work is demonstrating that for complex physical prob-lems, a deep, general-purpose model with sufficient expressive power and stable trainability can outperform a more specialized architecture that is prone to in-formation loss. This finding provides a crucial guideline for model selection in the intelligent design of physical devices, particularly in resource-constrained scenarios
Retraction: Research Progress on Novel Water-Soluble Lubricant Additives
At request of the authors, this article has been retracted by Erytis Publishing Limited in light of clear evidence that the results and conclusions are no longer valid. We thank the authors for notifying us so that the publication record can be amended accordingly. Retraction published: November 10, 2025.
Research on Welding Methods of Monel Alloy
Monel 400 alloy boasts excellent mechanical and processing properties, along with good corrosion resistance in high-temperature acid-base and strongly alkaline media. It is widely applied in high-pressure gas supply systems across aerospace, shipbuilding, chemical, pharmaceutical, and other industries. This article primarily analyzes and studies the welding issues of Monel 400 alloy pipelines, as well as the determination of welding process parameters, aiming to improve the welding process of Monel 400 alloy and ensure welding quality.
AKCD: An Attention-Driven Framework with Kernel Conditional Independence Testing for Time Series Causal Discovery
Causal discovery is crucial for understanding complex system dynamics, espe-cially with non-stationary time series data. This paper presents An Atten-tion-Driven Framework with Kernel Conditional Independence Testing (AKCD), a novel two-stage framework to enhance time series causal discovery accuracy and robustness. In the first stage, AKCD uses a Transformer-based Multi-Scale Temporal Self-Attention Network (MS-TSAN) to capture complex temporal de-pendencies in time series, generating attention matrices and hidden representa-tions for subsequent causal inference. In the second stage, it applies ker-nel-based conditional independence (KCI) tests on the hidden representations to generate a conditional independence (CI) matrix, which is used as prior knowledge to guide causal graph optimization via regularization constraints. This hybrid approach combines deep learning’s feature extraction advantages with statistical tests’ causal inference rigor, making AKCD particularly suitable for non-stationary time series data.
Simulation of Thermal Barrier Layer Effects on Cu/PI Structures for Flexible Film Antenna Fabrication
This study investigates the influence of a thermal barrier layer on the laser ablation behavior of a Cu/Ti/PI trilayer structure using finite element simulation in COMSOL Multiphysics. A 2D model was established to analyze the temperature distribution and material removal under nanosecond pulsed laser irradiation. The effects of single-pulse energy, thermal barrier layer thickness, pulse width, and scanning speed were systematically evaluated. Results indicate that a Ti thermal barrier layer with a thickness of 200–300 nm effectively delays the vaporization of the PI substrate while maintaining adequate copper removal. Optimal processing parameters were identified as a single-pulse energy of 450 μJ, pulse width of 120 ns, and scanning speed of 250 mm/s, which achieved complete copper ablation with minimal substrate damage.
Modeling of Urban Road Vehicle Exhaust Emission Laws and Comprehensive Analysis Under Accident Scenarios
With the rapid growth of motor vehicle ownership, vehicle exhaust has become a major source of urban air pollution. This study constructs a mathematical modeling system for carbon monoxide (CO) and hydrocarbon (HC) emissions based on real road observation data, focusing on speed-emission relationships, optimal driving speed, driving behavior impacts, and accident-induced idling emissions. Results show the "emission rate-speed" model outperforms "mileage emission-speed" (CO emission rate R²≈0.994); HC’s optimal speed is ~25 mile/h (practical), while CO’s optimal speed exceeds real limits; variable-speed driving increases instantaneous emission fluctuations; 200 vehicles idling 30 minutes emit ~1754g CO and ~189g HC. This study provides a scientific basis for traffic emission management.
Grating structures are fabricated inside glass based on femtosecond laser parallel processing technology
Femtosecond laser single-focus scanning has the problem of low efficiency when manufacturing large-area structures. This paper proposes a multi-focus parallel processing method based on the weighted Gerchberg-Saxton (GS) algorithm. A multi-focus hologram was designed and loaded onto a spatial light modulator to achieve uniform energy distribution. The effects of repetition frequency, processing times and scanning speed on the morphology of microchannels were systematically studied, and the uniformity was all above 90%. Using the optimized process parameters, a grating structure with a cross-sectional width of approximately 29.315 μm was fabricated inside the glass by the three-focal parallel process. Compared with single-focus scanning, this method triples the efficiency while maintaining high quality, providing a promising path for the manufacturing of scalable photonic devices.
Tribological properties of CF/PEEK paired with different metals
Under the condition of impurity water lubrication, the tribological properties of three friction pairs consisting of 45#, nickel-plated steel, 304L, and carbon fiber-reinforced PEEK composite (CF/PEEK) were investigated. Ball-on-block reciprocating tests were conducted using a self-made friction and wear tester. In combination with surface morphology characterization, the friction and wear mechanisms of the materials were deeply analyzed. The research results showed that with changes in load and rotational speed, the friction coefficient and wear volume of CF/PEEK-45# were consistently lower than those of CF/PEEK-NP and CF/PEEK-304L. Under the condition of impurity water lubrication, the compatibility of CF/PEEK with the three materials was ranked as follows: CF/PEEK-45# > CF/PEEK-NP > CF/PEEK-304L. This study provides key data support and theoretical guidance for the practical application of CF/PEEK composite materials in motion pair components such as water-lubricated bearings and guide rails.
Experimental Study on Dynamic Modulation Op-timization of Micro-Hole Morphology in Wa-ter-Assisted Laser Drilling
IN718 alloy, a typical nickel-based superalloy, is widely used in key components such as turbine blades and combustion chambers in aircraft engines due to its excellent high-temperature strength, creep resistance, and corrosion resistance. However, the material's high-temperature strength, poor thermal conductivity, and low plasticity lead to low efficiency in traditional machining and severe tool wear. Laser processing, with its high energy density, non-contact nature, and flexibility, is considered an effective method for machining micro-holes in high-temperature alloys. However, laser micro-hole processing in air still faces problems such as large hole expansion, rough hole walls, a large heat-affected zone, and thick oxide layers. To address these issues, this paper introduces a water-assisted environment and combines pulse delay technology to explore its effects and mechanisms in the micro-hole processing of IN718 alloy.
Synergistic laser drilling, pulse-delay modulation
Study on micro-hole morphology of Ni-based superalloy optimized by pulse-delay dynamic modulation of nanosecond laser
Ni-based superalloys are widely used in aerospace for their excellent high-temperature properties.. To address thermal damage and dimensional ac-curacy issues faced by IN718 in high-power nanosecond laser micro-drilling, this study proposes a pulse-time modulation strategy. By dynamically control-ling heat accumulation through precise pulse-delay adjustments, a segmented energy delivery mechanism enhances through-hole quality. Two pulse-delay op-timization strategies are proposed and verified in comparison with the conven-tional processing: the optimized hole size accuracy is improved by 70%, the av-erage roughness of the hole wall is reduced by 30%, and the taper of the through-hole is reduced by 54%.
From Grid-Following to Grid-Forming: A Review of Converter Technology Paradigm Shift and Its Applications in Renewable-Rich Power Systems
With the global penetration of renewable energy exceeding 30%, power systems are increasingly challenged by inertia deficiency and weak grid stability, exposing the limitations of conventional grid-following inverters (GFLI) which rely on external synchronization and may provoke sub-synchronous oscillations. This review analyzes the technological transition toward grid-forming inverters (GFMI)—capable of autonomously establishing and regulating voltage and frequency—and assesses their application in renewable energy systems. Through bibliometric analysis, case studies from major Chinese renewable bases, and hardware-in-the-loop simulations under weak-grid conditions (SCR as low as 1.0), we identify significant advancements in wide-bandgap semiconductor topologies, adaptive control strategies such as virtual synchronous generator (VSG) and self-synchronizing control (SSC), impedance reshaping, and fault ride-through capability. Real-world implementations demonstrate markedly improved performance: inertia response time reduced to 0.48s, suppression of sub-synchronous oscillations by 89%, reactive response within 25ms, and successful black-start within 180 seconds, alongside notable economic benefits including reduced wind curtailment and lower equipment costs. Nevertheless, widespread adoption faces standardization gaps particularly in thermal management of high-frequency devices, multi-timescale stability certification, and market mechanisms for ancillary services. We conclude that GFMI represents a foundational technology for future high-renewable power systems, though its full deployment requires integrated progress in adaptive intelligence, international standard alignment, and policy-supported market frameworks.
Evaluation and Suggestions on Logistics Level of Yangtze River Delta Urban Agglomeration Based on Factor Analysis and Cluster Analysis
As the "neural network" of economic integration, the development level of the logistics system determines the efficiency of cross-regional flow and allocation of factor resources. Currently, although the economic aggregate of the Yangtze River Delta region accounts for nearly one-fourth of the national total, the imbalance in logistics development has become a key bottleneck restricting in-depth regional collaboration. To comprehensively evaluate the logistics development level of the Yangtze River Delta urban agglomeration, this paper constructs an evaluation system for regional logistics level of the Yangtze River Delta urban agglomeration by selecting 9 indicators from 4 dimensions. The comprehensive scores of each city are calculated through factor analysis. The results show that Shanghai, Wuxi, and Nanjing have the highest logistics development level, followed by Jiaxing, Ningbo, and Nantong. Based on the scores of each common factor and the comprehensive scores obtained from the above factor analysis, a cluster analysis can be conducted on the regional logistics level of the cities in the Yangtze River Delta. According to logistics scale, openness, logistics network density, and industrial contribution, the 14 cities are divided into three categories. By analyzing and identifying the advantages and disadvantages of urban logistics development, revealing the spatial differentiation law of logistics development in the Yangtze River Delta, and proposing differentiated countermeasures, this paper puts forward policy suggestions for promoting the integration of the Yangtze River Delta region.
Wear Prediction for Helical Gears Used in EMU
By combining Hertz contact theory with the Archard wear formula, a wear model for the tooth surface of helical cylindrical gears used in high-speed trains was developed, and its wear characteristics were numerically simulate.The simulation findings show that, under ideal operating conditions, the tooth sur-face of the helical gear pair wears non-uniformly along the tooth width direc-tion, with the least wear near the pitch circle and the most wear at the root of the smaller gear. Based on this, the effects of various modules, tooth width, he-lix angle, and input torque on tooth surface wear are investigated further. The analysis shows that changes in module, input torque, and tooth width all have a considerable impact on the distribution of tooth surface wear, although varia-tions in helix angle have a modest impact.