Table of Content

25 March 2026, Volume 54 Issue 3

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2026, 54(3):  0. 

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Energy，Power & Electrical Engineering

Numerical Simulation of Lean Coal Combustion Based on Staged Plasma Ignition



LIU Dingping, WU Chaochao, PAN Shuhuan

2026, 54(3):  1-9.  doi:10.12141/j.issn.1000-565X.250214

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Improving coal adaptability of plasma ignition systems is of significant importance for assisting power plants in energy conservation, emission reduction, and achieving the dual-carbon goals. To address issues such as unstable combustion and flameout encountered during the operation of existing staged plasma burners when igniting lean coal with low volatile content, this study employs a numerical simulation approach. First, a three-dimensional mesh model of the staged plasma burner is established, incorporating the Realizable k-ε turbulence model, the P1 radiation model, and a pulverized coal combustion model with Two-competing-rates model for volatile matter release and kinetics/diffusion-limited model for char combustion. Subsequently, mesh independence verification is conducted by comparing temperature parameter differences among models with low, medium, and high mesh densities to ensure the reliability of the selected mesh. Following this, the control variable method is applied to sequentially investigate the effects of three critical operational parameters—plasma power, primary air velocity, and pulverized coal concentration—on lean coal combustion process within the staged plasma burner. Finally, the operational parameters of the plasma burner are optimized to resolve ignition and combustion issues associated with lean coal. The results demonstrate that plasma power is the key factor affecting lean coal ignition. To achieve stable ignition and combustion of lean coal in the plasma burners, the plasma power should not be less than 150 kW. Primary air velocity affects the combustion temperature during the initial ignition stage, with an optimal range identified between 22 to 25 m/s. Pulverized coal concentration is a crucial factor for successful lean coal ignition; maintaining a relatively high concentration is necessary to ensure stable ignition and combustion, with a recommended concentration not lower than 0.3 kg/kg. This research provides direct and important theoretical guidance and a practical basis for power plants to optimize the operational strategies of existing plasma burners, safely and economically utilize lean coal, reduce fuel costs, and lower carbon emissions. It holds positive implications for promoting fuel flexibility and facilitating the green, low-carbon transformation of coal-fired power plants.

Data-Driven Collaborative Management Strategy for Distribution Network-User Energy Coordination



BIAN Ruien, LIU Yadong

2026, 54(3):  10-20.  doi:10.12141/j.issn.1000-565X.250222

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Traditional home energy management systems focus predominantly on managing energy within the household itself, often neglecting user comfort and the potential for bidirectional interaction with the distribution network. To further enhance the intelligence of home energy management, this paper proposes a two-layer hierarchical home energy management strategy. The upper layer considers system operational constraints and costs of the distribution network, whereas the lower layer aims to satisfy user electricity preferences and cost requirements as much as possible. Based on interaction with the distribution network, this strategy achieves optimal household ener-gy management. To address the deficiencies and limitations of conventional distribution network power flow calculation and indoor thermal response algorithms in accurate modeling, two data-driven methods are introduced, namely the long short-term memory recurrent neural network (LSTM) and the online sequential extreme learning machine (OS-ELM). These methods aim to more accurately assess distribution network status and indoor thermal dynamics (LSTM for power flow estimation, OS-ELM for thermal response estimation). In addition, an efficient natural aggregation algorithm (NAA) is utilized to improve the solution speed and accuracy of the optimization process. To evaluate the effectiveness and scientific validity of the proposed algorithms, detailed comparative experiments are designed for the LSTM model in the upper layer, the dynamic thermal estimation algorithm proposed in the lower layer, and the overall two-layer energy management algorithm. Finally, experimental validation based on the IEEE 33-node system ultimately demonstrates that the proposed LSTM power flow estimation and OS-ELM-based thermal response estimation methods achieve significantly higher accuracy than conventional methods. The proposed stra-tegy also outperforms traditional approaches remarkably in reducing users’ electricity costs, ensuring distribution network security, and improving user comfort, thereby verifying its effectiveness and superiority. This method provides an effective technical support for addressing source-load coordinated optimization problems in communities with high penetration of distributed energy resources, and exhibits promising prospects for engineering applications.

Plug-and-Play Control Strategy for Distributed Energy Storage Systems Participating in Frequency Regulation



HUANG Xiangmin, ZENG Jun, WANG Pengxu, WANG Tianlun, LIU Junfeng

2026, 54(3):  21-30.  doi:10.12141/j.issn.1000-565X.250310

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The frequency stability of new power systems dominated by renewable energy faces severe challenges due to reduced rotational inertia, increased disturbance impacts,and insufficient frequency regulation resources. Distributed energy，typified by distributed energy storage systems (DESSs), has emerged as a novel and effective means to supplement system frequency regulation. However, the conflict between user’s autonomy demands and the deterministic requirements of the grid limits the application of DESSs in ancillary frequency regulation. To address this issue, this study first proposes a flexible aggregation and control model for DESSs, in which an aggregator generates real-time incentives based on the deviation between power demand and actual output to guide the response of distributed storage units. Next，prospect theory is applied to analyze the decision-making psychology of distributed energy users. Based on the incentive-response characteristic curve under bounded rationality, a simulation environment that better reflects real-world conditions is constructed for studying DER participation in frequency regulation. Furthermore, a flexible control strategy is proposed, which broadcasts dynamic incentive signals to distributed storage users. By employing dynamic linearization, the nonlinear frequency regulation problem is transformed into a parameter estimation task for a dynamically linearized system. A model-free adaptive incentive control method is employed to address uncertainties in user response and the difficulty of modeling user clusters. This method dynamically linearizes the aggregated response model using real-time data from the cluster control system and outputs an optimal real-time incentive signal. Finally, a case study based on a single-area power system is conducted in Matlab/Simulink. Under both step load fluctuation and continuous load fluctuation scenarios, the proposed method demonstrates effective frequency tracking performance, with the maximum frequency deviation controlled within 0.001 5 p.u. This validates the adaptability and effectiveness of the proposed incentive-based control method.

Intelligent Transportation System

Research Advances in Learning-Based Generative Methods for Interactive Scenarios of Autonomous Vehicles



XIONG Lu, FENG Haojie, ZHANG Peizhi, TIAN Mengjie, ZHANG Xinrui

2026, 54(3):  31-51.  doi:10.12141/j.issn.1000-565X.250188

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Interactive traffic scenarios, characterized by high-dimensional complexity and inherent risk, constitute a critical safety challenge in the testing and operation of autonomous vehicles (AVs). In recent years, learning-based generative method—exemplified by data generation, adversarial generation knowledge-driven generation, and large language models (LLMs)—has demonstrated significant advantages in improving the quality of interactive scenario generation and the efficiency of testing and validation, owing to their superior realism and coverage breadth. To systematically chart the advances, core techniques, and bottleneck issues in this domain, this paper presents a comprehensive review of learning-based generative methods for autonomous vehicle interactive scenarios, aiming to offer a clear technical roadmap and development direction for subsequent research. First, the fundamental attributes of interactive scenarios are outlined, and mainstream public datasets are comparatively analyzed to discuss their supportive role for learning-based generative methods. Concurrently, relevant standards, scenario description frameworks, and evaluation metrics for interaction are summarized, with an analysis of their impact on the expressive capacity of intertive behaviors. Second, the technical approaches and generation outcomes of various methods are categorized along two main directions: traditional learning-based methods and large language model-based me-thods. Finally, building upon existing findings, the article summarizes the current challenges faced by learning-based generative methods, both in terms of data and methodology, and proposes future research directions. The study finds that traditional learning-based methods face inherent challenges in balancing realism and diversity, as well as in the efficient discovery and generation of safety-critical scenarios. While LLM-based approaches show exceptional potential in semantic understanding and constructing complex logical narratives, they commonly encounter bottlenecks such as slow inference speeds, information hallucination, difficulties in aligning generated content with physical world constraints, and a lack of interpretability due to the “black-box” nature of their decision processes. Moreover, current research is still constrained by fundamental issues, including the scarcity of interaction-critical information in existing datasets and the absence of a unified metric for quantifying scenario criticality. This article concludes that although existing methods have achieved preliminary success in interaction modeling, significant improvements in generation quality and generalization capability are still required to meet the practical demands of autonomous vehicle development.

Predictive Motion Planning for Intelligent Connected Vehicles Based on Transformer Architecture



LI Anran, PAN Yuyan, XU Zhenlin, GAO Bolin, LI Yongxing, YU Hongsheng, CHEN Yanyan

2026, 54(3):  52-64.  doi:10.12141/j.issn.1000-565X.250056

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Efficient and safe motion planning for intelligent connected vehicles in complex traffic scenarios remains a pivotal challenge in the field of autonomous driving. This research proposed ST-Trans traffic prediction model based on the Transformer architecture and developed a predictive motion planner for intelligent connected vehicles leveraging ST-Trans. The ST-Trans model utilizes the Transformer architecture to mine spatial-temporal evolution patterns from real-time vehicle data and lane segment structural information provided by dynamic high-definition maps, thereby predicting future traffic states of lane segments. It further enhances prediction accuracy by incorporating lane segment connectivity and intersection signal phase information. The model adopts an encoder-decoder framework, where a lane encoder fuses vehicle and lane features, a road encoder models dynamic topological relationships, and a decoder iteratively generates future traffic state sequences. Experimental results demonstrate that ST-Trans outperforms the optimal baseline model by 12.2%, 12.1%, and 3.55 percentage points in terms of mean absolute error(MAE), root mean square error(RMSE), and accuracy, respectively. Based on the predictions from ST-Trans, the proposed predictive motion planner employs a two-layer structure. The lower-layer path planner dynamically selects target points and integrates dynamic programming with quadratic programming to generate smooth paths. The upper-layer speed planner constructs spatio-temporal corridors to compress the solution space and similarly combines dynamic programming and quadratic programming to generate safe efficient, and comfortable speed profiles. This structure significantly reduces the computational complexity of the motion planning task. Simulation experiments were conducted using SUMO and CARLA to evaluate the predictive motion planner. The results indicate that the ST-Trans-based predictive motion planner successfully implements predictive path and speed planning, and outperforms traditional motion planners in terms of safety, efficiency, comfort, and computational speed. The experiments verify that the proposed method effectively shortens the duration of high-risk states, improves traffic throughput and maintains low computational latency.

CGT-YOLO-Based Algorithm for Small-Target Traffic Sign Recognition



XING Yan, GUO Sihao, ZHANG Zhen, PAN Xiaodong, AN Dong

2026, 54(3):  65-78.  doi:10.12141/j.issn.1000-565X.250092

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To address the degradation in recognition accuracy caused by false and missed detections of small target traffic signs, this study proposes a small traffic sign recognition algorithm based on CGT-YOLO. First, a context-aware enhancement module (CAM) is introduced to replace the spatial pyramid pooling fast (SPPF) module in the YOLOv5s network. By employing parallel dilated convolutions with different dilation rates, the CAM enhances multiscale feature representation and contextual information of small traffic signs without reducing spatial resolution. Second, a global attention mechanism (GAM) is inserted after the concatenation operation in the backbone network of YOLOv5s. The GAM extracts features enhanced by the CAM and strengthens global interaction between channel and spatial dimensions through 3D permutation, multi-layer perceptron, and convolutional spatial attention, thereby highlighting the features of small traffic signs and mitigating the negative effects of complex backgrounds and long distances. Finally, a task-specific context (TSC) decoupled head is utilized to separate features for classification and localization tasks. Through the semantic context encoder (SCE) and detail preservation encoder (DPE) modules, the head generates semantically rich low-resolution feature maps for classification and high-resolution feature maps containing boundary information for localization, respectively. This disentangles classification and localization tasks at the feature source, resolving feature conflicts between the two tasks for small target traffic signs. Experimental results on a dataset constructed by integrating TT100K and CCTSDB show that the improved model achieves enhanced performance across all metrics: the missed detection rate and false detection rate are reduced by 12.1 and 11.6 percentage points, respectively, while mAP(0.50∶0.95) increases by 0.026 0. Compared to models such as YOLOv8s, NanoDet-Plus, and RT-DETR-Nano, CGT-YOLO demonstrates superior performance across multiple metrics. While maintaining a high inference speed (72.5 FPS), it effectively reduces false and missed detections, significantly improving the detection accuracy and robustness of small target traffic signs in complex scenarios.

Joint Optimization of Electric Bus Charging Station Siting and Vehicle Scheduling



HU Yucong, HUANG Weibin, CHEN Junhua, WU Weitiao

2026, 54(3):  79-90.  doi:10.12141/j.issn.1000-565X.250098

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Pure electric buses have become an important component of urban public transportation due to their environmental benefits. However, their widespread adoption is constrained by limited driving range, placing high demands on the planning of charging infrastructure and the formulation of vehicle schedules. Existing research often treats charging station siting and vehicle scheduling as independent problems, overlooking their interdependence. Moreover, most studies focus on single-depot or small-scale scenarios, which cannot adequately address the requirements for coordinated, cross-regional dispatching in large-scale and complex networks. To address these issues, this study constructs an integrated optimization model for electric bus charging station siting and vehicle scheduling. The model is built upon a spatio-temporal network framework designed for a multi-depot electric bus system. The objective is to minimize the total system cost, subject to various constraints including charging station construction, trip connection, state-of-charge(SOC) maintenance, vehicle scheduling, and charger matching.In order to accurately describe the operating cost, the model introduces the time-of-use electricity pricing and accounts for the parallel charging capacity of stations. To effectively solve this high-dimensional, discrete combinatorial optimization problem, an enhanced cultural memetic algorithm is designed. The algorithm incorporates improved genetic operators, introduces local search strategies such as trip-chain relocation and merging, and integrates a hierarchical constraint repair mechanism to ensure solution feasibility. The model and algorithm are validated using a case study based on a partial bus network in Chancheng District, Foshan City. The results demonstrate their effectiveness in handling problems of varying scales. Compared to traditional genetic algorithm and simulated annealing algorithm, the proposed algorithm can achieve better cost reduction in both small and large-scale instances. Sensitivity analysis further reveals that increasing battery capacity and reducing unit energy consumption can significantly reduce the total cost of the system, while the electricity pricing policy, especially off-peak rates, has a decisive influence on the operating cost. The study also confirms that charging station siting indirectly affects total cost by influencing scheduling efficiency, highlighting the necessity of joint optimization. This research enriches the theoretical framework for electric bus charging station siting and vehicle scheduling. The findings provide valuable, simultaneous insights for both the strategic planning and day-to-day operational decision-making of electric bus systems.

Electric Bus Scheduling Optimization in Cold-Region Cities Based on Battery-Vehicle Matching



HU Baoyu, ZHANG Yuheng

2026, 54(3):  91-103.  doi:10.12141/j.issn.1000-565X.250244

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To address the challenges such as accelerated battery lifespan degradation and significant fluctuations in driving range caused by extreme temperature variations in cold-region electric buses, this study proposes an integrated scheduling strategy based on “battery-vehicle matching”. This strategy involves equipping buses with batteries of different capacities according to seasonal temperatures and route load demands, enabling public transport operators to formulate more efficient operational plans. In terms of modeling, a mixed-integer programming model is established with the objective of minimizing operator cost. The model comprehensively calculates vehicle battery procurement expenses, daily maintenance costs, and charging expenses that consider time-of-use electricity pricing and demand charges. It quantifies both cyclic and calendar degradation of batteries and integrates them into a comprehensive battery lifespan degradation function incorporated into the overall cost calculation. For algorithm design, a hybrid genetic algorithm ( HGA ) incorporating a post-processing refinement strategy is developed to efficiently solve this NP-hard model. A case study based on actual bus routes in Harbin demonstrates that the optimized strategy reduces the annualized total cost by 17.8%, decreases the required fleet size by 16.4%, and lowers maintenance costs by 16.8%. It is obvious that the dual battery configuration reduces the annualized degradation cost of battery ownership cost by 39.7 %, effectively extending the actual service life of battery assets. Sensitivity analysis shows that appropriately relaxing the state-of-charge (SOC) upper limit during moderate-temperature seasons (e.g., spring and autumn) can reduce cycling frequency, thereby better mitigating battery aging. Finally, the applicability of the model to larger-scale scenarios is discussed. This study provides a theoretical model and optimization tool for the sustainable operation of electric buses in cold regions, and plays an important role in formulating seasonal operation strategies for operators.

Optimization Strategy for Mobile Energy Storage Vehicle Dispatch in Highway Service Areas Considering Traffic Conditions



ZHANG Lina, XU Hongke, DAI Liang, WANG Dawei

2026, 54(3):  104-113.  doi:10.12141/j.issn.1000-565X.250101

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To address the issues of insufficient distribution network capacity in highways and the mismatch between renewable energy output and service area energy loads, utilizing mobile energy storage vehicles (MESVs) for energy mutual support between service areas can enhance renewable energy consumption. To enhance the real-time response capability of mobile energy storage, this paper proposes an MESV dispatch optimization strategy that considers traffic state, under the constraint of the average loss rate for electric vehicle (EV) battery swap services. First, leveraging the characteristics of free-flow traffic on highways, vehicle travel speed is modeled as a random variable following a truncated normal distribution and discretized into multiple speed intervals to represent different traffic states and their probabilities. Concurrently, considering the randomness of EV battery swap demand, a multi-state Bernoulli distribution is adopted to describe the swap demand within each time slot, establishing a transportation time cost model for MESVs and an energy state update model for battery swap stations (BSSs). Second, a Markov chain is constructed based on the traffic states between service areas and the energy states of BSSs to characterize the one-step transition probabilities of the system state. Building on this, a constrained Markov decision-making optimization model is then formulated, aiming to minimize the long-term average transportation time cost for mobile energy storage, subject to the constraints of the average loss rate for battery swap services. The model is solved to obtain optimal dispatch parameters and steady-state probability distributions. Simulations based on the actual operational parameters of NIO’s fourth-generation EV BSSs were conducted for validation. The results show that the proposed strategy exhibits a dual-threshold structure based on traffic condition and energy state, allowing adaptive adjustment of MESV dispatch frequency according to traffic conditions between service areas and the energy reserve levels of BSSs. Compared with greedy strategy and Q-learning method, the average transportation time cost was reduced by 17.23% and 8.89%, respectively, achieving an optimal trade-off between transportation cost and battery swap service performance while meeting service quality constraints.

Research on College Students' Travel Mode Choice Behavior Considering Peer Effect



ZHANG Rui, GE Yuhan

2026, 54(3):  114-126.  doi:10.12141/j.issn.1000-565X.250067

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Investigating the behavior of college students in choosing off-campus travel mode is of great significance for enhancing the off-campus travel environment, increasing their social engagement, and further promoting their physical and mental health development. However, previous studies neglected the influence of peer groups on individual travel mode choices. To address the lack of uniform standard for defining effective peer groups among college students, this study employs a roommate relationship quality scale to identify effective peer groups. Data on off-campus travel preferences across different travel scenarios are collected via questionnaire surveys. To further explore the role of peer effects in travel behavior decision-making, we introduce a peer matrix adjusted by relationship strength based on relational quality and psychological traits, and construct multiple network econometric linear models to capture peer effects. Potential endogeneity issues were addressed though combining fixed effects and two-stage least squares, and the optimal model for identifying and measuring peer effects is determined through model evaluation. Finally, based on the model calibration results, the influence and potential mechanisms of dormitory groups on individual college students’ off-campus travel mode choice are analyzed. The results show that, compared to gene-ralized econometric models and local aggregation models, the local average model incorporating relationship strength demonstrates greater robustness in identifying and measuring peer effects. As travel distance increases and time constraints relax, the influence of peer-related behaviors on college students’ individual travel mode choices-that is, the endogenous peer effect-decreases. Factors such as peers’ monthly living expenses, number of family members, possession of a driver’s license, openness and environmental awareness have significant exogenous peer effects on individual travel mode choices. Furthermore, extroverted individuals exhibit more pronounced conformity-based peer effects compared to introverted ones, and in entertainment contexts, open-minded individuals show stronger than conservative individuals. The research extends the boundaries of relevant theories and provides empirical evidence for the targeted guidance and group-specific interventions aimed at promoting low-carbon travel among college students.

Integrated Optimization Method for Cross-Line Operations of Subway Crew Scheduling



LI Hao, CHEN Shaokuan, SHI Mengtong, CHEN Ziqi

2026, 54(3):  127-134.  doi:10.12141/j.issn.1000-565X.250142

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This study addresses the collaborative optimization of crew scheduling and rostering for subway trains operating in a cross-line pattern. It investigates the impact of an integrated optimization approach, based on a cyclic roster system, on both the efficiency of crew plan formulation and the utilization rate of crew members. A spatio-temporal network is constructed based on the cyclic roster system to search for feasible crew duty segments and the sequence of shift connections within a roster cycle. A mathematical model is formulated with the dual objectives of minimizing the total number of crew shifts and minimizing idle time during duties. Constraints are established, including crew shift connection rules and shift feasibility requirements, to define permissible roster paths for crew members. These constraints ensure that the assigned duties within the roster cycle comply with the rules for connecting duty segments.A roster path search algorithm and an improved column generation algorithm are developed, considering factors such as sign-on/sign-off depots for shifts, shift types, roster cycle length, and the shift system design. These algorithms are employed to obtain optimal duty assignments within the roster cycle. Furthermore, a hybrid roster system is proposed, exploring the effects of mixing “four crews for three operational shifts” and “six crews for five operational shifts” systems on the crew schedule. The results show that, compared to the traditional separate “four crews for three shifts” and “six crews for five shifts” systems, the proposed hybrid system within the integrated optimization framework increases the average shift efficiency by 1.5 and 2.3 percentage points, respectively. It also reduces the number of deadhead segments by 12.18% and 24.45%, respectively. Compared to a sequential (two-stage) optimization approach, the integrated method improves average shift efficiency and crew utilization rate without increasing the number of shifts worked per crew member. Additionally, it reduces the total number of shifts required within the roster cycle and decreases the redundancy in covering duty segments. The integrated optimization approach with the hybrid roster system can adapt to flexibile roster cycles and the spatio-temporal distribution differences of duty segments across various lines. This adaptability is beneficial for ensuring balanced duty assignments for crew members and enhancing their overall utilization efficiency.

Materials Science & Technology

Application of X-Ray Computed Tomography in Frontier Research of Advanced Materials



CUI Jie, GUI Yan, ZHANG Chengyi, YANG Xianfeng

2026, 54(3):  135-147.  doi:10.12141/j.issn.1000-565X.250168

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As an advanced non-destructive three-dimensional (3D) imaging and detection technology, X-ray computed tomography (CT) enables the visualization and characterization of the internal structure of samples. This technique operates based on the interaction mechanism between X-rays and matter, collecting signals after X-rays penetrate the sample to form images. Then computer algorithms process the acquired tomographic images to reconstruct a 3D representation of the sample. With advantages such as high-density resolution and convenient digital processing, this technology has achieved significant breakthroughs in fields including medical diagnostics and industrial inspection. This paper focuses on the application of X-ray CT technology in the cutting-edge research field of advanced materials, particularly structural materials and new energy materials. It systematically sorts out the core principles of X-ray CT, including X-ray attenuation, signal conversion and 3D reconstruction. With an emphasis on materials science applications, this paper clarifies the role of CT in defect localization, damage tracking and microstructure quantification through case studies, such as weld defect detection in aerospace components, hidden defect identification in in electronic packaging solder joints, and porosity quantification in additive manufacturing materials. By examing its applications in studies on lithium-ion battery electrode evolution, fuel cell water management and metal anode dendrite tracking, this paper highlights the function of CT technology in revealing the relationship between material structure and electrochemical performance, optimizing device design and improving safety. Furthermore, this paper summarizes the advantages of CT Technology, including its non-destructiveness nature, 3D quantitative capability and dynamic tracking capacity. It also analyzes the bottlenecks such as low efficiency in nanoscale imaging and difficulties in data fusion. Finally, potential pathways for future advancements are proposed, including the development of novel detectors, artificial intelligence (AI)-assisted reconstruction and the integration of multiple complementary techniques. These insights aim to guide the deeper application of CT in enhancing the performance of structural materials and advancing the development of new energy materials. These discussions can provide directions for technological innovation for researchers and contribute to improving China’s independent R & D capabilities of high-end detection equipment.

Research Progress in 3D-Printed Noise-Reducing Concrete Materials



CHEN Yu, LUO Chuyu, ZHANG Yamei

2026, 54(3):  148-159.  doi:10.12141/j.issn.1000-565X.250372

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As environmental noise pollution becomes increasingly severe, concrete noise barriers have been widely used in noise control applications due to their excellent durability and cost-effectiveness. However, conventional concrete barriers still have shortcomings in sound absorption and insulation performance, making it difficult to meet the comprehensive demands of lightweight design, functional integration, and environmental sustainability simultaneously. Existing research has improved the noise reduction capability of concrete at the material level by incorporating components such as foaming agents and porous lightweight aggregates into cement-based systems. By adjusting material density, porosity, and pore connectivity, the pore structure and acoustic energy dissipation mechanism of the material have been optimized. On this basis, 3D printing provides a new way to achieve complex geometric designs, lightweight manufacturing, and structural personalization for noise-reducing elements. The directional interconnected pore networks and unique interlayer interface characteristics formed during the printing process can effectively extend the sound wave propagation paths and enhance energy dissipation, thus achieving acoustic performance optimization at the structural level. This article systematically summarizes the key factors and their control strategies affecting the noise reduction performance of concrete, focuses on analyzing the mechanisms by which 3D printing processes regulate pore distribution, interlayer interfaces, and surface textures, and summarizes relevant engineering application cases. Existing research indicates that properly designed 3D-printed concrete structure exhi-bits significant advantages in mid-to-low-frequency sound absorption, while further targeted optimization of acoustic performance can be achieved by coordinated regulation of geometric configurations and surface textures. In addition, 3D-printed noise-reducing concrete holds significant application potential in road noise barriers and architectural acoustics, though it still faces technical challenges such as material printability, interlayer bond strength and long-term service performance. Future research should focus on the utilization of green and low-carbon raw mate-rials, multi-scale structural design, and durability assessment to promote the high-performance development and engineering application of 3D-printed noise-reducing concrete.

Durability Analysis of Load-Bearing Recycled Concrete Blocks in Severe Cold Regions of Qinghai-Tibet



QI Yunpeng, WANG Qiusheng, LI Zhiyi, XIONG Yijun

2026, 54(3):  160-171.  doi:10.12141/j.issn.1000-565X.250120

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To realize resource utilization of solid waste and improve the performance of recycled concrete blocks in severe cold regions of the Qinghai-Tibet Plateau, this study investigates the effects of recycled fine aggregates and active supplementary cementitious materials on the impermeability, water resistance and frost resistance of load-bearing concrete blocks, based on 100% replacement of coarse aggregates with recycled aggregates. The microstructure of recycled concrete was analyzed by scanning electron microscopy (SEM) and nuclear magnetic resonance (NMR). A life cycle assessment (LCA) approach was employed to quantitatively evaluate material performance, costs and carbon emissions. The results show that the permeability and water absorption of load-bearing recycled concrete blocks increase with the increase of the proportion of recycled fine aggregates, while they decrease first and then increase with a reduction in the fly ash-to-slag blend ratio. Conversely, frost resistance and the softening coefficient exhibit the opposite trend. Under the condition of 100% recycled coarse aggregate replacement and a fly ash-to-slag blend ratio of 3∶1, the compressive strength of the blocks reached 11.77 MPa, flexural strength was 3.89 MPa, softening coefficient was 0.99, water absorption was 0.7%, mass loss after 50 freeze-thaw cycles was 2.2%, and the loss rates of compressive and flexural strength were 10.2% and 13.9%, respectively. These properties meet the load-bearing and durability requirements for severe cold regions, making this mix ratio the recommended formulation. Microscopic analysis shows that alkali activation promotes secondary hydration in the composite cementitious materials, generating additional hydration products that fill internal pores, thereby enhancing the densification of the recycled concrete. However, with the increasing freeze-thaw cycles, the number of internal pores gradually increases, with micropores and mesopores envolving into macropores and cracks, leading to performance degradation. Based on the performance-cost-carbon emission analysis, using 100% recycled coarse aggregates alone is unfavorable for carbon reduction. In contrast, the recommended mix proportion incorporating both recycled aggregates and active SCMs demonstrates the optimal comprehensive benefits, achieving a carbon reduction rate of 31.03%.

Effect of Esterification-Modified Lignin on Superhydrophobic Performance of Packaging Paper



LIU Chao, ZHANG Haoyu, WANG Meng, HE Yuqian, HE Congying, HOU Yi, XIAO Huining

2026, 54(3):  172-184.  doi:10.12141/j.issn.1000-565X.250257

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The presence of abundant hydrophilic hydroxyl groups on the surface of paper fibers limits their application in barrier packaging. Currently, petroleum-based materials commonly used to enhance the barrier performance of paper are often non-biodegradable. In contrast, alkali lignin offers unique advantages for the preparation of barrier packaging materials due to its inherent biodegradability, hydrophobicity, and flame-retardant properties. To investigate the influence of alkali lignin on the hydrophobic performance of packaging paper, this study used alkali lignin (A-Lig) as a raw material and modified it via esterification with palmitoyl chloride and stearoyl chloride, synthesizing lignin palmitate (Lig-P) and lignin stearate (Lig-S). Subsequently, coating solutions were formulated based on A-Lig, Lig-P, and Lig-S, and applied to base paper using a cross-spraying method to fabricate a series of lignin-coated papers. The chemical structures, micro-morphologies, and thermal properties of A-Lig, Lig-P, and Lig-S were systematically characterized using Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), nuclear magnetic resonance (NMR), scanning electron microscopy (SEM), and thermogravimetric analysis (TGA). The microstructure, hydrophobic properties, and mechanical strength of the base paper and coated papers were evaluated through measurement of static water contact angle, rolling angle, water absorption, water stability on the paper surface, self-cleaning performance, and mechanical properties. The results show that the hydroxyl groups in lignin are effectively substituted after esterification modification, with successful grafting of aliphatic chains. The relative content of C—O bonds decreases, and the thermal stability is slightly reduced. The static water contact angles of the base paper (Bas-P), A-Lig-coated paper (Lig-P1), Lig-P-coated paper (Lig-P2), and Lig-S-coated paper (Lig-P3) are 45.9°, 83.5°, 150.8°, and 151.6°, respectively. Meanwhile, the rolling angles of Lig-P2 and Lig-P3 are 9.3° and 3.5°, achieving a superhydrophobic effect. The micro-surfaces of both Lig-P2 and Lig-P3 exhibit a petal-like micro-nano rough structure, significantly reduced water absorption, and demonstrate excellent water stability and self-cleaning performance. Compared to Bas-P, Lig-P2 and Lig-P3 exhibit a slight decrease in tensile strength but a pronounced increase in elongation at break, indicating improved flexibility of the paper.