International Core Journal of Engineering
International Core Journal of Engineering is an international journal to promote all fields of Engineering, it considers original and timely contributions containing new results in mechanical engineering, electrical engineering, civil engineering, chemical engineering, etc. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
Failure Mechanism of Airtightness Detection Caused by Corrosion-Induced Clogging in Power Battery Liquid Cooling Plates
Leakage detection of coolant is critical for the safe operation and maintenance of power battery liquid cooling systems. However, a false‑negative phenomenon has frequently been observed in engineering practice: dry‑air airtightness tests indicate acceptable sealing performance even though corrosion-induced perforation has already occurred. To reveal the underlying mechanism, a multi‑physics coupling model integrating electrochemical corrosion, ionic transport, precipitation kinetics, and porous seepage was developed based on an actual field failure case. Accelerated galvanic corrosion experiments were further conducted to validate the corrosion morphology and failure mode. The results demonstrate that galvanic corrosion between the aluminum liquid cooling plate and the steel battery enclosure leads to the formation of an occluded corrosion cell. Hydrolysis of Al3+ ions induces an autocatalytic “dissolution–acidification–accelerated dissolution” cycle, which continuously promotes localized corrosion growth. Meanwhile, Al3+ and OH− ions accumulate near the defect channel and lead to preferential precipitation of Al(OH)3​ corrosion products. The local deposition density reaches approximately 1321 kg/m3 , causing the permeability of the leakage channel to decrease from 10−11 m2 to below 10−15 m2. Consequently, the seepage capacity of the leakage path is essentially lost. To quantitatively evaluate the risk of leakage misdetection, a False‑Negative Index (FNI) is proposed. When FNI exceeds 0.99, conventional dry‑air airtightness testing becomes incapable of identifying existing leakage defects. Furthermore, three‑dimensional airtightness simulations show good agreement with experimental measurements, with pressure‑drop deviations below 8%. The proposed mechanism successfully explains the contradiction between actual corrosion perforation and qualified airtightness test results. The findings provide theoretical support for leakage diagnosis, corrosion prevention, and safety‑oriented design of battery liquid cooling systems.
Liquid Cooling Plate; Galvanic Corrosion; Multi-Physics Simulation; Corrosion Product Deposition; Failure of Airtightness Detection.
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Optimal Design of Crankshaft for a Single-cylinder Diesel Engine based on ANSYS
The present paper discusses the critical prerequisites to improve lightweight design and structural reliability of the crankshaft of the single-cylinder diesel engines. Using ANSYS Workbench finite element analysis software, a three-dimensional solid model of the crankshaft is created and then static finite element analysis is done to determine the stress distribution when subjected to working conditions under real-life conditions. As shown by the findings, the highest value of the equivalent stress of the original crankshaft is 100.16 MPa and the least value of the safety factor is only 2.49. Moreover, there is a high level of stress concentration where the crankshaft meets both the main journal and crank pin, which can cause fatigue failure after many operating hours and negatively impact the lifespan of the diesel engine. These problems are solved with a specific optimization strategy, taking into account the features of stress distribution. Stress concentration regions are relieved using transition fillets to reduce local stress fluctuations. Also, the stress relief slots are provided at the crank arm to distribute the concentrated stress. At the same time, the diameter of the crank pin and main journal was optimized to make light weight at the cost of structural integrity. After optimization, the static finite element analysis was carried out to confirm the outcomes. It was found that the maximum equivalent stress of the crankshaft has been minimized to 87.713 MPa and the smallest safety factor was raised to 2.85, which greatly improved the reliability of structures. Simultaneously, the total mass of the crankshaft was decreased by 9.3% thus accomplishing the aim of light weighting. This study successfully resolves the trade-off between lightweight design and reliability of the crankshaft of the single-cylinder diesel engine by combining finite element analysis with structural optimization. The suggested optimization scheme is scientifically justified and feasible, and can be used as a dependable engineering guide to the best design of a crankshaft of a single-cylinder diesel engine, and it can be regarded as very valuable to improve the performance of diesel engines in general.
Crankshaft; Finite Element; Optimal Design; Statics; Light Weighting.
Review on the Current Situation and Shortcoming Remediation Strategies of Urban Excess Rainwater Storage and Drainage System Construction
Under the background of global climate change, extreme rainfall occurs frequently. Coupled with the increase in land hardening rate during urbanization, the problem of urban waterlogging has become increasingly prominent, seriously threatening urban safety and residents' lives. Constructing an efficient urban excess rainwater storage and drainage system is the key to preventing waterlogging and implementing water safety policies, and conducting relevant research has important theoretical and practical significance. Taking the current situation and shortcoming remediation strategies of urban excess rainwater storage and drainage system construction as the research theme, this paper adopts a variety of research methods suitable for review papers to ensure the rigor of the research process and the practicality of the conclusions: through the literature research method, systematically retrieve relevant domestic and foreign journal papers and policy norms, comprehensively sort out the research context and existing deficiencies of urban excess rainwater storage and drainage technologies, shortcoming identification and remediation strategies; use the inductive and deductive method to summarize the core laws of existing research and derive an adaptive research idea combined with the research focus of this paper; use the comparative research method to compare the applicability and limitations of different storage and drainage technologies and shortcoming remediation strategies, and learn from mature research experience; supplemented by the normative analysis method to conform to relevant national and local policy standards, providing compliance support for the research content. The research finds that the current system in China has formed a full-chain technical layout, but there are still prominent shortcomings in multiple dimensions. The targeted remediation strategies proposed in this paper can effectively make up for the construction shortcomings and improve the system operation efficiency. This research can provide practical reference for the scientific construction and efficient operation and maintenance of urban excess rainwater storage and drainage systems in China, and help the construction of resilient cities.
Urban Waterlogging; Excessive Rainwater; Storage and Drainage Systems; Current Technological Status; Addressing Shortcomings.
A Review and Prospect of Optimal Scheduling for Integrated Energy Systems
Optimal scheduling of integrated energy systems (IESs) is a key technical approach for coordinating energy supply and demand, improving multi-energy utilisation efficiency and supporting low-carbon energy transition. To address the fragmented coverage of existing studies, this paper reviews the research progress of IES optimal scheduling from five aspects: theoretical foundations and modelling frameworks, optimisation objectives and constraint systems, core optimisation algorithms, demand response and multi-agent interaction mechanisms, and uncertainty handling methods. The review shows that IES modelling has gradually developed from physical architecture description to energy-hub, microgrid and virtual-power-plant-based aggregation frameworks. Optimisation objectives have evolved from single economic cost minimisation to coordinated economic, environmental and technical performance. Algorithmic research has moved from classical mathematical programming to intelligent optimisation, game-theoretic modelling and data-driven hybrid methods. Integrated demand response and multi-agent coordination have become important sources of operational flexibility, while stochastic optimisation, robust optimisation and distributionally robust optimisation provide complementary tools for handling renewable generation, load, network and market uncertainties. However, current research still faces challenges in dynamic modelling, real-time scheduling, multi-agent benefit coordination, uncertainty modelling efficiency, policy-market coupling and life-cycle optimisation. Future research should further integrate digital twins, blockchain, machine learning and scheduling theory to improve the engineering applicability of IES optimal scheduling.
Integrated Energy System; Optimal Scheduling; Energy Hub; Demand Response; Game Theory; Uncertainty Handling.
Graphene-Based Composite Coatings for Ship Anticorrosion and Antifouling: A Review on Progress and Prospects
The marine environment, on account of its elevated salinity, humidity, and UV radiation, brings about severe corrosion and biofouling to ships and offshore structures. Traditional coatings encounter difficulties in fulfilling the requirements of eco-sustainability, durability, and multifunctional synergy. Graphene, by virtue of its extraordinarily high specific surface area, chemical stability, as well as gas barrier properties, has emerged as a pivotal material for marine protection. The present review carries out a systematic consolidation of the recent advances in graphene-based composite coatings for ship anticorrosion and antifouling. It provides an elaboration on the “labyrinth effect” barrier mechanism of graphene and its integration with low-surface-energy components, for instance, FOTS and LDH. The key bottlenecks-namely functional isolation, weak interfacial adhesion, and elevated scaling costs-are subjected to a critical examination. The potential of a gradient functional design that incorporates an anticorrosion base combined with an antifouling top layer for integrated synergistic protection is explored, and a performance evaluation framework that encompasses impedance, contact angle, and biofouling attachment is put forward. Future directions are outlined, covering interfacial reinforcement, gradient synergy optimization, cost-effective scalable processes, as well as standardized evaluation criteria, thereby offering insights for engineering applications.
Graphene-Based Composite Coating; Ship Anticorrosion; Ship Antifouling; Integrated Synergistic Design; Interfacial Reinforcement.
YOLO-LMC: An Enhanced Framework for Robust Object Detection
Object detection in hazy scenes holds significant real-world implications, particularly in areas such as intelligent transportation and environmental monitoring. However, image quality degradation caused by haze inherently weakens the feature extraction capabilities of neural network models, leading to reduced accuracy. To address this challenge, this paper proposes YOLO-LMC (YOLO with Latent-space Metric Constraints), a novel architecture that integrates metric learning within the latent space optimization of object detection. Specifically, a positive sample assignment strategy is designed to filter high-confidence features within the latent space. This strategy, coupled with a cosine distance metric loss function based on class centers, effectively compels intra-class samples to form compact distributions in the latent space while ensuring distinct inter-class separability. Experimen-tal validation on the Foggy-Cityscapes dataset demonstrates that the proposed method significantly outperforms other object detection enhancement approaches, achieving an impressive at least 8.6% improvement in mAP.
Hazy Scenes; Object Detection; YOLO-LMC; YOLO-v8; Latent Space Metric Learning; Positive Sample Assignment.
Low-Altitude UAVs and Urban Rail Transit Join Hands to Solve the Last-Mile Problem in Urban Logistics Distribution
Taking Tianjin Metro Line 2 as an example, this paper explores an innovative model for low-altitude UAVs and urban rail transit to collaboratively address the "last-mile" issue in urban terminal logistics distribution. The study proposes the "intelligent rail-UAV collaborative distribution" model, with innovations including: revealing the temporal and spatial complementarity between the "off-peak freight periods" of rail transit and the "point-to-point direct delivery" of UAVs; constructing a network architecture of "suburban distribution center - rail transit station - UAV site - community station"; and designing a joint transportation process: goods are transported to depots (such as Haojixing Logistics Park) via the outer ring road, packaged and then delivered to urban district stations by rail transit, and finally distributed to community stations by UAVs. The temporal-spatial relay model utilizes off-peak hours, non-operating periods, and peak-shifting with passengers to build a three-dimensional network encompassing underground (rail transit), ground (external transportation), and low-altitude (UAVs) layers. For site selection calculation, Tianjin Metro Line 2 is taken as a case, and initial operating stations are determined based on calculated scores, with relevant operational ideas proposed.
Low-altitude; Rail Transit; Collaborative Distribution; Last-Mile.
Design and Simulation of Mechatronic System for Additive Manufacturing of Recyclable Nylon
This paper focuses on additive manufacturing of recyclable nylon and designs a mechatronic system. In terms of mechanical transmission, high-precision screws and linear guides are selected, combined with precision motor drives to ensure that the print head moves accurately in the X, Y, and Z axes, with a positioning accuracy of ±0.05mm. The electrical control is based on PLC, and the motor parameters are adjusted in real time based on sensor feedback. The multi-sensor fusion detection system uses the Kalman filter algorithm to integrate temperature, pressure, displacement and other data, and the data accuracy is improved by 20%. The adaptive path planning algorithm optimizes the path in real time based on the material circulation state, shortens the printing path length by 15%, and improves the uniformity of material deposition by 18%. Through the hardware-in-the-loop simulation experimental platform test, the material recovery efficiency of the new algorithm is 25% higher than that of the traditional algorithm, reaching 85%. The printing accuracy test shows that the dimensional accuracy error is controlled at ±0.1mm, and the shape accuracy deviation is reduced by 22%. In the mechanical properties test, the tensile strength is increased by 12% and the bending strength is increased by 10%. The experimental data fully verified the efficiency and superiority of the system and algorithm in the additive manufacturing of recyclable nylon.
Recyclable Nylon; Additive Manufacturing; Mechatronics; Dynamic Adaptive Path Planning Algorithm; Experimental Verification.
A Review of Low-Temperature Heating Technologies for Lithium-Ion Batteries in New Energy Vehicles
With prominent features such as high specific power, low self-discharge rate, high energy density, and long cycle life, lithium-ion batteries have become the core energy storage components supporting the operation of power systems in new energy vehicles. However, in environments with low temperatures, this leads to a notable decrease in maximum power output and energy availability, and it also readily initiates lithium plating on the negative electrode during such charging, drastically reducing the battery's capacity retention and lifespan. Consequently, the effective preheating of lithium-ion batteries in environments with low temperatures has emerged as a key technical aid for the consistent and dependable functioning of new energy vehicles in colder areas. This paper systematically sorts out the low-temperature heating technology of lithium-ion batteries and analyzes the applicable scenarios and limitations of various technologies. In closing, this study systematically delineates the pressing technical bottlenecks demanding immediate breakthroughs within the domain of low-temperature heating technologies for lithium-ion batteries, and presents an outlook on their future evolution pathways and practical application prospects.
New Energy Vehicles; Lithium-ion Batteries; Low-temperature Heating; External Heating; Internal Heating; Thermal Management System.
Distribution Network Reconfiguration with Multi-Objective Optimization of Distributed Generation based on Chance Constrained Programming
For distribution network with distributed generation, reducing the network loss has always been the focus of research. With increasing proportion of distributed generation in the distribution network, how to improve the power consumption capacity of the distribution network which consider the randomness of distributed generation is increasingly concerned. Firstly, A multi-objective optimization model for distribution network reconfiguration based on maximum consumption of distributed generation and minimum network loss is established in this paper, then, uncertainty of photovoltaic and wind power output are handled by chance-constrained programming model. Subsequently, an improved NSGA-II algorithm is proposed to solve the model which combined with a global Pareto set maintainance strategy. Finally, Taking IEEE 33-node system as an example to compare and analyze multi-objective and multi-scenario optimization. The results show that the model built in this paper can effectively improve the ability of consumption of distributed generation while reducing network losses.
Distribution Network Reconfiguration; Distributed Generation; Chance-Constrained Programming; NSGA-II.
A Review of Research on Electromagnetic Suspension Technology for New Energy Vehicles
Driven by the global "double carbon" goal and the transformation of the automobile industry towards electrification and intelligence, the chassis system of new energy vehicles has changed from achieving a "balance of handling smoothness and stability" to meeting the needs of "good driving performance, intelligence, and high energy utilization". Relying on its core advantages such as fast response speed, wide adjustable damping range, and high energy recovery efficiency, electromagnetic suspension has become a key technology to resolve the contradiction between long endurance and high performance of new energy vehicles. This paper conducts a systematic review of the domestic and foreign research progress of electromagnetic suspension technology for new energy vehicles, explores its structural types, performance optimization and control strategies, and quantitatively analyzes the impact of structural characteristics on the transmission efficiency and spatial compatibility of different topological configurations; analyzes the key approaches to performance optimization from the three dimensions of electromagnetic characteristics, mechanical characteristics, and thermal characteristics; and compares the control effects and engineering applicability of classic control, modern control, and intelligent control strategies. Finally, the shortcomings of the current technology are clarified, and the future development of related fields is prospected, with a view to providing reference for the technological innovation and industrial application of electromagnetic suspension for new energy vehicles.
New Energy Vehicles; Electromagnetic Suspension; Structural Design; Performance Optimization; Energy Recovery.
Research on Remanufacturing Reverse Supply Chain Optimization Strategies Driven by Enhanced Carbon Reduction Capability
The reverse supply chain is a key driver of the circular economy, and its low-carbon operation plays a vital role in achieving the “Dual Carbon” goals. To address this issue, this study takes the remanufacturing rate ( ) as a core variable, constructs a two-tier remanufacturing reverse supply chain system dynamics model. The model analyzes the impact mechanism of on the carbon emissions of supply chain members. By enhancing carbon emission reduction capability, it investigates the phased characteristics of carbon reduction investment strategies to optimize the low-carbon operation of the reverse supply chain. Built on the Vensim platform, the model with carbon emission features from processes including treatment, disassembly, retreatment, and remanufacturing. Simulation results reveal that collector' carbon emissions decline as grows, while remanufacturer' emissions peak at moderate levels. When carbon reduction investments are introduced, the optimal strategies of the collector and the remanufacturer present phased characteristics with distinct critical thresholds. The study offers theoretical and quantitative support for corporate carbon reduction decisions and differentiated policy-making in reverse supply chains.
Reverse Supply Chain; Carbon Emission; System Dynamics; Remanufacturing Rate; Carbon Emission Reductionintroduction.
A Theoretical Framework for High-Precision and Simultaneous Localization and Mapping Through Multi-Sensor Fusion
Simultaneous Localization and Mapping (SLAM) always contains a core challenge for autonomous systems, that is, single-sensor architectures are often not enough in complex real-world environments. We propose a theoretical framework that combines stereo vision, LiDAR, IMU, and GPS, achieving high precision and robustness by adding their complementary strengths. The framework systematically addresses sensor fusion, state estimation, loop closure, and map representation, and also lays out a perfect experimental blueprint. The challenging aim is to orient SLAM research toward practical autonomy, with a focus on robustness, accuracy, and computational efficiency.
SLAM; Multi-Sensor Fusion; Graph Optimization; State Estimation; Robust Perception; Sensor Calibration; UAV.
Intelligent Transformation and Industrial Application of Motor Rotor Magnet Insertion Equipment in the Context of High-End Manufacturing
Magnet steel assembly of motor rotor is a key process in the manufacture of permanent magnet motor. Its slot posture, assembly position and compaction state are related to the machining organization and quality control of the subsequent rotor assembly. Traditional magnet insertion equipment relies on manual operation, single serial and decentralized setting, which is easily disturbed by personnel skills, magnet steel characteristics and model change, and it is difficult to meet the requirements of high-end manufacturing for stability, coordinated beat and data traceability. In this paper, aiming at the magnet insertion equipment of motor rotor, combined with the functional relationship of its core mechanism, a modular intelligent transformation scheme is proposed. The research focuses on equipment layout, material grabbing, station interlocking and data management, and forms the automatic operation logic driven by process state. This scheme retains the necessary manual operation links, and hands over the highly repetitive and strict positioning procedures to the automatic mechanism for execution, which provides a reference for upgrading the existing motor assembly equipment.
High-end Manufacturing; Motor Rotor; Magnet Steel Insertion; Intelligent Transformation; Industrial Robot.
Research Progress on Fatigue Damage Behavior and Performance Improvement of High Strength Steel Materials at High Temperature
The fatigue failure of high-strength steel in high temperature environment is a bottleneck problem restricting the reliability of key equipment such as nuclear power and aero-engine. In this paper, the microscopic mechanism of high temperature fatigue damage, life prediction models and performance improvement strategies are reviewed, with emphasis on cyclic softening/hardening behavior, martensite slab coarsening and crack initiation mechanisms. The applicability of macroscopic phenomenological model, crystal plasticity model and defect fracture mechanics model is compared and analyzed. The ways of performance optimization are discussed from three aspects: alloy design, surface strengthening and heat treatment technology, which provide theoretical reference for anti-fatigue design of high strength steel.
High Strength Steel; High Temperature Fatigue; Micromechanism; Life Prediction; Performance Optimization.
Research on Tree Carbon Storage Estimation Method based on Point Cloud and Voxel Modeling
Since global climate change has become more pronounced, accurate estimation of forest carbon stocks has become a vital part of carbon cycle research and carbon neutrality strategies. However, it is well known that traditional methods for estimating tree carbon stocks rely mainly on empirical models and manual measurements, both of which have inherent problems of low accuracy and low efficiency. Therefore, recent developments in 3D laser scanning offer promising solutions.since LiDAR technology has provided new and effective methods for precise forest structure measurement, this paper makes a very clear and logical use of high-density point cloud data acquired by the Oslight Intelligent R8 backpack SLAM laser scanning system for vegetation on the campus of Liaoning University of Science and Technology, and then applies a voxelization modeling approach to compute tree volumes and estimate carbon stocks.Using Lidar360 software for individual tree segmentation, parameter extraction, and volume calculation, the results were systematically compared with those from MATLAB voxelization algorithms, and it was clearly and convincingly shown that a voxel resolution of 5 cm gives the most accurate tree volume measurements. Therefore, the paper naturally leads into discussing the advantages of voxelization algorithms in representing complex tree structures while maintaining computational efficiency, making it an excellent foundation for a high-precision, automated method of urban forest carbon sink monitoring. It also provides a solid technical reference for high-precision carbon stock estimation from point cloud data.
Point Cloud; Voxelization; Tree Carbon Storage; SLAM Laser Scanning; Lidar360.
Optimization Design of Steam Turbine Rotors based on Thermo-Structural Coupling
This study focused on the original model of a first-stage shrouded steam turbine blade and investigated its mechanical behavior under a multiphysics environment through prestressed modal analysis, transient thermo-mechanical coupling analysis, and long-term static creep analysis. The results showed that the system exhibited nodal-diameter vibration, with a first-order natural frequency of 588.88 Hz, satisfying the requirements for dynamic stability. Under steady-state conditions, the maximum equivalent stress reached 345.09 MPa and was mainly located in the shroud connection region. After service for 6.8×108 s, the equivalent creep strain at the blade root accumulated to 0.19073 mm. This study identified the structural strength bottlenecks and deformation characteristics of the blade, providing a reference for the safe service of this type of turbine blade.
Steam Turbine Blade; Original Model; Prestressed Modal Analysis; Thermo-Mechanical Coupling; Static Creep; Stress Concentration; Service Safety.
An Optimization Model for a Two-Echelon Remanufacturing Reverse Supply Chain with Carbon Emission Reduction Consideration
To address the high level of carbon emissions in the transportation stage of remanufacturing reverse supply chains, this study develops a two-echelon network optimization model that incorporates multiple transportation modes. Two mixed-integer linear programming (MILP) models are formulated with the objectives of minimizing total cost and carbon emissions, respectively. A case study based on the Yangtze River Delta region is conducted to compare road transportation with multimodal transportation. The results indicate that under a single road transportation mode, the cost-minimization and emission-minimization objectives are aligned. In contrast, multimodal transportation achieves approximately a 48.9% reduction in carbon emissions with only a marginal increase in total cost. These findings demonstrate that multimodal transport provides an effective pathway for low-carbon optimization in remanufacturing supply chains.
Remanufacturing Reverse Supply Chain; Multimodal Transportation; Carbon Emissions; Network Optimization.
A Review of Optimization Methods for Sheet Metal Forming Processes
Sheet metal forming is a core manufacturing process in aerospace, automotive, and marine industries, where optimization of process parameters directly impacts product quality and production costs. This paper systematically reviews research progress in optimizing sheet metal forming process parameters, examining the current application status of traditional optimization methods, numerical simulation techniques, and intelligent optimization algorithms in this field. It focuses on exploring the principles and characteristics of typical optimization techniques such as finite element simulation, response surface methodology, genetic algorithms, neural networks, and multi-objective optimization. Furthermore, it provides an outlook on cutting-edge research directions like online monitoring and real-time control. Research indicates that intelligent optimization methods integrating digital twins, artificial intelligence, and big data will become a significant development trend in sheet metal forming process parameter optimization.
Sheet Metal Forming; Process Parameter Optimization; Numerical Simulation; Intelligent Algorithm; Multi-Objective Optimization.
Grid Planning Considering the Development Stages of Distributed PV and Electric Vehicles
Under the "dual carbon" goal, distributed photovoltaics (DPV) and electric vehicles (EV) have developed rapidly in China and have now become an important part of the power system. Their impact on the distribution grid, including voltage distribution, load characteristics, power flow and equipment utilisation, there are significant differences in different stages of development. This study considers the process of transforming distributed photovoltaics from "spontanetary self-use, surplus electricity on the Internet" to high penetration rate reverse power flow, and the evolution of electric vehicles from disorderly charging to intelligent coordinated charging and finally developing into V2G mode. On this basis, a multi-stage power grid planning framework is proposed, which includes the generation of uncertain scenarios, coordinated "network-source-load-storage" optimisation model and elastic network structures suitable for different penetration rates. The method was tested on the 33-node power distribution system and the improved 123-node feeder system. The results showed that compared with traditional static planning, the method reduced the whole life cycle cost by about 15-28%, alleviated the problems of voltage over-limit and equipment overload, and postponed the investment in power grid upgrade by 2-4 years. In addition, the sensitivity analysis of the participation rate of electric vehicles, the cost of energy storage and the elasticity of time-shring electricity prices is also carried out.
Grid Planning; Distributed Photovoltaics; Electric Vehicles; Development Stages; V2G; Coordinated Optimization; Life-cycle Cost.
Key Technologies for Online Joint Optimization of Transmission-Distribution Coordination for Reactive Power Reserve Requirements in New Power Systems
With the rapid development of high-proportion renewable energy power systems, reactive power and voltage regulation faces multiple challenges including intensified source-load bilateral uncertainty and deepened coupling between transmission and distribution networks. The traditional independent optimization mode for transmission and distribution networks can hardly satisfy the requirements of global voltage stability and economic operation. This paper proposes a transmission-distribution collaborative online joint optimization method for reactive power reserve demand in new-type power systems. First, the spatio-temporal distribution characteristics of reactive power reserve demand under high renewable energy penetration are analyzed, and a reactive power reserve demand assessment model considering voltage stability margin (VSM) constraints is established. Second, a transmission-distribution collaborative multi-objective joint optimization model is constructed, taking system active power loss, reactive power reserve cost, voltage deviation, and voltage stability margin penalty as optimization objectives. Then, the alternating direction method of multipliers (ADMM) is adopted to achieve decomposition-coordination solving between transmission and distribution networks, meeting the computational efficiency requirements for online applications. Finally, case studies are carried out based on a coupled test system consisting of a modified IEEE 30-bus transmission network and an IEEE 33-bus distribution network. The results demonstrate that compared with independent optimization strategies, the proposed method can reduce network losses by 21.6%, improve voltage stability margin by 83.3%, and the computing time is only 20.8% of that of centralized optimization, verifying the effectiveness and practicality of the method.
New-type Power System; Reactive Power Reserve; Transmission-distribution Collaboration; Online Optimization; Alternating Direction Method of Multipliers.
Efficacy and Mechanism of Temperature-Regulated ECT-MFC for Heavy Metal Removal from Mine Water
Nowadays sustainable development has gained widespread attention, making people pay more attention to water pollution problems. However, fast industrial development makes water pollution increasingly serious, especially heavy metal pollution in water. Traditional wastewater treatment methods cost much to operate and often produce secondary pollution with poor removal results. Microbial fuel cells can recycle electricity while cleaning sewage, so they have obvious comprehensive advantages. We applied a modified eight-chamber ECT-MFC to treat composite heavy metal wastewater in this test, and the device maintained stable operation and great treatment performance. Temperature controls microbial metabolism and electrode reaction efficiency, which largely changes the overall removal effect. Test results show low temperatures strongly inhibit system reactions; high temperatures raise the initial removal speed but damage microbial activity and cut treatment efficiency after long-term running. The study confirmed that the optimal synergistic effect of reactions occurs under medium-temperature conditions (30°C), and these findings can provide a reference for temperature control in practical equipment applications.By comparing all test working conditions, medium temperature provides the optimum reaction synergy and counts as the best running temperature for the reactor, and all conclusions from this research can offer practical support for field temperature control of such devices.
Microbial Fuel Cell; Eight-chamber ECT-MFC; Heavy Metal Wastewater; Temperature Regulation; Wastewater Treatment.
Key Structural Design of a Downhole Variable-Angle Hollow-Shaft Turbine Generator
To address the insufficient wide-flow adaptability, limited structural integration, and restricted tool passability of downhole turbine power generation systems, a downhole variable-angle hollow-shaft turbine generator system is proposed. The system consists of a guide vane, a variable-angle turbine, a hypoid gear adjustment mechanism, and a hollow-shaft outer-rotor permanent magnet generator. Based on blade design theory, velocity triangle theory, and isentropic flow theory, the key parameters of the guide vane and turbine are determined, and 14 attack-angle models ranging from 0° to 90° are established. A hypoid gear mechanism is designed to realize synchronous angle adjustment of multiple blades, and a hollow-shaft outer-rotor permanent magnet generator is developed to achieve compact integration of the turbine, transmission mechanism, and power generation unit, providing a reference for the optimization and engineering application of downhole turbine power generation devices.
Downhole Turbine Power Generation; Variable-Angle Turbine; Hollow Shaft; Hypoid Gear; Outer-Rotor Permanent Magnet Generato.
Data-Driven Development of Deep Eutectic Solvents: A Review of Machine Learning in Property Prediction and Rational Design
Deep Eutectic Solvents (DESs) represent a novel class of emerging green solvents formed through hydrogen-bonding interactions between hydrogen bond donors (HBDs) and hydrogen bond acceptors (HBAs). Boasting core advantages such as facile synthesis, low cost, environmental benignity, and exceptional structural and functional designability, DESs have demonstrated substantial application value and industrial potential across various sectors, including natural product extraction, biomass valorization, hydrometallurgy, acidic gas capture, and electrochemical energy storage. However, several fundamental scientific and technical bottlenecks-including the combinatorial explosion of the chemical design space arising from vast HBD/HBA combinations, the elusive structure-property relationships between microscopic intermolecular interactions and macroscopic physicochemical properties, and the challenges in synergistically optimizing multiple performance indicators in complex application scenarios-have rendered traditional "trial-and-error" R&D models inadequate for the targeted rational design of DESs, thereby significantly impeding their large-scale industrialization. As a data-driven intelligent research paradigm, machine learning (ML) provides a transformative technical route to address these challenges by leveraging its robust capabilities in high-dimensional feature mining, complex non-linear relationship fitting, and global optimization. This review systematically delineates the latest domestic and international advancements in ML-enabled DES research. First, it elucidates the core principles and mainstream methodologies for molecular descriptor selection and high-quality dataset construction in DES property prediction. Second, it summarizes model architectures, algorithmic optimizations, and predictive performance of ML regarding the thermophysical properties as well as chemical and biological activities of DESs. Subsequently, it highlights technical implementations and application outcomes of ML in the intelligent prediction of DES formation, application-oriented reverse design, and multi-objective optimization of extraction and separation processes. Finally, the review provides an in-depth analysis of the core challenges currently facing the field-such as the lack of standardized datasets, insufficient model interpretability, and weak mechanistic insights-while offering perspectives on future research directions. This review aims to provide a systematic theoretical framework and research roadmap for the intelligent rational design, precise property modulation, and industrial application of DESs, facilitating the paradigm shift in green solvent development from traditional empirical trial-and-error toward a data-driven intelligent paradigm.
Deep Eutectic Solvents (DES); Machine Learning; Property Prediction; Inverse Design; Multi-objective Optimization; Data-driven.
Simulation Analysis of Axle Load Transfer During Actual Traction and Braking
Driven by the Belt and Road Initiative, the demand for efficient transport has promoted the development of multimodal transport, particularly road-rail intermodal transport, to overcome the drawbacks of traditional railway freight. This paper investigates the axle load transfer problem of a specific type of road-rail combined vehicle during operation. Unlike existing studies that mostly employ idealized or simplified traction/braking conditions, this study innovatively introduces real traction and braking curves into the dynamic model, thereby accurately reproducing the actual operating conditions of the train. Through simulating various empty and loaded vehicle marshalling conditions, the axle load transfer characteristics under different train formations are systematically analyzed. On the basis of exploring the fundamental mechanisms inducing axle load transfer, three targeted improvement schemes are put forward. Numerical simulations are carried out for these schemes and compared with the original design, so as to obtain the quantitative values and variation laws of axle load transfer under different conditions. The findings can provide important theoretical data and engineering references for the structural design, performance optimization and manufacturing of road-rail combined vehicles.
Road–rail Vehicle; Actual Traction Braking; Axle Load Transfer; Coupler Buffer Device.
Design of an Environmental Monitoring System for Additive Manufacturing Workshops based on Two-Level Data Fusion
As an important element in the framework of intelligent manufacturing, additive manufacture works in the complex and changing workshop environment. These environments will bring great changes and instability to the data measured by every single sensor. And the traditional evaluation ways by one parameter only is limited by the one-dimensional view of it, which is not enough to get the precision that we need for the environment grade in the real time control application. In this paper, we solve these problems by giving an environmental monitoring system for the additive manufacture workshop, which is based on the dual level data fusion architecture of Kalman filtering and Bayesian networks. The system implementation is using an STM32 micro controller, a USR-EG828 edge gateway, embedded system program, Python development environment and the Modbus communication protocol. The results of experiments show that after the first fusion stage, the temperature data has greatly decreased the fluctuation, and the data of integration is more like the actual conditions. The estimated environmental parameters are very close to the real measured ones. In particular, the mean absolute error (MAE), root mean square error (RMSE) and maximum absolute error (MAXAE) for the temperature are decreased from 0.855 °C, 0.9776 °C, and 1.7543 °C (with one sensor only) to 0.373 °C, 0.2985 °C and 0.6127 °C, respectively. Similar improvements on the noise suppression and the measure precision can also be seen for the humidity, TVOC, and PM2.5 data. The second level data fusion gets perfect accuracy on the classification of the environmental grades for all ten validation samples. In summary, the introduced framework can help to make very accurate monitoring of many parameters and a complete assessment of the environmental grades in the manufacturing environment, which can largely improve the reliability of evaluating the ambient conditions. This research provides a practical technical way for the management of environmental factors in the additive manufacturing facilities.
Two-level Data Fusion; Kalman Filtering; Bayesian Network; Additive Manufacturing Workshop; Environmental Monitoring System.
Teaching Reform and Practice of the AI-Empowered Model in Web Front-End Development Courses
The deep integration of artificial intelligence technology with new engineering education provides a brand-new path for the transformation of teaching models in computer-related courses in universities. In response to the problems existing in the traditional Web front-end development technology courses, we have constructed an AI-driven teaching reform model. This method first systematically reconfigures the teaching content through five dimensions (interest, problem, project, integration, and ideological education), and then designs a multi-dimensional guiding teaching system of “interest-driven, problem-driven orientation, project practice, interdisciplinary integration, ideological and political education”, integrating artificial intelligence technology throughout the entire classroom teaching and complementing it with a diversified process assessment system, aiming to improve the course quality and enhance students’ professional abilities. Teaching practice shows that this method effectively compensates for the shortcomings of traditional teaching, significantly improving students’ classroom participation, code development ability, and autonomous learning literacy, providing a replicable practical framework and theoretical reference for Web front-end and similar computer courses in the context of new engineering education.
AI; Web Front-end; New Engineering Education.
Prediction and Interpretability Analysis of Deep Eutectic Solvent Viscosity based on a Stacking Ensemble Model
Deep eutectic solvents (DES) are a new type of green solvent with significant application potential in the selective leaching of zinc-containing solid waste via hydrometallurgy. However, traditional design methods rely heavily on trial-and-error experimentation, resulting in low R&D efficiency, and the core physical property (viscosity) that determines mass transfer and reaction kinetics in the system is difficult to estimate accurately. To address this industry challenge, this paper proposes a high-precision viscosity prediction and micro-mechanism analysis framework that integrates multi-dimensional cross-feature engineering, the Whale Optimization Algorithm (WOA), and Stacking ensemble learning. Based on a rigorously cleaned, multi-source standardized dataset, the model employs Extreme Gradient Boosting (XGBoost) and Random Forest (RF)-both globally optimized via WOA-as base learners, combined with a Linear Regression (LR) meta-learner through a two-stage deep integration. Extrapolation evaluation on an independent test set demonstrates that this two-layer architecture effectively overcomes the generalization bottleneck in complex sample chemical spaces, achieving an excellent accuracy with a coefficient of determination ( ) of 0.8620 and an average absolute relative deviation (AARD) as low as 9.88%, with overall performance significantly outperforming various single-baseline models.Furthermore, this study introduced game-theoretic SHAP analysis, successfully breaking through the “black-box” barrier of deep ensemble models. The research quantitatively confirmed that the drastic changes in DES viscosity essentially stem from strong electrostatic coupling and a dense hydrogen-bond cross-linking network formed after component mixing.The positive synergistic interaction between high steric hindrance in the mixed space and strong electrostatic attraction constitutes the underlying mechanism driving the macroscopic viscosity jump, while the “coupled resonance” of multiple microscopic features at low temperatures serves as the fundamental driving force behind the sharp rise in hydrodynamic viscosity.This study not only provides a breakthrough data-driven paradigm for estimating the physical properties of complex chemical systems, but also offers solid quantitative theoretical guidance for the targeted reverse design of low-viscosity, high-performance green DES solvents for industrial zinc extraction through a multidimensional, mechanism-transparent analysis.
Deep Eutectic Solvents (DESs); Viscosity Prediction; Stacking Ensemble Learning; Whale Optimization Algorithm (WOA); SHAP Interpretability Analysis; Hydrometallurgy.
Optimizing Road Networks in Small and Medium-sized Cities: A Multidimensional Trade-off between Efficiency and Environmental Sustainability
The unreasonable structure of the road network has become a significant bottleneck restricting the sustainable development of transportation systems in small - medium cities. This paper is based on road network grading theory and integrates a multi-dimensional performance evaluation framework to address the common problem of the "inverted pyramid" structure and its environmental impacts. We first establish a theoretical road network model that follows a pyramid structure. In order to reconcile the contradiction between public transit priority and travel efficiency, we put forward a new type of functional segmentation strategy of the arterial road system, which can divide it into "passenger-oriented" and "freight-oriented" corridors. A set of 12 second-order indicators is included under the first-order dimensions of structure, efficiency, equity, resilience, and environmental impact. The weights of these indicators are quantified using an improved hierarchical CRITIC objective weighting method. We simulate and analyse three road network density scenarios (a base case of 6.8 km/km2, a plan density of 12.8 km/km2, and a high density benchmark of 21.1 km/km2) for a virtual case city that has been calibrated with empirical data. The results show a significant deviation between environmental and efficiency objectives. The planned densification scenario (12.8 km/km2) achieves the best overall performance (score: 0.819), lower than 15.2% compared to the baseline. However, this situation also leads to an increase in noise, and corresponding noise reduction measures need to be taken. Pursuing excessive density (21.1 km/km2) leads to decreased efficiency of resource utilization and intensified noise pollution. This study provides a systematic decision-support tool and the theoretical basis for road network optimization design, public transportation priority, and reducing environmental externality effects of small- to medium-sized cities.
Road Network Hierarchy; Multi-Criteria Evaluation; Environmental Externalities; CRITIC Weighting; Transit Priority; Small and Medium- Sized Cities; Transportation Carbon Emissions; Traffic Noise.
Hydrocarbon Accumulation Conditions and Main Controlling Factors of Intracratonic Carbonate Rocks in Russia
Research objectives and scope: Taking intracratonic carbonate rocks in Block S as the research object, this paper aims to clarify the complex hydrocarbon accumulation rules, define the core controlling factors and hydrocarbon migration-accumulation mechanisms, and fill the research gaps in relevant fields. Research methods: Based on drilling, seismic and core test data, integrated seismic-geological interpretation, tectonic-sequence evolution restoration and accumulation element coupling analysis were adopted to systematically analyze source-reservoir-cap assemblage characteristics and hydrocarbon migration-accumulation laws. Research results and conclusions: An efficient hydrocarbon accumulation system consisting of high-quality hydrocarbon source rocks, high-energy reef-beach reservoirs and thick gypsum-salt cap rocks was identified. A dual-mode migration pathway network dominated by inherited paleo-uplifts as well as faults and unconformities was developed. Hydrocarbon accumulation was jointly controlled by the three-dimensional coupling of paleo-uplifts, reef-beach reservoirs and source-connected faults, and the corresponding hydrocarbon accumulation model was established. Innovation and application value: The established model further improves the accumulation theory of intracratonic carbonate reservoirs, provides important analogical references for the exploration of similar reservoirs in Tarim Basin, Sichuan Basin and other basins, and has important guiding significance for oil and gas exploration practice.
Intracratonic Carbonate Rock; Inherited Paleo-uplift; Dual-mode Migration and Accumulation; Reef-beach Reservoir; Block S.