Eng, Volume 7, Issue 3 (March 2026) – 44 articles

Fault detection in microgrids is a critical element of system stability and uninterrupted power delivery. Herein, a comparative study using LSTM and bidirectional LSTM networks is performed based on three-phase current data for multi-class fault classification. Five major fault types, namely LG, LL, LLG, LLL, and LLLG, were simulated using a Real-Time Digital Simulator (RTDS) under grid-connected and islanded modes. Collected current signals were preprocessed, normalized, and segmented for sequence learning. Later, both models were trained using the best hyperparameter setting to enhance their capabilities and classify faults. To measure how well they identified faults, evaluation metrics, like accuracy, precision, recall, F1-score, and ROC-AUC, were calculated. The results revealed that the Bi-LSTM outperformed the LSTM and classical machine learning models consistently, with more than 99% accuracy for most fault types. More importantly, the proposed framework also checked classification performance for LLLG faults, with the Bi-LSTM model having a test accuracy of 98.8%. These results confirm that the Bi-LSTM model can robustly and precisely classify and detect faults in real time within specific phases of microgrids; therefore, it provides a scalable foundation for the development of intelligent protection in smart power systems. Full article

To investigate the load characteristics and rock-breaking efficiency of the hobs on the conical cutterhead, a theoretical model of the hob’s rock-breaking load was established based on the plastic-brittle characteristics of rock, with a verification error of less than 5%. A numerical model of dual-hob rotary rock breaking was developed using ABAQUS 2022 software to comparatively study the influence of penetration depth (P), cutter spacing (S), and rotational speed (V) on the hob’s load behavior and rock-breaking efficiency. The specific energy of rock breaking under various test conditions was obtained through orthogonal experiments. The results indicate that, as the penetration depth increases, the average rock-breaking load of the hob gradually increases, while the specific energy first decreases and then increases. With larger cutter spacing, the average load shows a modest increase, and the specific energy exhibits a gradually rising trend with a diminishing growth rate. As the rotational speed increases, the average load increases slightly, while the specific energy rises with an accelerating growth rate. Range analysis revealed that the order of influence of factors on rock-breaking efficiency is P > S > V. The highest rock-breaking efficiency was achieved at P = 2 mm, S = 60 mm, and V = 7 r/min. At a significance level of 0.05, the penetration depth was found to have a significant effect on specific energy. This study provides a valuable reference for the design of hob layouts and parameter settings of conical cutterheads, contributing to improved rock-breaking efficiency. Full article

The widespread commercialization of wireless chargers for electric vehicles generally suffers from one main problem, which is the perfect alignment between the two coils, leading to a decrease in mutual inductance, which causes a drop in magnetic coupling and even a failure to transfer power. To address this persistent problem, this work proposes a comprehensive and integrated method for optimizing the coils and control architecture for reliable and safe battery charging. To address the challenges of a complex, nonlinear design space and the need for misalignment-tolerant geometries, we employ a memetic algorithm (MA) that hybridizes Particle Swarm Optimization (PSO) for broad global exploration with Mesh Adaptive Direct Search (MADS) for precise local refinement. This combination effectively avoids poor local solutions—a limitation of standalone PSO or GA approaches reported in recent studies—while efficiently converging to coil geometries that maintain strong magnetic coupling under misalignment. After the coils have been designed, electromagnetic validation is tested using finite element analysis (FEA), which allows the magnetic field distribution to be evaluated, as well as the coupling coefficient under different scenarios of misalignment and variation in the air gap between the ground side and the vehicle side. At the same time, a comprehensive control strategy for the primary side of the system has been developed. This control method ensures power management on the primary side, enabling system interoperability for charging multiple types of vehicles, as well as reducing vehicle weight for greater range. All this is combined with an innovative pulsed current charging method, chosen for its advantages in terms of thermal stability, ensuring safe and efficient recharging that is mindful of battery health. Simulation and experimental validation demonstrate that the proposed framework maintains stable wireless power transfer and achieves over 87% DC–DC efficiency under lateral misalignments up to 100 mm, fully complying with SAE J2954 alignment tolerance requirements. Full article

Clarifying the dynamic response characteristics of buildings and pile foundations under the action of blasting vibration is of great significance to ensure the safety and stability of the buildings adjacent to the underwater drill blasting project in the waterway. Based on the blasting construction project of the HD13 section of the Western Land-Sea New Passage (Pinglu) Canal Waterway Project, the attenuation law of the blasting vibration along the riverbank was obtained through the on-site blasting vibration monitoring. Based on on-site blasting vibration monitoring results, the dynamic response characteristics of residential buildings in the adjacent waterway were analyzed using the LS-DYNA dynamic finite element analysis method. The numerical results show that the roof’s peak vibration velocity decreases with increasing height from the foundation within the same building, and the peak attenuation is 67.76%. The peak vibration velocity and the maximum principal stress of the pile foundation increase with increasing pile depth. Based on the numerical analysis results, a linear relationship formula is established between the peak vertical vibration velocity of the pile body and the peak maximum principal stress. It is calculated that the safe control threshold value of pile foundation blasting vibration within the parameter range of this study is 13.92 cm/s. Full article

The objective of this article is to optimise the deposition modes and the content of exothermic additions (EAs) in the core filler in Fe-C-Cr-Ti with Cu additions hardfacing. To achieve this, JMatPro Release 7.0, Sente Software Ltd., 2016 material characterisation software was used to simulate and calculate the equilibrium phase structure and composition of the Fe-C-Cr-Ti-Cu alloy during the welding thermal cycle. A synergistic approach combining the Taguchi–Analysis of Variance (ANOVA)–Factorial design (FD) method with the standard hybrid Taguchi–ANOVA–Principal Component Analysis (PCA)–Grey Relational Analysis (GRA) is used and justified to optimise factors and develop mathematical models for parameters in the L9 orthogonal experimental design. The study examines how the transfers of deoxidisers depend on the content of exothermic additions in the cored wire filler (EA) and the contact tip-to-work distance (CTWD), while the behaviour of carbide formers is influenced by wire feed speed (WFS) and present arc voltage at the power source (U_set). The research specifically investigates the Fe-C-Cr-Ti-Cu system and the role of copper in stabilising austenite. Findings show that high Cu concentrations (7 wt.%) enhance hardenability by 13%, effectively suppressing pearlite transformation and expanding the bainite region. The desired chemical composition of the deposited metal is determined by the distribution of selected factors, as measured by the transfer coefficients of each element. Full article

The complexity of an extrusion die profile is determined by its geometry. Various metrics such as the complexity index, shape factor, and form factor are used to quantify how geometric intricacy affects production costs, die life, energy consumption, product quality, and overall manufacturability. Bearing geometry plays a critical role in controlling metal flow and tool life in aluminum extrusion. In this study, a simulation-based investigation is performed to investigate the influence of bearing geometry on extrusion behavior using the finite element method. Two extrusion dies are examined: A single-cavity die with uniform bearing geometry and a dual-cavity die with controlled bearing geometry modification in one cavity. The results show that the bearing modification in the dual-cavity die causes severe flow imbalance, with exit velocity deviations. This imbalance leads to localized pressure amplification, increased thermal gradients, and stress concentration in critical die regions. In contrast, the single-cavity die, due to its simple geometry, exhibits uniform flow, stable pressure evolution, and low tool stress. These findings demonstrate the high sensitivity of multi-cavity extrusion dies to bearing geometry and highlight the importance of simulation-driven die design for achieving balanced flow and improved tool performance. Full article

Ensuring the crashworthiness of heavy-duty truck cabs is essential for reducing occupant fatalities and improving passive safety in commercial vehicles. Regulatory frameworks such as UNECE Regulation No. 29 (R29) define structural integrity requirements through full-scale destructive impact tests, which are costly and limit iterative design. In this study, an integrated experimental–numerical methodology is presented for the impact assessment of a real Iveco Eurocargo 120E18 truck cab reconstructed using high-resolution 3D scanning. The scanned geometry was used to generate a dimensionally accurate CAD model of the load-bearing cab structure, which was analysed using explicit finite element simulations in ANSYS Academic Mechanical and CFD Teaching package under impact conditions compliant with UNECE R29 and implemented according to the Swedish regulation VVFS 2003:29. In parallel, a full-scale physical pendulum impact test was performed on the same cab using a cylindrical impactor with a diameter of 580 mm, a length of 1800 mm, and a mass of approximately 1000 kg, impacting the upper region of the A-pillar. The experimental setup was instrumented using high-speed optical measurements and an accelerometer to capture impact kinematics and structural response. The numerical predictions showed good agreement with experimental results in terms of acceleration–time histories, absorbed energy evolution, and structural deformation, with differences generally below 6%. Critical regions susceptible to local buckling and plastic collapse were consistently identified in both approaches, while preservation of the driver survival space was confirmed. The results demonstrate that scan-based finite element models, when properly calibrated and validated, can reliably reproduce certification-level impact behaviour. The proposed workflow provides a robust and cost-effective framework for regulatory pre-validation, structural optimisation, and digitalisation of crashworthiness assessment for heavy-duty truck cabs. Full article

Addressing the challenges in 3D reconstruction of large-scale open-pit mines, such as dramatic terrain undulations, complex texture features, and the difficulty of balancing geometric accuracy with real-time rendering efficiency using traditional methods, this paper proposes a high-fidelity reconstruction framework integrating UAV multi-modal data with the state-of-the-art 3D Gaussian Splatting (3DGS) architecture. First, an integrated air-ground multi-modal data acquisition system is established. Using a UAV equipped with LiDAR and a high-resolution camera, high-quality geometric and textural data of the mining area are acquired through terrain-adaptive flight planning. Second, to tackle the VRAM bottlenecks and loose geometric structures inherent in original 3DGS for large scenes, we adopt the advanced CityGaussianV2 architecture as our core reconstruction engine. By leveraging its divide-and-conquer parallel training strategy, 2DGS planar geometric constraints, and Decomposed Gradient Densification (DGD) mechanism, this framework effectively overcomes memory limitations and significantly enhances the geometric sharpness of slope crests and toes. Finally, engineering validation was conducted at Kambove Mining. Experimental results demonstrate that the proposed method achieves centimeter-level geometric accuracy, a real-time web rendering frame rate exceeding 60 FPS, and a model storage compression rate of over 90%. The digital twin control platform built upon this model successfully achieves deep fusion and visual scheduling of multi-source heterogeneous data, providing a novel technical path for constructing high-precision reality-based foundations for smart mines. Full article

The paper presents a comprehensive deep learning-based framework for automated visual inspection of overhead power line infrastructure using unmanned aerial vehicles. Traditional manual and helicopter inspections are costly, time-consuming, and hazardous for maintenance personnel. The proposed approach integrates UAV imaging with advanced computer vision models such as YOLOv8, EfficientDet-D2, and Faster R-CNN to automatically detect defects in critical components, including insulators, conductors, and transmission towers. Several open datasets (InsPLAD, TTPLA, MPID) were used for training and validation, ensuring robustness under diverse lighting and environmental conditions. Experimental results demonstrate that YOLOv8 achieved the best performance, reaching 88.5% mAP@0.5 with real-time inference capabilities (over 50 FPS on GPU). The system significantly enhances inspection efficiency, allowing for a threefold increase in coverage capacity and an up to 70% reduction in defect remediation time. The integration of AI-powered visual analytics with maintenance and SCADA systems enables a shift from reactive to predictive maintenance, improving the safety, reliability, and resilience of power transmission infrastructure. Full article

The increasing deployment of collaborative and industrial robots in manufacturing systems places high demands on equipment reliability, availability, and maintenance efficiency. Robotic workcells, in which multiple automated subsystems operate in tightly coordinated cycles, are particularly sensitive to unplanned downtime, as failures of individual components can disrupt the entire production process. Traditional time-based preventive maintenance is often insufficient under such conditions, as it does not adequately reflect actual operating loads or component degradation. This paper proposes a structured framework for the design of an integrated maintenance concept for a multi-robot packaging workcell. The framework systematically combines component identification, criticality assessment, and the selection of appropriate maintenance strategies, including preventive, predictive, corrective, proactive, and reactive approaches. Preventive maintenance is complemented by condition-based monitoring and trend analysis of selected diagnostic parameters, enabling predictive decision-making for critical components. The proposed methodology further integrates maintenance planning and performance evaluation through a computerized maintenance management system (CMMS), supporting the coordination of maintenance activities and the assessment of key performance indicators. The novelty of the proposed framework lies primarily in the dynamic allocation of maintenance strategies based on semi-quantified component criticality and in the structured integration of predictive diagnostic information with CMMS-supported maintenance planning. Unlike traditional RCM-based or single-strategy maintenance approaches, the framework enables coordinated preventive, predictive, corrective, proactive, and reactive actions within a unified decision-making architecture, supporting proactive continuous improvement of maintenance performance through a closed-loop feedback mechanism that updates component criticality based on real-time operational data. The framework is demonstrated on a robotic workcell comprising a collaborative robot, an industrial robot, pneumatic subsystems, and a centralized control architecture. The results suggest that the integrated approach may provide a coherent basis for reducing reactive maintenance actions, improving system availability, and supporting data-driven maintenance planning. As a conceptual framework with partial (pilot) practical implementation within the context of this paper, the proposed approach establishes a foundation for future broader implementation, experimental validation and the integration of advanced diagnostic and prognostic methods, mainly in the context of multi-Robot workcell and production process maintenance. Full article

Given that freeze–thaw damage of prestressed concrete significantly threatens structural service life and that existing conventional simulation techniques fail to capture prestress time series, this paper proposes a deep learning prediction model based on the Transformer model. The model integrates a multi-head self-attention mechanism and positional encoding to effectively capture long-range dependencies in prestressed time series. It enhances temporal modeling capability through a 128-dimensional high-dimensional feature space (chosen to balance representation capacity and computational efficiency for the dataset scale) and a 4-layer encoder stacking structure. A dataset was constructed using time-series data from three prestressed concrete components subjected to 50 freeze–thaw cycles. The F-a component was used as the training set, while F-b and F-c served as the testing sets. During the training phase, a Noam learning rate scheduler, gradient clipping, and an early stopping strategy were employed. The results indicate that the training strategy enables the loss function to converge quickly without overfitting, demonstrating good generalization performance. The prediction model performs well on the F-a and F-c datasets, with determination coefficients (R²) of 0.8404 and 0.8425, and corresponding Mean Absolute Error (MAE) of 61.71 MPa and 57.41 MPa, respectively. It can accurately track the periodic variation trend of prestress, demonstrating the model’s effectiveness in prestress prediction. This model provides a new technical tool for the health monitoring and performance prediction of prestressed concrete structures in freeze–thaw environments. Full article

Constrained nonlinear optimization plays a fundamental role in engineering design due to the presence of irregular feasible regions and interacting nonlinear restrictions. This study evaluates the performance of the Plant Growth Optimization (PGO) algorithm in a constrained nonlinear benchmark problem. The algorithm was implemented in MATLAB^® and assessed using a fixed external penalty formulation for constraint handling. Performance was analyzed through convergence dynamics, constraint evolution, dispersion across 20 independent runs, and computational efficiency. A comparative study was conducted against Particle Swarm Optimization (PSO), Genetic Algorithms (GA), and Differential Evolution (DE) under identical experimental conditions. Results show that PGO achieves stable convergence within 87 generations, consistently attaining a feasible solution near the constraint boundary with low dispersion across runs. Statistical validation using the Friedman test (${\chi }^2=32.45$, $p<0.001$) confirmed significant performance differences among algorithms, while post-hoc Wilcoxon tests indicated comparable performance between PGO and DE and significant differences relative to PSO and GA. These findings demonstrate that PGO provides a balanced compromise between robustness, convergence stability, and computational efficiency, supporting its suitability for constrained nonlinear engineering optimization tasks. Full article

This study evaluates the feasibility of utilizing manufactured sand as a full or partial replacement for river sand in mass concrete production, motivated by the growing scarcity of natural river sand and stringent environmental regulations on mining. The influence of the manufactured sand replacement level, water-to-cement ratio, and fly ash content on key properties including workability, mechanical strength, early-age shrinkage, and thermal stress was systematically investigated. The results demonstrate that, while the incorporation of manufactured sand marginally impairs workability, it contributes to an improved particle size distribution of the fine aggregate. At 100% replacement, the 56-day compressive, flexural, and tensile strengths, as well as the elastic modulus of manufactured sand concrete, exceed those of river sand concrete, accompanied by a notable reduction in early-age shrinkage. A decrease in the water–binder ratio enhances mechanical performance but concurrently elevates the risk of cracking due to the increased autogenous shrinkage and adiabatic temperature rise associated with a higher cement content. The addition of an optimal amount of fly ash (e.g., 25%) effectively improves both workability and mechanical properties while substantially mitigating hydration heat, thereby reducing temperature differentials and the associated cracking risks. Microscopic analysis reveals that unhydrated particles, including fly ash and quartz, may act as initial defects within the microstructure. Overall, the replacement of river sand with manufactured sand in mass concrete is technically feasible, and an appropriate mix design optimization can achieve a desirable balance between performance and crack resistance. Full article

Granite is widely used in buildings, stone carvings, and sculptures, where long-term durability is strongly influenced by the micromechanical behavior of its constituent minerals and mineral interfaces. However, conventional rock mechanics tests cannot resolve the mechanical heterogeneity at the mineral scale, particularly at mineral interfaces. To address this limitation, a systematic nanoindentation study was conducted to quantitatively characterize the elastic modulus, hardness, creep behavior, residual deformation, and fracture toughness of both individual minerals and mineral interfaces in granite, and to clarify their mechanical contrasts and interrelationships. The results show that the constituent minerals quartz, feldspar, and biotite exhibit elastic modulus of 121.9 GPa, 115.6 GPa, and 66.3 GPa, respectively. Quartz and feldspar show relatively better mechanical properties, whereas biotite exhibits the weakest mechanical behavior. Hardness shows the same trend. In contrast, creep displacement and residual indentation depth follow the opposite order, i.e., quartz < feldspar < biotite. In addition, the elastic modulus and hardness of mineral interfaces are lower than those of the adjacent minerals, whereas their creep displacement and residual indentation depth are higher. The dispersion of these micromechanical parameters for mineral interfaces is generally greater than that of the adjacent minerals. The fracture toughness values of both minerals and mineral interfaces were also obtained: mineral fracture toughness ranges from 3.1 to 6.2 MPa·m^0.5, while mineral interfaces range from 0.7 to 4.3 MPa·m^0.5. Further analysis of the micromechanical parameters indicates that elastic modulus, hardness, and fracture toughness exhibit clear positive correlations among minerals, mineral interfaces, and the mineral aggregate. Comparatively, the correlations are strongest for minerals and weakest for mineral interfaces. Full article

Metal additive manufacturing (AM) facilitates the production of geometrically complex components, yet its broader industrial use remains limited by the risk of defect formation and uncertainties in their detection, originating from the highly dynamic and high-temperature process environment. To make additive manufacturing more reliable and establish high-quality parts, it is important to understand how these defects form and how their characteristics appear during the process. This review explains the main causes of common defects, such as cracking, porosity, lack of fusion, and inclusions in metal AM processes, including Powder Bed Fusion and Directed Energy Deposition. It also connects main defect formation mechanisms to the optical, thermal, acoustic, and spectroscopic signals that can be measured during the process. Moreover, it is described how commonly used in situ monitoring systems work and how their signals correspond to melt pool dynamics, vapor plume, particle movement, and the solidification process for each kind of defect. An overview is provided of how data from these systems are analyzed, including the extraction of features from images, the evaluation of temperature fields, and the use of time and frequency domain techniques for various signals. By linking the physics of defect formation to measurable process signals, the interpretation of sensor data is enabled, and potential strategies for monitoring specific problems are outlined. Finally, recent developments are examined, including the integration of multiple sensors, advanced feature-representation approaches, and real-time data interpretation coupled with adaptive control. Together, these directions represent promising advances towards more intelligent and reliable monitoring systems for the future of metal AM. Full article

This study experimentally investigates the thermal–hydraulic performance of heat exchanger tubes fitted with wired twisted tapes, with particular emphasis on the effects of the hole spacing-to-width ratio (s/W) and edge margin-to-width ratio (e/W). Experiments were conducted over a Reynolds number range of 6000–20,000, and the results were compared with those of plain tubes and tubes equipped with conventional twisted tapes. The findings revealed that the incorporation of wires significantly enhanced heat transfer due to the combined action of longitudinal eddies generated by wire protrusions and swirling flow induced by the twisted tape. At identical Reynolds numbers, tubes with a smaller hole spacing (s/W = 0.16) exhibited superior heat transfer performance, achieving Nusselt number enhancements of up to 107.7% relative to plain tubes and 51.6% relative to conventional twisted tapes. Similarly, reducing the edge margin ratio intensified near-wall eddies and further disrupted the boundary layer. The friction factor was found to increase with decreasing hole spacing and edge margin, primarily due to additional flow obstructions and enhanced near-wall shear stresses. For wired twisted tapes with s/W = 0.16, the friction factor reached nearly six times that of a plain tube. Despite this penalty, the thermal performance factor (TPF) remained favorable, with values of up to 1.2, indicating that the heat transfer benefits outweighed the corresponding pressure losses. Full article

Additive manufacturing (AM) technologies are a key tool in producing complex and functional polymer parts, with Fused Deposition Modelling (FDM) emerging as the most widely used technique. PA6 polyamide is gaining increasing importance due to its high strength, wear resistance and processability, making it suitable for polymer product manufacturing. However, the mechanical properties of PA6 FDM components are largely determined by process parameters, and their optimisation is necessary to achieve stable and reliable properties. In this study, the influence of nozzle temperature, infill density and infill geometry on the tensile strength of PA6 specimens was investigated. The Central Composite Design (CCD) method was used for process modelling and optimisation, along with statistical analysis and experimental validation. The individual effects of the analysed parameters were confirmed by a preliminary experiment, while a detailed analysis of their mutual relationships was enabled through the main experiment. Analysis of the results showed that increasing both temperature and infill density positively affects tensile strength, regardless of the infill structure. The accuracy and reliability of the model were confirmed by validation, with a coefficient of determination R² = 0.8958 and a high level of agreement between experimental and predicted data. By optimising the process parameters, maximum tensile stresses of 17.705 MPa were achieved with an infill density of 74.142%, a Triangle-Hexa infill pattern, and a nozzle temperature of 254.142 °C. The confirmation experiment validated the optimised parameters, and the results provide a statistically validated framework for optimising the tensile performance of PA6 components manufactured by FDM under controlled laboratory conditions. Full article

Intelligent energy management systems are increasingly necessary for integrating renewable energy sources within microgrids. This paper investigates the application of a reinforcement learning (RL) neural network to optimize the operation of an electrochemical storage system in an environment composed of residential loads, commercial loads, and a photovoltaic plant, all connected to the grid. A dataset combining market purchase prices, photovoltaic generation, and residential and commercial load profiles was generated and used to train a Twin Delayed Deep Deterministic Policy Gradient (TD3) agent with the primary goal of deriving a reliable and adaptive post-training policy capable of maximizing photovoltaic self-consumption, minimizing operational costs through intelligent price arbitrage, and ensuring strict compliance with battery physical constraints. The system state includes battery state of charge, load demand, PV generation, and normalized market purchase prices, whereas the action represents the battery’s charge/discharge power, which is restricted from exporting energy to the grid. Results show that the agent learns to effectively store surplus PV energy and minimize grid dependency through dynamic charge management. The proposed approach outperforms strategies based solely on storing surplus self-generated energy and maintains the battery within safe operational limits. Tests with previously unseen data demonstrate robust, adaptive, and economically efficient energy management, highlighting the potential of reinforcement learning in intelligent energy systems. Full article

Ti(C,N)-based cermet turning inserts with two distinct chip breaker groove structures were employed to investigate the influence of chip breaker geometry on cutting performance. Chip removal performance and wear resistance of the inserts were evaluated according to chip morphology. The results reveal that, compared with inserts with the V-type groove, those with the SF-type groove exhibit superior chip removal capability and enhanced flank wear resistance. Based on two key parameters of the equivalent groove width and initial chip curl radius, an oblique cutting model was proposed for turning inserts with three-dimensionally complex grooves. The model incorporates the coupled effects of chip breaker geometry, workpiece material properties, inserts material properties and cutting process parameters. By controlling chip morphology, the proposed model effectively realizes the improvement and rational optimization of cutting performance, providing a theoretical basis for the design and optimization of complex groove turning inserts. Full article

Modern materials science is focused on the development of steels with a range of performance characteristics, including high strength, wear resistance, corrosion resistance, and long-term performance in various conditions. Special attention is paid to the control of the microstructure of steels at the crystallization stage, which allows for the improvement of metal properties without significantly increasing the cost of the manufacturing process. One of the promising methods of microstructural engineering is the modification of steels with dispersed particles of refractory compounds, such as titanium carbide (TiC), zirconium carbide (ZrC), and tungsten carbide (WC). However, the processes of dissolution, dissociation, and interaction of such ceramic particles with the metal melt, as well as their influence on the formation of the microstructure and properties under the conditions of non-equilibrium crystallization, which is typical for centrifugal casting, are not sufficiently studied for austenitic stainless steels. In this work, the influence of dispersed carbide particles of TiC, ZrC, and WC, which are introduced into the melt of austenitic stainless steel (Cr ≈ 18%, Ni ≈ 10%) during centrifugal casting, on the redistribution of alloying elements, the formation of the microstructure, and the mechanical properties of the material is investigated. Special attention is paid to the kinetic nature of the dissolution and interaction of the carbides with the melt, as well as the directional distribution of elements across the cross-section of the billets. The study includes the analysis of the distribution of Ti, W, and Zr across the cross-section of the centrifugally cast billets, the study of the microstructure and phase composition of the inclusions using SEM/EDS, and mechanical testing. It is found that the implementation of dispersion hardening leads to an increase in the tensile strength by up to ~22% compared to the initial alloy (from 496 to 612 MPa), while the impact strength decreases by 5–25% (from 110 to 82 J/cm²) depending on the type and quantity of the introduced particles. The analysis of microhardness shows the presence of a gradient of local properties across the cross-section of the centrifugally cast billets, with microhardness values ranging from ~110 to 195 HV0.5. For the modified samples, the relative difference between the inner and outer zones is ~5–20%, reflecting the combined effect of non-equilibrium solidification, redistribution of alloying elements, formation and spatial distribution of secondary phases, and local structural heterogeneity. These results confirm the possibility of controlling the distribution of properties within a single billet. Full article

Through static bending tests on two full-scale specimens of a new steel grating–UHPC (ultra-high-performance concrete) composite bridge deck, the load–displacement curves, crack propagation, strain distribution, and failure characteristics were analyzed. According to the experimental results, a numerical model was established using ABAQUS software 2021, in which two contact methods were employed to simulate the interfacial connection between UHPC and steel. The results indicate that the surface-to-surface contact method provides better agreement with the experimental data. Subsequently, conducted parameter studies using this model to investigate the impact of key geometric parameters, including section height, flange width, flange thickness, steel bottom plate thickness, and steel web plate thickness, on the flexural performance of the structure. The results demonstrated that the section height and the steel bottom plate thickness had a significant effect on the load-bearing capacity and overall stiffness of the component, while the influence of other parameters was comparatively minor. Finally, based on both experimental and numerical results, a formula for calculating the flexural bearing capacity of steel grating–UHPC composite bridge slabs was proposed, providing a reference for the structural design and promotion of the new composite bridge deck. Full article

This study aims to experimentally evaluate and compare the electrical–thermal performance of a 20-cell 18650 lithium-ion battery pack cooled by a pure phase change material (PCM) and a PCM/TiO₂ nanoparticle composite to identify an effective passive thermal management approach for EV battery applications. Using a controlled charging–discharging system, thermocouple-based temperature mapping, and systematic tests across multiple C-rates (0.75 C–1.5 C), the study measures the variations in battery temperature, generated heat, and voltage behavior as functions of depth of discharge (DOD) and state of charge (SOC). The results show that the PCM/nanoparticle mixture markedly improves thermal conductivity, reduces peak temperature by approximately 8–10 °C compared with pure PCM, delays thermal saturation at higher C-rates, and enables a wider safe DOD range with reduced voltage sag and lower heat accumulation. Based on the experimental temperature/voltage trends in this study, limit DOD to ≤40–50% at high power (≈1.5 C), ≤50–60% at moderate power (≈1 C), and ≤60–70% at low power (≈0.75 C) (i.e., target SOC windows roughly 60–100% SOC at 1.5 C, 40–100% SOC at 1 C, and 30–100% SOC at 0.75 C), with an absolute practical upper DOD limit of ~70% to avoid frequent deep discharge damage; these limits keep peak temperatures below ~40–45 °C, reduce severe voltage sag near cutoff, and greatly extend cycle life because shallower cycling (e.g., 50% vs. 100% DOD) produces many times more cycles. These improvements enhance battery safety, performance stability, and cycle life, making the nanoparticle-enhanced PCM a practical, compact, and energy-efficient solution for passive battery thermal management in electric vehicles. Full article

The optimal placement of samplers and sensors in water distribution systems (WDSs) and wastewater collection systems (WCSs) is fundamental to effective monitoring, early contamination detection, and system protection. The goal of optimal sensor/sampling placement (OSP) is to maximize the ability to detect, monitor, and track critical variables, such as contaminants or temperature, while maintaining cost-effectiveness and operational efficiency. In practice, OSP problems are inherently multi-objective and typically involve trade-offs between cost minimization, spatial and temporal coverage, detection accuracy, and robustness under uncertainty. This paper presents a comprehensive review of recent single- and multi-objective optimization strategies for source detection and monitoring, drawing on approaches developed in various research fields. The reviewed literature is systematically organized according to problem formulation, objective functions, optimization techniques, and decision-making strategies, paying particular attention to their applicability in real-world WDSs and WCSs. Beyond summarizing existing methods, this review critically examines key methodological assumptions and limitations that hinder practical implementation. These include sparse sensor deployment, budget constraints, and modeling and sensor uncertainty. Finally, the paper identifies open challenges and outlines potential directions for future research aimed at improving the robustness, scalability, and practical relevance of OSP strategies. Full article

In nature, organisms have evolved unique structures that feature low weight, high strength, and damage resistance. The Eurasian eagle-owl serves as a representative example, with specialized feather architectures that enable stable flight in intense and turbulent airflow conditions. Herein, driven by classical design layup guidelines, and inspired by the distinctive fiber architecture of the feather shaft cortex, we propose a regionally tailored layup (RTL) design to enable mass-efficient composite beams with high load-bearing capacity and enhanced damage tolerance. The feather shaft reference lay-up rectangular beam (FSRB) adopts the RTL, and a flange overlap is introduced to preserve the integrity and strength of the flange–web interface; it is then manufactured using inner–outer matched molds in conjunction with vacuum bag molding. Three-point bending shows that the FSRB achieves a flexural strength of 180 MPa and a flexural modulus of 12.1 GPa. Relative to conventional axial (ALRB), Cross-ply (CPRB), single-helix (SLRB), and quasi-isotropic (QLRB) lay-up rectangular beams, the FSRB improves strength by 59.5%, 46.6%, 26.8%, and 21.2%, and increases modulus by 81.7%, 34.7%, 25.1%, and 10.8%, respectively. FEA and SEM observations confirm an RTL architecture in the rectangular beams, characterized by differentiated fiber arrangements in the flange and web. Flanges with an axially dominated layup provide high initial flexural strength and stiffness. The web, formed by a crossed-ply/axial hybrid layup, provides transverse support and redirects crack/delamination growth, thereby promoting progressive failure and enhancing energy dissipation. Overall, this RTL design enables concurrent improvements in load-carrying capacity and damage tolerance. This study offers a design perspective for high-performance load-bearing components. Full article

Rapid urbanization has significantly increased energy demand in buildings, which now represent nearly 30% of global energy use. In India, buildings are built across highly varied climatic conditions, from hot-dry and warm-humid to cold, high-altitude areas, making climate-responsive envelope design essential to enhance thermal performance. Among envelope components, roofs are the most exposed to solar and outdoor thermal loads, playing a key role in managing indoor heat transfer. This study offers a parametric analysis of climate-responsive roof design strategies for India’s five main climatic zones, using transient simulations and statistical evaluation. The effectiveness of insulation placement, insulation material and thickness, and external surface absorptivity was systematically assessed based on roof heat gain and heat loss. Results indicate that over-slab insulation can lower roof heat gain by approximately 15–35% compared to under-slab insulation in warm-humid, hot-dry, composite, and temperate zones. In comparison, under-slab insulation decreases heat loss by about 10% in colder areas. Among insulation materials, 50 mm polyurethane foam (U = 0.433 W/m²·K) consistently outperformed extruded polystyrene and expanded polystyrene, achieving 82–83% reductions in maximum heat gain in cooling-dominated climates and 89% reductions in heat loss in cold regions relative to uninsulated roofs. When combined with a white reflective surface finish (α = 0.26), the total heat transfer reduction increased further to 89–92%. Surface treatments alone cut heat gain by 37–51% in non-cold climates, highlighting their potential as cost-effective retrofit options. Statistical analysis confirmed that dry-bulb temperature is the primary climatic factor influencing roof heat transfer (R² = 0.86–0.98, p < 0.0001), while solar radiation had a weaker effect, especially in optimized roof systems. The findings emphasize the importance of climate-specific roof design and demonstrate that insulation U-value has a greater impact on thermal performance than surface absorptivity, although both are significant. This research offers practical, climate-adjusted guidance for architects, engineers, and policymakers to enhance the thermal performance of roofs in Indian buildings. It supports the development of more resilient, energy-efficient building envelopes. Full article

With the increasing popularity of photovoltaic (PV) equipment in residential and commercial buildings, there is a pressing need for systems that maximize energy efficiency and self-consumption. This paper introduces an integrated management framework for retrofitting existing infrastructures, enabling high photovoltaic (PV) self-consumption in residential buildings through a rule-based control strategy. The framework supports three service classes—defined by user-level Quality of Service (QoS) parameters—and monitors battery voltage along with grid power exchange to coordinate heat pumps, batteries, and hot water cylinders. Experimental deployment in a residential testbed achieved up to 89% PV self-consumption while keeping daily grid usage below 0.5 kWh. Ablation experiments on battery size further demonstrated the approach’s robustness to reduced storage capacities. The use of Commercial-Off-The-Shelf (COTS) components underscores the minimal intrusiveness of this solution, highlighting its potential for seamlessly integrating diverse, vendor-specific equipment into a coordinated control system. Full article

The development of portable electrocardiographic analysis systems necessitates identifying an optimal balance between diagnostic precision and computational efficiency. This research addresses the challenge of optimal feature selection for automated cardiac arrhythmia classification in resource-constrained portable applications. We present a comparative investigation of three distinct feature selection strategies for ECG classification: the MRMR (Minimum Redundancy Maximum Relevance) method, which maximizes relevance while minimizing feature interdependencies; the ReliefF technique, which evaluates discriminative power through proximity analysis in the feature space; and permutation-based importance analysis implemented with neural networks. Utilizing the Large-Scale 12-Lead Electrocardiogram Database for Arrhythmia Study, we construct a hybrid feature space integrating 12 conventional time- and frequency-domain parameters (previously validated and included in the database’s official documentation) with 26 advanced nonlinear descriptors, including the Hurst exponent, DFA scaling parameter, log-absolute correlation measures, mean standard increment from the Poincaré plot, and wavelet entropy. The experimental results demonstrate remarkable convergence among the three paradigms in selecting optimal feature subsets, achieving classification accuracies of 87–89% for four arrhythmia classes using compact configurations of 7–10 features, and 93.57% with an extended 12-parameter set. The 7-feature configuration achieves an 82% complexity reduction compared to the full 38-feature set. Multi-algorithmic analysis confirms the consistent discriminative contribution of the proposed nonlinear descriptors, demonstrating that MRMR, ReliefF, and permutation analyses yield convergent rankings of critical parameters for automated cardiac pathology diagnosis. Full article

Asphaltene–resin–paraffin deposits (ARPDs) represent one of the most complex flow assurance challenges in oil production, particularly under late-stage reservoir development conditions characterized by pressure depletion, temperature gradients, multiphase flow, and compositional changes. Despite extensive industrial experience, ARPD control strategies are often applied empirically, without explicit linkage to the underlying physicochemical mechanisms governing deposit formation. This review presents a comprehensive and mechanism-oriented analysis of ARPD formation and mitigation in a reservoir–wellbore system. The multicomponent composition, structural heterogeneity, and interfacial activity of paraffins, resins, and asphaltenes are examined alongside thermodynamic, hydrodynamic, and operational factors controlling precipitation, transport, adhesion, and deposit growth. Particular attention is paid to the correspondence between ARPD formation stages and applicable prevention or removal technologies. The analysis demonstrates that preventive strategies targeting early-stage physicochemical processes are fundamentally more effective than post-formation removal methods. The mechanisms of inhibitor action—adsorption, desorption, and dissolution—are shown to operate in a complementary manner, while delivery efficiency is strongly influenced by spatial distribution and retention in the formation. Advanced delivery technologies, including microencapsulation and nanocarrier-based systems, provide enhanced control over inhibitor release and persistence under complex reservoir conditions. Overall, this review establishes an integrated framework linking crude oil properties, formation mechanisms, inhibition chemistry, and delivery technologies, providing a rational basis for designing adaptive and efficient ARPD mitigation strategies in modern oil production systems. Full article

Superhydrophilic micro/nano-porous media have extensive applications in electronic thermal management and energy storage systems. Predicting two-phase pressure drop in complex porous structures is of great importance for system design and optimization while remaining highly challenging. This study systematically investigates the two-phase flow resistance characteristics of bi-porous microstructures with multiple particle sizes and porosities under varying boiling regimes. Experimentally, porous samples were fabricated via vacuum sintering using nickel powders and pore-forming agents (CaCl₂), which exhibit superhydrophilicity and enhanced wicking characteristics. A visualized experimental platform was constructed to investigate the impact of pore size combinations, flow velocities, and boiling states on pressure drop. The dataset obtained through multi-factor saturated boiling experiments was further used to derive a semi-empirical model for the two-phase flow pressure drop based on the classic Kozeny-Carman (K-C) and Akagi-Chisholm (A-C) correlations. Results show that the pore size combinations and boiling states have a significant impact on the resistance performance. The proposed model achieves an average prediction deviation below 15.7%, confirming its reliability and its effectiveness as a design framework for optimizing high-capillary-force porous wicks in advanced thermal management systems. Full article