Search Results (13,918)

Eddy-current inspection of anisotropic composites, such as aeronautical CFRP, demands models that ensure mathematical rigor, tensorial consistency, and clear energetic interpretation. This work presents a novel unified variational framework with a multiplicative tensor perturbation for the time-harmonic eddy-current problem in anisotropic media with defective regions. The formulation is posed in the natural spaces $H(\mathrm{curl};\Omega )\times H^1\left({\Omega }_c\right)$, and the well-posedness is established via the Lax–Milgram theorem under physically consistent assumptions on permeability and conductivity. The sesquilinear form admits a Hermitian decomposition that separates dissipative and reactive contributions, revealing the energetic structure of the weak formulation. Defects are modeled through multiplicative modifications of the baseline anisotropic conductivity tensor. This congruence-based approach preserves symmetry and positive definiteness, ensuring non-negative Joule losses and structural stability, allowing a modular representation of subsurface delamination, fiber breakage, conductive inclusions, and distributed porosity within a single tensorial framework. A central result of the present formulation is the reconstruction of the complex power functional from the evaluation of the weak form at the solution, showing that the active dissipated power and the magnetic reactive power arise directly from the same integral terms. Through the complex Poynting theorem, the quadratic form is linked to the internal complex power, establishing a direct connection between the variational formulation and measurable quantities such as probe impedance variations. Simulations of realistic layered CFRP configurations, including single- and multi-defect scenarios, confirm that, compared with additive perturbations, the multiplicative model provides enhanced energetic contrast, particularly in strongly anisotropic and interacting defect conditions. Agreement with experimental measurements, supported by a quantitative comparison of dissipated power variations obtained from controlled EC experiments, corroborates the physical relevance and robustness of the proposed complex power functional. Full article

The Qilian Mountains serve as a crucial ecological security barrier in western China, and the soil structural stability of alpine meadows directly affects regional ecological security and the sustainable utilization of grasslands. However, current research on grazing mostly relies on short-term artificially controlled experiments, which differ greatly from the pattern of long-term natural grazing. Herein, this study abandoned the artificially controlled grazing method and selected sampling areas with stable grazing regimes for more than a decade. Taking no grazing (CK) as the control, four treatments were established, including light grazing (LG), moderate grazing (MG), heavy grazing (HG) and extreme grazing (EG). The particle size distribution and stability of mechanically stable and water-stable soil aggregates in different soil layers were determined. Combined with environmental and biological factors, the effects of grazing on the structure and stability of soil aggregates were elucidated. The results showed that no grazing improved the mechanical stability of soil aggregates but reduced their water stability. Light and moderate grazing maintained a balanced and resistant soil structure, with the surface soil being more fragile than the subsurface soil. Heavy and extreme grazing led to severe structural degradation, with the subsurface soil being more fragile than the surface soil. Soil aggregate stability was jointly regulated by elevation, soil properties, root biomass, nitrogen forms, mineralization and microbial biomass. In conclusion, from the perspective of soil structural stability and sustainable utilization, light and moderate grazing represent the optimal utilization mode for the alpine meadows of the Qilian Mountains. This mode not only maintains the structural stability of subsurface soil aggregates but also balances biological cementation and physical disturbance, thus avoiding the insufficient water stability under no grazing and the risk of structural fragmentation under heavy or extreme grazing. Environmental and biological factors mediated the divergent responses of mechanical and water stability to different grazing intensities. The findings of this study provide a scientific basis and new insights for the rational grazing management and soil conservation of alpine meadows in the Qilian Mountains. Full article

The deployment of heterogeneous Automated Guided Vehicles (AGVs) in smart manufacturing requires control strategies that can accommodate distinct actuation characteristics and constraints. This paper proposes a Multi-Factor Coupled Parameter-Adaptive Model Predictive Control (MFCP-AMPC) framework. Unlike conventional approaches requiring vehicle-specific tuning, this framework unifies differential-drive, dual-steer, and mecanum-wheel platforms under a single parameter-varying state-space model that respects the specific actuation limits of each topology. A key contribution is the multi-factor coupling mechanism that dynamically adjusts the prediction horizon and weighting matrices based on path curvature, vehicle speed, and tracking error. Experiments on industrial AGV prototypes demonstrate that the framework achieves robust tracking precision under varying payloads. Crucially, by acknowledging physical limits, the framework achieves strict millimeter-level accuracy (RMSE < 7 mm) in quasi-static low-speed complex maneuvers ($v\le 0.3$ m/s), and maintains highly competitive industrial precision (RMSE ≈ 15∼25 mm) under aggressive high-speed tracking ($v\ge 1.0$ m/s). Crucially, the proposed method significantly improves the control input smoothness (Smoothness Index > 0.75), thereby reducing mechanical wear and preventing actuator saturation. Real-time validation (12 ms average solve time on an Intel i7 IPC) confirms its suitability for resource-constrained industrial controllers. Full article

Soil compaction and reduced infiltration capacity are critical constraints limiting soil physical quality and hydraulic functioning in semi-arid vineyard systems subjected to repeated machinery traffic. This study aimed to develop and evaluate a geometry-optimized strip tillage tool designed to improve structural functionality within the compacted root zone while minimizing inter-row disturbance. A U-shaped working body configuration, consisting of two oppositely inclined shanks and a central chisel, was theoretically substantiated and optimized using multifactor analysis. Field experiments were conducted to assess changes in penetration resistance, bulk density, and infiltration rate within the 20–40 cm soil layer under semi-arid conditions. The optimized geometry significantly reduced penetration resistance and bulk density in the trafficked strip, indicating alleviation of mechanical impedance and improved root-relevant physical conditions. Infiltration capacity increased after treatment, indicating enhanced hydraulic continuity within the root zone. Unlike full-width subsoiling, the localized strip intervention preserved inter-row soil stability and limited unnecessary disturbance, which is consistent with conservation-oriented soil management. The results indicate that geometry-optimized strip tillage is associated with improved soil physical quality and hydraulic function within compacted vineyard strips. The operational applicability of the developed implement may also depend on vineyard layout and terrain conditions. The prototype tool was tested under conditions representative of vineyards with standard row spacing and relatively moderate slopes typical for the experimental site. In vineyards with very narrow row spacing, steep slopes, or highly heterogeneous soil conditions, adjustments in working width, shank spacing, or tractor–implement configuration may be required. Future studies should therefore investigate the performance of the optimized geometry under contrasting vineyard configurations, including steep hillside vineyards and high-density planting systems. By linking implement design to quantitative soil structural and hydraulic indicators, this study contributes to the development of vineyard soil management practices for semi-arid perennial cropping systems. Full article

Extended-solvent steam-assisted gravity drainage (ES-SAGD) has emerged as a promising advancement over conventional SAGD for improving the efficiency and sustainability of in situ heavy oil and bitumen recovery. By co-injecting light hydrocarbon or alternative solvents with steam, ES-SAGD integrates thermal and compositional mechanisms to reduce viscosity, accelerate chamber development, and reduce steam–oil ratios. This review synthesizes the current state of knowledge on ES-SAGD, encompassing fundamental transport mechanisms, solvent selection and phase behavior, mass transfer dynamics, laboratory and physical modeling studies, numerical simulation approaches, and field-scale operational experiences. Experimental evidence consistently demonstrates substantial mobility enhancement through solvent-induced dilution, while compositional thermal simulations highlight an improved sweep efficiency and reduced energy intensity relative to steam-only processes. Field pilots further validate accelerated early-time production and significant steam savings, though challenges related to solvent retention, asphaltene stability, and reservoir heterogeneity persist. Key research gaps are identified in solvent transport prediction, formation damage risk, long-term solvent recovery, and integrated economic–environmental optimization. Overall, ES-SAGD offers a viable pathway toward lower-emission, higher-efficiency bitumen production, provided that solvent chemistry, reservoir complexity, and operational controls are carefully managed through continued research and targeted field deployment. Full article

Since the inception of utility-scale wind turbines, there has been a continual increase in the size of the devices used. One drawback of turbine size increase is that the weight of the rotor blades has grown dramatically. Technological advancements have allowed for the creation of light blades to overcome this issue. These lighter rotors are also less stiff than their predecessors and prone to experiencing aeroelastic vibrations that can lead to fatigue damage. Aerodynamic damping occurring during blade vibration has the potential to mitigate those oscillations; thus, understanding its underlying physics provides an extremely useful tool for future blade design. In a series of previous publications, the authors presented a novel reduced-order characterization technique for the oscillatory response of wind turbines, which allows for the analysis of rotor vibrations when excited by wind gust pulses. In this paper, the authors will apply the same gust pulse technique to analyze the physics of blade’s aerodynamic damping, identifying two physical mechanisms. The first acts either as a damper, or as an energy feeder, depending on operational conditions. The second operates in a purely dissipative manner. Results of numerical experiments on several operational scenarios illustrating these behavioral responses will be presented and discussed. Full article

This study develops a kinetic model that captures poly-ether-ether-ketone (PEEK) crystallization over a temperature $T$ window from glass transition ($T_g$) to melting ($T_m$) temperature, and across cooling rates from 5 to ~10³ °C/min. The framework is a parallel dual-Nakamura formulation whose isokinetic parameters {$k_i\left(T\right),n_i,w_i\left(T\right)$} are obtained from a bi-level non-linear regression of isothermal crystallization tests conducted using a flash-differential scanning calorimeter (FSC). The weight $w_i\left(T\right)$ partitions the faster primary and slower secondary crystallization and is represented by a physics-based analytical function that captures its dome-shaped temperature dependence. A maximum isothermally achievable enthalpy function is introduced so that the model predicts enthalpy $\Delta H\left(t\right)$ natively under arbitrary thermal profiles. To extend this isothermal backbone to non-isothermal conditions, two explicit cooling-rate-dependent scalars are introduced, $\omega \left(\dot{T}\right)$ and $\chi \left(\dot{T}\right)$, which shift $w_i\left(T\right)$ and limit attainable crystallinity at high cooling rates respectively. Finally, a rate-dependent induction time relation is added to adjust the onset of crystallization. Calibrating these rate functions against non-isothermal experiments, while keeping the isokinetic parameters fixed, yields a single isothermal–non-isothermal model that predicts $\Delta H\left(t\right)$ under arbitrary $T\left(t\right)$ profiles. Model performance is validated using an interrupted FSC experiment with a multi-segment cooling program that mimics a local transient thermal history of PEEK during additive manufacturing. The sample is cooled through successive constant-rate segments with intermittent quench–remelt cycles to probe the accumulated crystallinity along the path. Without additional fitting, the model predicts the measured enthalpy evolution with R² ≈ 0.95. The framework thus provides a practical route for predicting polymer crystallinity under processing-relevant thermal histories. Full article

The increasing proportion of renewable energy leads to frequent changes in turbine load, making the regulating stage more prone to degradation. Traditional anomaly detection methods lack sufficient sensitivity and generalization. To address this issue, this study proposes a method combining multi-attention gated fusion and physical information learning. A gated fusion mechanism is proposed to adaptively extract and fuse key temporal and feature information. Furthermore, the generalization ability of the model is improved by introducing physical constraints derived from the relationship between pressure, temperature, and valve position. Finally, a dynamic temperature prediction model is established using the multi-output long short-term memory neural network. Experiments using actual power plant data demonstrate that the proposed method effectively improves the accuracy of post-regulating-stage temperature prediction and the sensitivity of anomaly detection. The proposed gating fusion method improves prediction accuracy by 4.6% compared to direct addition, while the fusion of physical information reduces the generalization error by more than 6%. In addition, compared to traditional deep learning and machine learning models, the proposed method improves anomaly detection accuracy by at least 3.9%. This research is of great significance for the safe operation of thermal power units and the power grid. Full article

Against the global carbon neutrality target, predictive maintenance (PdM) of automotive engines represents a core technical strategy to advance the sustainable development of the automotive industry. Conventional single-modal diagnostic approaches for engine abnormal sound defects suffer from low accuracy and weak anti-interference capability. Existing multi-modal fusion methods fail to deeply mine the physical coupling between cross-modal features and often entail excessive model complexity, hindering deployment on resource-constrained on-board edge devices. To resolve these limitations, this study proposes a Physical Prior-Embedded Cross-Modal Attention (PPE-CMA) mechanism for lightweight multi-modal fusion diagnosis of engine abnormal sound defects. First, wavelet packet decomposition (WPD) and mel-frequency cepstral coefficients (MFCC) are integrated to extract time-frequency features from engine audio signals, while a channel-pruned ResNet18 is employed to extract spatial features from engine thermal imaging and vibration visualization images. Second, the PPE-CMA module is designed to adaptively assign attention weights to audio and image features by exploiting the physical coupling between engine fault acoustic and visual characteristics, enabling efficient cross-modal feature fusion with redundant information suppression. A rigorous theoretical derivation is provided to link cosine similarity with the physical correlation of engine fault acoustic-visual features, justifying the attention weight constraint (β = 1 − α) from the perspective of fault feature physical coupling. Third, an improved lightweight XGBoost classifier is constructed for fault classification, and a hybrid data augmentation strategy customized for engine multi-modal data is proposed to address the small-sample challenge in industrial applications. Ablation experiments on ResNet18 pruning ratios verify the optimal trade-off between diagnostic performance and computational efficiency, while feature distribution analysis validates the authenticity and effectiveness of the hybrid augmentation strategy. Experimental results on a self-constructed multi-modal dataset show that the proposed method achieves 98.7% diagnostic accuracy and a 98.2% F1-score, retaining 96.5% accuracy under 90 dB high-level environmental noise, with an end-to-end inference speed of 0.8 ms per sample (including preprocessing, feature extraction, and classification). Cross-engine and cross-domain validation on a 2.0T diesel engine small-sample dataset and the open-source SEMFault-2024 dataset yield average accuracies of 94.8% and 95.2%, respectively, demonstrating strong generalization. This method effectively enhances the accuracy and robustness of engine abnormal sound defect diagnosis, offering a lightweight technical solution for on-board real-time fault diagnosis and in-plant online quality inspection. By reducing engine fault-induced energy loss and spare parts waste, it further promotes energy conservation and emission reduction in the automotive industry. Quantified experimental data on fuel efficiency improvement and carbon emission reduction are provided to substantiate the ecological benefits of the proposed framework. Full article

Virtual tours are increasingly used in education, particularly when access to real environments is limited. This study examined how display mode and representation format affect subjective user experience in an educational virtual tour of a hospital operating room. A within-subject 2 × 2 design compared two representation formats (360° photographs vs. 3D models) and two display modes (desktop PC vs. immersive virtual reality using Meta Quest 2). Eighty-four university students completed the four visualization conditions and evaluated each experience using an adapted version of the QUXiVE questionnaire. Descriptive statistics and internal consistency indices were calculated, and each questionnaire dimension was analyzed using a two-way repeated-measures ANOVA with display mode and representation format as within-subject factors. A significant main effect of display mode was found for presence, engagement, immersion, flow, emotion, judgment, physical consequences, and perceived educational usefulness (all p < 0.001), but not for usability (p = 0.273). A significant main effect of representation format was observed for presence (p = 0.003), emotion (p = 0.018), and perceived educational usefulness (p = 0.015), whereas no significant interaction effects were found. These findings indicate that immersive VR had the strongest and most consistent effect on subjective user experience across both 360° and 3D virtual tours, although it was also associated with higher physical-consequence scores. By contrast, the effect of representation format was more limited. Overall, both approaches appear to be complementary educational resources, depending on pedagogical goals, available infrastructure, and desired levels of interactivity. Full article

The NA64 experiment at the CERN H4 beamline recently started a high-energy positron-beam program to search for light dark matter particles through a thick-target, missing-energy measurement. To fulfill the energy resolution requirement of the physics measurement ${\sigma }_E/E\simeq 2.5\%/\sqrt{E\left[\mathrm{GeV}\right]}\oplus 0.5\%$ and cope with the constraints and performance requests of the NA64 setup, a new high-resolution homogeneous electromagnetic calorimeter PKR-CAL has been designed. The detector is based on PbWO₄ crystals, each read by multiple SiPM sensors to maximize the light collection. The PKR-CAL design has been optimized to mitigate and control unavoidable SiPM saturation effects at high light levels, as well as to minimize the gain fluctuations induced by instantaneous variations of the H4 beam intensity. The R&D program culminated in the construction of a small-scale prototype, POKERINO. In this work, we present the results from the experimental characterization campaign of the POKERINO, aiming at demonstrating that the obtained performances are compatible with the application requirements. Full article

Accurate yaw alignment is critical for maximizing power capture in horizontal-axis wind turbines, as even moderate yaw misalignment leads to significant aerodynamic losses, increased actuator usage, and accelerated mechanical wear. This research paper proposes a hybrid smart yaw control system for small-scale wind turbines that combines real-time measurements with short-term wind direction prediction to improve alignment accuracy, operational reliability, and energy efficiency under realistic operating conditions. The system integrates four wind direction information sources, such as physical wind vane sensing, live online weather data, forecast data, and a data-driven prediction module within a structured priority framework (VANE → LIVE → FORECAST → AI), to ensure continuous yaw control during sensor or communication unavailability. The prediction module is based on a long short-term memory (LSTM) neural network trained in MATLAB using live data from an online platform, with sine–cosine encoding employed to address the circular nature of directional data. The yaw controller incorporates a ±15° deadband, dwell-time logic, shortest-path rotation, and cable-safe constraints to reduce unnecessary actuation while maintaining effective alignment. The proposed system is validated through MATLAB/Simulink simulations and real-time microcontroller-based experiments using a stepper motor-driven nacelle. Compared with conventional vane-based yaw control, the hybrid AI-assisted approach reduces the average yaw error by approximately 35–45%, maintains a yaw error within ±15° for more than 90% of the operating time, increases average electrical power output by 3–5%, and reduces yaw motor energy consumption by 10–15%, while decreasing corrective yaw actuation events by 30–40%. These results demonstrate that integrating an LSTM-based wind direction predictor with multi-source wind data provides a robust, low-cost, and practically deployable yaw control solution that enhances energy capture and mechanical durability in small-scale wind turbines. Full article

The modern high-pressure lifestyle has led to an increasing emphasis on the healing construction of residential spaces, while air, as an important environmental factor, has received little attention in terms of situational healing experiences within the context of residential culture. Employing grounded theory, this study develops a theoretical model to explain the mechanism through which indoor air environments influence the healing benefits of residential spaces. Guided by the dynamic interaction process of “physical attributes–embodied cognition–behavioral regulation–social context”, the analysis focuses on human embodied perception and emotional responses to indoor air environments as the foundation for healing effects. It highlights the joint role of behavioral regulation and social context, ultimately affecting four levels of healing benefits. Furthermore, it systematically elaborates a theoretical model for embodied interactive residential air experiences, expanding healing environment theory from a contextual air experience perspective, and providing new research paradigm and insights for promoting healing benefits in residential settings. Full article

Master–slave bilateral teleoperation systems face several practical challenges, including model uncertainties, time-varying communication delays, and environment-induced force disturbances. To address these issues, this paper proposes an adaptive super-twisting sliding-mode control scheme to achieve high-precision position tracking and real-time force-feedback synchronization. First, joint-space dynamic models are established for both the master and the slave manipulators, and a passive impedance model is adopted to characterize the interaction dynamics at the operator–master and environment–slave interfaces. Second, to attenuate measurement noise in the environment interaction force, a first-order low-pass filter is used to preprocess the raw force measurements, and a radial basis function neural network (RBFNN) is employed to approximate the environment torque online. Furthermore, a super-twisting sliding-mode controller is developed and combined with an adaptive law to compensate online for system uncertainties, including dynamic parameter variations and environment-induced force disturbances. The stability of the resulting closed-loop system is rigorously analyzed using Lyapunov stability theory. Finally, the effectiveness of the proposed method is validated through numerical simulations, virtual experiments conducted in the MuJoCo physics engine, and real-world hardware experiments. The results show that the proposed strategy achieves accurate position synchronization and force tracking while maintaining stable haptic interaction in the presence of bounded time-varying delays, parameter uncertainties, and external disturbances. Full article

Groundwater contamination caused by dense non-aqueous phase liquid (DNAPL) has long been recognized as a persistent environmental challenge, particularly in fractured porous media. DNAPL migration is highly uncertain due to the heterogeneity and complexity of fracture networks, which complicates risk assessment and remediation design. This paper begins with an overview of mathematical models for multiphase flow migration in fractured media, followed by a systematic analysis and classification of DNAPL migration mechanisms based on laboratory experiments and numerical simulations. Subsequently, key challenges in current DNAPL remediation practices are discussed, including difficulties in monitoring and characterizing fractured aquifers, limited delivery and utilization efficiency of remedial agents, and the back-diffusion of DNAPL from low-permeability zones. Based on this analysis, three primary DNAPL remediation approaches—physical, chemical, and biological methods—are reviewed and evaluated. Finally, future research directions for understanding DNAPL migration and improving remediation strategies in fractured media are proposed. Overall, this review bridges mechanistic knowledge, simulation research, and remediation practice, providing insights that contribute to future technological progress and management decision-making in DNAPL-contaminated fractured aquifers. Full article