Search Results (1,239)

The article deals with the further development of national innovative subway cars with asynchronous electric drives and energy recovery systems through the introduction of capacitive energy storage. It has been determined that the assessment of the effectiveness of existing concepts for energy processes control of subway cars with asynchronous electric drives and capacitive energy storage under identical specified conditions remains a relevant issue. Five of the most promising concepts for managing energy processes were selected and idealized. Oscillograms of energy flows for the selected concepts are presented. Parameters for evaluating the effectiveness of the selected control concepts are presented. The scientific novelty lies in the development of a procedure for selecting a rational concept for controlling energy processes in subway rolling stock with asynchronous electric drives and CES, based on the application of a unified comparative analysis system using a comprehensive evaluation criterion. A scheme for replacing subway cars with asynchronous electric drives and capacitive energy storage is presented, and a mathematical model of energy flow processes for traction and regenerative braking modes has been developed based on this scheme. Algorithms for controlling energy processes between asynchronous electric drives, capacitive energy storage devices, and contact networks have been developed for each of the selected concepts. The efficiency of each of the five selected concepts for the same specified operating conditions of the subway cars, parameters of the asynchronous traction electric drive and capacitive energy storage device has been investigated using the developed mathematical model and the formulated comprehensive evaluation criterion. It was established that it is possible to save up to 18% of the electricity consumed from the contact network per braking-acceleration cycle under the specified operating conditions, parameters of the subway cars, asynchronous traction electric drive, and capacitive energy storage device. An additional possibility exists to reduce the installed power of the power supply system equipment by up to 33.5% under the specified operating conditions of a subway train with the proposed technical characteristics. It has been determined that the most rational concept for controlling energy processes in subway cars with asynchronous electric drives and capacitive energy storage is the fifth concept, which allows the use of stored energy from regenerative braking in both normal and emergency operation of the subway power supply system. Full article

Efficient management of soil–water resources is critical for global food security under intensifying climatic and demographic pressures. This review provides a comprehensive synthesis of artificial intelligence (AI) and distributed deep learning methodologies applied to soil–water interactions in precision agriculture. The physical and hydraulic foundations of soil–water systems—including water retention, unsaturated flow governed by the Richards equation, and soil degradation processes—are examined and situated within a unified framework of AI-based modeling and decision support. Classical machine learning (ML) algorithms (Random Forests, Support Vector Machines, gradient boosting) and deep learning architectures (convolutional neural networks, long short-term memory networks, transformers) are evaluated with respect to their capacity to predict soil moisture dynamics, estimate hydraulic properties, support smart irrigation scheduling, and generate digital soil maps at field-to-regional scales. Distributed training paradigms, federated learning for privacy-preserving multi-farm analytics, and edge AI deployment on low-power IoT hardware are assessed as enabling infrastructures for scalable agricultural intelligence. This review further addresses explainability, uncertainty quantification, and ethical dimensions inherent to AI-driven agricultural systems. Key challenges—including training data scarcity in data-poor regions, model interpretability, integration with physics-based hydrological models, and real-time deployment constraints—are critically discussed. Prospective research directions encompass physics-informed neural networks, foundation models for earth observation, autonomous digital twins of soil–water systems, and federated learning architectures aligned with data sovereignty frameworks. The synthesis underscores AI’s transformative potential for sustainable agricultural water management while delineating the technical and sociotechnical barriers that must be resolved to realize this potential at a global scale. Full article

Canned motor pumps are widely utilized due to their distinct advantage of a completely leakage-free structure. Among them, an integrated impeller–rotor configuration is employed in the axial-flux canned motor pump, resulting in a shorter axial length and higher power density. This novel configuration allows for easy integration into space-constrained systems, such as electric vehicles, aerospace applications, and liquid-cooled servers. However, research on the internal flow characteristics of these pumps remains scarce. To address this gap, the present study investigates the internal flow across various flow rates. Numerical simulations are validated against experimental data. The average error remains below 2%. The pump achieves a peak efficiency of 68.6% at the design condition, but experiences efficiency drops of 15.0 and 25.2 percentage points under 0.5Q_d and 1.5Q_d, respectively. Results demonstrate that flow rates significantly govern internal characteristics. These include pressure, velocity, and entropy distributions, along with vortex structures and pressure fluctuations. Notably, operating at off-design conditions can intensify the internal pressure fluctuations by up to a factor of 29.4. Entropy analysis identifies major losses on blade suction sides and diffusers. These findings provide crucial hydrodynamic guidelines for low-noise thermal management systems in electric vehicles and ensuring high-reliability cooling loops in aerospace and liquid-cooled servers. Full article

Background/Objectives: Preoperative differentiation among benign thyroid nodules, follicular variant papillary thyroid carcinoma (FV-PTC), and classical papillary thyroid carcinoma (C-PTC) remains clinically challenging. FV-PTC is particularly difficult to identify due to its substantial sonographic and cytological overlap with both benign nodules and other malignant subtypes, frequently resulting in overtreatment or delayed diagnosis. This study aimed to develop and validate an interpretable multimodal model for accurate three-class discrimination using routine ultrasound images, with a specific focus on improving the preoperative identification of FV-PTC. Methods: This retrospective study included 479 pathologically confirmed thyroid nodules from 462 patients. Conventional ultrasound features and radiomics features extracted from grayscale ultrasound and color Doppler flow imaging were used to construct three predictive models: a Conventional Ultrasound model (conventional ultrasound features only), a Radiomics model (radiomics features only), and an Integrated model (combined features). Each model was trained using four machine learning classifiers. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. Model interpretability was assessed using SHapley Additive exPlanations (SHAP) analysis, and clinical usefulness was evaluated using decision curve analysis (DCA). Results: The support vector machine (SVM)-based Integrated Model achieved the best overall performance. In the independent testing cohort, the AUCs were 0.853 for FV-PTC, 0.882 for C-PTC and 0.928 for benign nodules. The Integrated Model showed the greatest improvement for FV-PTC, with a ΔAUC of 0.141 compared with the Conventional Ultrasound Model. SHAP (SHapley Additive exPlanations) analysis identified wavelet-HL_gldm_Dependence and wavelet-HH_glcm_InverseVariance as the two most important radiomics predictors in both the Radiomics Model and the Integrated Model, demonstrating robust cross-model stability and high discriminative power. Conclusions: The SVM-based Integrated Model demonstrated promising performance for three-class classification of thyroid nodules and enhanced the preoperative identification of FV-PTC. This approach may provide an interpretable and noninvasive decision-support tool for refining subtype-specific risk stratification and supporting individualized clinical management. Full article

Nature provides efficient strategies for fluid transport and thermal regulation through evolved structural features. This review summarizes recent progress in biomimetic thermal–fluid structures for enhancing fluid flow and heat transfer, with emphasis on the links among biological inspiration, engineering geometry, transport mechanisms, and application performance. Representative designs are classified into tree-like branching and fractal networks, compact hexagonal layouts, and bio-inspired curved morphologies, including riblets, grooves, fins, fluctuating channels, and TPMS structures. Their enhancement mechanisms involve flow redistribution, boundary-layer disturbance, secondary-flow and vortex generation, local acceleration, enlarged heat-transfer area, drag reduction, and compact flow organization. Applications using biomimetic structures are assessed in detail, such as in battery thermal management, electronic cooling, etc. The reviewed studies indicate that biomimetic structures can improve temperature uniformity, suppress hotspots, and enhance thermohydraulic performance, but the gains may be accompanied by pressure-drop or pumping-power penalties. Therefore, coupled thermal–hydraulic evaluation is essential for objective comparison. Key challenges of practical usage are identified in mechanism-based design, manufacturability, reliability, etc. This work establishes the guidance for translating biological forms into practical thermal–fluid structures with balanced efficacy. Full article

To enhance the thermal management of lithium-ion batteries in new-energy vehicles, various bio-inspired liquid-cooled plate channel designs were investigated to improve hotspot dissipation within the laminar flow regime. A series of three-dimensional numerical simulations were conducted to compare leaf vein-, tree branch-, honeycomb-, and spider web-inspired channels, followed by further optimization to improve thermohydraulic performance. The selected optimized bio-inspired channels were subsequently evaluated against conventional structures. Simulation results indicate that the honeycomb-inspired liquid-cooled plate channel achieved the best performance, followed by the tree branch- and spider web-inspired channels, which exhibited comparable thermohydraulic performance. The leaf vein-inspired channel demonstrated the lowest performance. The key design element for enhanced heat dissipation is the inclusion of longitudinal branch channels, which minimize flow zones with near-zero velocity and effectively mitigate local hotspots. Furthermore, the combination of longitudinal and inclined branch channels can redirect flow direction and enhance fluid mixing. Compared with the conventional channel widely adopted in existing studies, within the Reynolds number range of 260 to 920, the optimized honeycomb-inspired liquid-cooled plate channel achieves a 44.0–49.3% increase in Nusselt number and an 81% enhancement in comprehensive performance metric. Concurrently, thermal resistance is diminished by 2.6–9.2%, and pumping power is reduced by 50.0–56.8%. Full article

Background: Exercise plays a crucial role in the prevention and management of type 2 diabetes. During self-motion, optic flow provides visual information about heading direction and influences postural control. This study investigated postural responses and muscle activation in individuals with type 2 diabetes exposed to optic flow stimuli simulating self-motion, and examined whether these responses varied according to habitual physical activity over 12 months. Methods: Surface electromyographic (EMG) and stabilometric data were collected from 23 individuals during quiet standing under different visual motion conditions. Participants were classified as physically active or inactive based on standardized criteria. EMG activity was recorded bilaterally from the tibialis anterior and soleus muscles at baseline, 6, and 12 months. Center of pressure (COP) displacement was measured using two force platforms. Results: Stabilometric analysis revealed a significant effect of visual stimulus on COP displacement in both antero-posterior and medio-lateral directions, as well as on COP speed, indicating that optic flow modulates postural control. COP speed changes over time differed by sex, while medio-lateral sway showed time-dependent variations across sides and physical activity groups. EMG analysis showed a significant effect of visual stimulus on soleus activation, with no consistent effects for tibialis anterior. Conclusions: Optic flow significantly modulated postural control and lower-limb muscle activation in individuals with type 2 diabetes. Preliminary differences in response profiles associated with habitual physical activity level were observed, though these should be interpreted cautiously given the exploratory nature of the study. Larger, adequately powered studies are warranted to further investigate these associations. Full article

Lattice-based heat sinks have attracted increasing attention as volumetric thermal management architectures for forced-convection cooling of high-power electronic systems. In contrast to conventional plate-fin, pin-fin, and straight-channel configurations, lattice geometries promote three-dimensional flow–solid interaction through interconnected ligament networks that modify boundary-layer development, wake formation, and internal heat-spreading pathways. This review synthesizes recent experimental and numerical studies to examine the thermo-fluid mechanisms governing lattice performance, with emphasis on the coupled influence of porosity, ligament dimensions, topology, orientation, and channel confinement on heat-transfer enhancement and hydraulic resistance. The analysis indicates that while lattice structures can increase average Nusselt number and improve temperature uniformity, these gains are intrinsically linked to pressure-drop penalties associated with flow tortuosity and form drag, resulting in regime-dependent thermal-hydraulic behavior. Apparent discrepancies reported across the literature are frequently attributable to differences in geometric definition, Reynolds-number normalization, and boundary-condition specification rather than to inconsistencies in physical mechanisms. By consolidating geometric scaling, performance metrics, manufacturing considerations, and system-level constraints, this review clarifies the conditions under which lattice heat sinks may provide net benefit relative to conventional cooling technologies and identifies key research directions required to support application-relevant design and evaluation. Full article

Intelligent completion technologies, including Inflow Control Valves (ICVs), have become increasingly important for remotely managing zonal production in complex well architectures. However, quantifying flow rates and phase fractions in such systems remains challenging due to space constraints and the harsh downhole environment, which limit the deployment of conventional sensors. Distributed Acoustic Sensing (DAS) provides a promising solution by converting standard fiber-optic cables into dense arrays of acoustic sensors. While DAS has been successfully applied in applications such as integrity monitoring and leak detection, its use for direct two-phase flow characterization within intelligent completions remains largely unexplored. In this study, we present a DAS-based methodology to monitor and analyze oil–water two-phase flow in horizontal experiments that mimic field conditions. Acoustic data collected from DAS are transformed into time–frequency spectrograms using Short-Time Fourier Transform (STFT) to extract dynamic spectral features. These features are then correlated with pressure drop across the ICV and flow rate, revealing distinct frequency band behaviors associated with fluid changes. To quantify flow characteristics, a power-law model is trained using spectral features to predict flow rate and phase fractions. The results demonstrate strong predictive capability for pressure drop and flow rate under controlled laboratory conditions, highlighting the potential of DAS for multiphase flow diagnostics in field applications with intelligent completions, while water cut prediction remains challenging due to the complex and non-unique relationship between flow conditions and DAS response and is left for future work. This research not only provides new insights into the acoustic response of oil–water flows but also introduces a data-driven framework for leveraging DAS in real-time flow monitoring and control within ICV-equipped completions. Full article

Accurate short-term forecasting of residential electricity demand is increasingly important for smart distribution systems, particularly in the context of demand-side management and flexibility-oriented grid operation. In this study, a high-resolution forecasting framework is proposed in which household electricity demand is classified into fixed, shiftable, and adjustable load categories and forecasted together with total load. A one-minute-resolution synthetic residential load dataset is generated using the Centre for Renewable Energy Systems Technology (CREST) demand model for households with two to five occupants over a 31-day winter period in January. The appliance-level demand data are grouped according to operational characteristics and integrated into a representative four-bus distribution feeder. Minute-level power flow analysis is then performed to calculate technical losses, which are incorporated into the forecasting dataset together with meteorological variables (temperature, wind speed, and solar irradiance) and temporal descriptors. Using this multi-input structure, random forest (RF), support vector machine (SVM), feed-forward neural network (FFNN), and long short-term memory (LSTM) models are comparatively evaluated for the prediction of fixed, shiftable, adjustable, and total residential loads. Model performance is assessed using root mean square error (RMSE) and Pearson correlation coefficient (R), while mean absolute error (MAE) is additionally reported for the final test set. The results show that the LSTM model provided the most consistent overall forecasting performance, particularly for shiftable, adjustable, and total load estimation, while RF yielded competitive results for fixed-load correlation and short-window forecasting in Buses 1 and 2. In contrast, SVM and FFNN exhibited weaker generalization performance across several load categories. The proposed framework provides a practical foundation for the development of dynamic pricing mechanisms that consider load-type-based controllability levels. Overall, the findings demonstrate that integrating load categorization with meteorological, temporal, and technical loss information provides a robust and reproducible framework for smart grid applications such as demand-side management, peak load mitigation, and flexibility-aware residential load analysis. Full article

High-power-density insulated gate bipolar transistor (IGBT) modules in new energy vehicles require efficient heat dissipation and good temperature uniformity. This study proposes a tri-objective topology optimization method for a liquid-cooled heat sink using average temperature, temperature variance, and flow dissipation as the objective functions. A two-dimensional model was established in COMSOL to investigate the effects of the fluid volume fraction, inlet pressure, and optimization-weight distribution on the optimized topology and thermo-hydraulic performance. The results show that a fluid volume fraction of 0.4 and an inlet pressure of 15 Pa provide the best overall performance. Compared with the bi-objective design, introducing temperature variance as a third objective reduces temperature variance by 50.23%, with only a 1.08% increase in average temperature. Three-dimensional simulations further verify the optimized design. Compared with a conventional pin-fin heat sink, the topology-optimized structure reduces the average temperature by 10.43% and the temperature variance by 44.37%, while increasing the flow dissipation by 49.57%. These results show that tri-objective topology optimization is an effective method for improving the thermal management of high-power-density IGBT modules. Full article

Hydrogen fuel cells offer strong potential for decarbonizing aviation, yet their megawatt-scale integration is limited by thermal management system (TMS) challenges. In low-temperature Proton Exchange Membrane Fuel Cell (PEMFC) systems, the heat exchanger (HEX) is the key TMS component influencing thermal efficiency, mass, and reliability. While prior work has focused on thermo-hydraulic optimization, structural behavior under flight conditions remains insufficiently addressed. This study introduces a coupled CFD–FEA methodology for a nacelle-integrated, megawatt-class plate–fin HEX. The model captures the effects of non-uniform thermal loads, constrained thermal expansion, and dynamic excitation. Local flow-induced vibrations are assessed through pre-stressed modal analysis, and global dynamic behavior is predicted using a homogenized approach. Results show that thermally induced stresses dominate over pressure loads, and the introduction of coolant-fin geometries with suitable expansion tolerances mitigates stress and resonance risks. The approach provides design guidance for structurally robust, vibration-tolerant, and aero-thermally efficient HEXs for next-generation PEMFC-powered aircraft. Full article

To address heat accumulation, localized hot spots, and non-uniform temperature distribution in large-capacity lithium iron phosphate energy storage battery modules under high ambient temperature and high-rate charge/discharge conditions, this study proposes a fin-enhanced phase change material (PCM)-air hybrid thermal management structure for a 100 Ah prismatic lithium iron phosphate battery and a 2P18S energy storage battery module. First, the battery thermal model is validated using single-cell experimental data reported in the literature. Subsequently, a three-dimensional transient fluid–solid coupled heat transfer model is established by considering transient battery heat generation, PCM solid–liquid phase change, air-side flow and heat transfer, and temperature-dependent thermophysical properties. User-defined functions are employed to implement the transient heat source and temperature-dependent material properties. Under identical boundary conditions, the thermal management performances of three configurations, namely Fin-Air, PCM-Air, and Fin-PCM-Air, are compared. The effects of ambient temperature (20 °C, 25 °C, and 30 °C) and inlet air velocity (1 m/s, 2 m/s, and 3 m/s) on the maximum module temperature, temperature uniformity, PCM liquid fraction evolution, and flow field distribution are quantitatively analyzed. The results show that, compared with the Fin–Air system without PCM and the PCM-Air system without fins, the Fin-PCM-Air configuration reduces the maximum module temperature by 1.57% and 0.25%, respectively, at an ambient temperature of 30 °C and an inlet air velocity of 3 m/s. After four charge–discharge cycles, the peak maximum temperature of the module is approximately 38.56 °C, and the peak maximum temperature difference remains below 3.6 K, indicating good temperature uniformity and latent heat buffering capability. In addition, the air velocity trade-off analysis indicates that increasing the inlet air velocity can improve cooling performance but also increases the air-channel pressure drop and fan power consumption. Therefore, the Fin-PCM-Air structure is more suitable for high-thermal-load conditions, and its practical application should comprehensively consider cooling benefits, additional mass, manufacturing cost, and long-term reliability. This study provides a reference for the design and engineering application of hybrid thermal management structures for large-capacity energy storage battery modules. Full article

Coal combustion in large-scale power plants is a major source of atmospheric pollution, including SO₂, NO_x, particulate matter, and the halogen acids HCl and HF. Predicting HCl and HF emissions is challenging due to interactions among fuel composition, fly ash chemistry, combustion conditions, and flue gas dynamics. In this study, artificial neural network (ANN) models are developed from field experiments at the lignite-fired TPP “Kostolac B”. The models incorporate operational parameters (flue gas temperature and flow rate) and fuel/ash characteristics (moisture and total sulphur in coal and CaO content in ash) to estimate HCl and HF emissions. SHAP analysis identified key variables affecting halogen acid release. The developed ANN models achieved satisfactory predictive accuracy, with the test-set performances of RMSE = 2.05 mg/Nm³, R² = 0.80, and MAPE = 18.7% for HCl prediction, and RMSE = 3.23 mg/Nm³, R² = 0.83, and MAPE = 18.7% for HF prediction. SHAP analysis indicated that CaO content in fly ash and coal moisture are the primary drivers of HCl and HF emissions, while operating conditions and coal sulphur content influence emissions through non-linear interaction effects. The proposed ANN-SHAP framework provides a data-driven approach for emission prediction and interpretation, supporting decision-making in emission management. Full article

To address the critical challenge of excessive junction temperature caused by ultra-high heat flux densities (>100 W/cm²) in deep-sea LED Fish-Attracting Lamp (FAL) arrays, this study proposes a hybrid thermal management scheme integrating interfacial micro-texturing, chimney-effect convection, and heat pipe phase-change heat transfer, achieving the unification of passive high-efficiency heat dissipation and pressure-resistant sealing. The FAL housing structure is reconfigured using topology optimization to construct chimney-effect enhanced flow channels integrated with heat pipe bundle arrays, thereby establishing efficient heat conduction pathways from the Phenolic Resin Substrate (PRS) to the structural periphery. Micro-Element Texture (MET) arrays are fabricated at the PRS thermal interface to enhance interfacial thermal conductance. Based on multi-physics coupled numerical simulation, a parametric mapping model correlating geometric topology with thermal performance is established through response interface methodology, enabling the parametric optimization of micro-texture configurations. A thermal interface performance testing platform is constructed to validate the accuracy and reliability of the numerical model. Experimental results demonstrate that the integrated heat pipe technology effectively suppresses LED junction temperature rise; moreover, groove-type MET arrays oriented perpendicular to the gravity direction not only significantly increase the effective heat dissipation area but also optimize the dynamic characteristics of natural convection. This proposed solution reduces the maximum operating temperature of deep-sea FALs by 6.70% compared with conventional structures, providing an effective engineering solution for thermal structural design of high-power illumination systems. Full article