Search Results (2,583)

The snapshot proper orthogonal decomposition (POD) method relies on synchronously sampled datasets, significantly limiting its utility for analyzing asynchronous measurements in unsteady flow studies. This paper proposes a frequency-domain proper orthogonal decomposition (FDPOD) method tailored for mode extraction and flow field reconstruction from asynchronously sampled data. The FDPOD framework integrates three key components: frequency-domain transformation to decouple phase discrepancies inherent in asynchronous sampling, power spectral density (PSD) analysis combined with segmented ensemble averaging to suppress spectral leakage errors, and eigenvalue decomposition of energy-ranked frequency components to identify dominant coherent structures. Validated through numerical simulations of a subsonic jet and experimental measurements from a low-speed mixed-flow fan, the method demonstrates exceptional performance under asynchronous conditions: cumulative energy errors are reduced to 0.3% across the first 50 modes, while flow field reconstruction achieves 99.5% accuracy. Dominant mode structures exhibit remarkable consistency with those derived from synchronous conditions, with hot-wire measurement errors remaining below 0.03% for both asynchronous and temporally shuffled datasets. These results position FDPOD as a robust and practical tool for analyzing complex unsteady flows where synchronous data acquisition proves impractical, particularly in large-scale or spatially distributed measurement systems. 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

The large-scale integration of electric vehicles (EVs) has made the siting and sizing of electric vehicle-charging stations (EVCSs) a critical challenge in distribution systems, as inadequate deployment may compromise secure network operation due to voltage and thermal limit violations. This problem is formulated as a mixed-integer nonlinear programming (MINLP) model, where discrete variables define EVCS locations and charging capacities expressed in terms of the number of EVs served. To address this problem, this paper proposes a unified parallel AC-feasible optimization framework that maximizes EV hosting capacity while explicitly enforcing all operational constraints of the distribution system. Particle Swarm Optimization (PSO), a Population-based Continuous Genetic Algorithm (PGA), and Monte Carlo (MC) optimization are evaluated under a common decision-variable encoding, objective function, AC power-flow evaluator, and constraint-handling strategy, enabling a fair comparison among methodologies under identical operating conditions. The proposed framework is assessed on a modified 33-bus distribution system considering a representative weekly operating scenario and 100 independent runs. Results show that PSO achieves the highest hosting capacity, integrating up to 1246 EVs and an average of 1213.3 EVs, compared with 1225 and 1196.1 EVs for PGA and 1077 and 1049.4 EVs for MC, respectively. All methodologies exhibit standard deviations below 3%, confirming robust and repeatable performance, while requiring less than 1428 s on average to identify feasible planning solutions. In addition, the parallel implementation reduces computational times by 42.22%. These results demonstrate the effectiveness of the proposed framework for identifying high-capacity EVCS planning solutions while preserving secure network operation. Full article

The growing share of intermittent renewable electricity has increased the need for long-duration storage in industrial energy systems. Meanwhile, many industrial processes still release recoverable low-grade waste heat. Introducing this heat into pumped thermal energy storage (PTES) can improve thermal integration, but industrial waste heat is often unsteady, and its temperature and mass flow fluctuations may disturb the charging process. This study investigates an air-based reverse Brayton PTES system assisted by an industrial hot-water waste heat stream of approximately 100 °C. A dynamic model was developed in Simulink/Simscape. The shaft speed is fixed at 3000 rpm, and a PID controller regulates the molten-salt flow rate to maintain the thermal storage temperature. The results show that increasing the waste heat temperature from 95 °C to 105 °C mainly changes the charging-side heat distribution. The waste heat utilization power increases from 36.0 MW to 37.9 MW, while the regenerator power decreases from 126.8 MW to 122.0 MW. The thermal storage power increases slightly from 117.0 MW to 119.0 MW, with the mechanical input fixed at 81.0 MW. The influence of waste heat temperature is concentrated near the low-temperature heat exchanger, regenerator, and turbine outlet. Under dynamic disturbances, faster temperature ramps increase short-term deviations, but the PID-based molten-salt flow regulation keeps the storage temperature close to 550 °C, indicating that the proposed control strategy can suppress moderate thermal disturbances during charging. When waste heat temperature and mass flow rate vary together, same-direction changes strengthen the disturbance, whereas opposite-direction changes partly offset it. These results clarify the disturbance propagation mechanism of fluctuating industrial waste heat in the PTES charging loop and provide a basis for the dynamic design and temperature-control strategy of waste-heat-assisted PTES systems. Full article

The increasing integration of distributed electricity–heat adjustable resources into distribution networks poses significant challenges for virtual power plant (VPP) dispatch, as conventional aggregation models often neglect network constraints, leading to infeasible or unsafe operation plans. To address this issue, this paper proposes a source-grid-load-storage aggregation optimization method that explicitly incorporates both distribution network power flow constraints and district heating network hydraulic–thermal coupling constraints. The network constraints are integrated into the optimization objective as penalty terms, and the dispatch problem is formulated as a Markov decision process. A deep reinforcement learning framework, combining twin delayed deep deterministic policy gradient (TD3) and deep deterministic policy gradient (DDPG) algorithms, is employed to solve the sequential decision-making problem. Simulation results demonstrate that the proposed method effectively ensures distribution network security and heating quality while maintaining economic efficiency, providing a feasible and safe dispatch strategy for VPPs in coupled electricity–heat systems. Full article

The increasing penetration of renewable energy sources introduces significant variability, low-inertia behaviour, and operational uncertainty into modern power systems, resulting in frequent frequency deviations and degraded dynamic stability. Conventional Load Frequency Control (LFC) approaches based on fixed-parameter PID controllers often exhibit limited disturbance rejection capability under nonlinear and stochastic operating conditions. This study proposes an enhanced LFC framework that integrates a PID controller optimised using the Flower Pollination Algorithm (FPA) with support from a Unified Power Flow Controller (UPFC) and a Redox Flow Battery (RFB) to improve frequency regulation, damping, and robustness in renewable-integrated low-inertia power systems. This study developed a MATLAB/Simulink single-area power system model comprising governor, turbine, and generator-load dynamics to evaluate controller performance under a 0.01 pu step disturbance, stochastic load variations, renewable energy fluctuations, and ±20% parameter uncertainty conditions. The FPA optimally tuned the PID controller gains using the Integral Time Absolute Error criterion to enhance transient response and disturbance rejection capability. Comparative analyses were conducted against conventional PID and fuzzy-based controllers using settling time, overshoot, RMS deviation, ITAE, and mean frequency deviation indices. Simulation results demonstrate that the proposed FPA–PID + UPFC framework significantly outperforms the conventional PID controller by achieving approximately 66.6% settling-time reduction, 72.1% RMS reduction, and 75.5% ITAE reduction. The proposed framework reduced settling time from 18.46 s to 6.16 s and substantially improved damping performance under stochastic disturbances. The coordinated integration of the UPFC and RFB further enhanced transient stability through dynamic power-flow regulation and rapid active-power compensation during disturbances. Sensitivity analysis under parameter uncertainty and stochastic operating conditions confirmed stable and reliable operation under stochastic disturbances and parameter uncertainty conditions. The proposed architecture, therefore, provides an effective, practically applicable solution for secondary frequency regulation in renewable-rich smart grids, low-inertia transmission systems, microgrids, and future distributed power networks. Full article

Modern distribution networks undergo frequent topology reconfiguration, volatile bi-directional flows, and noisy measurements, making five-class line-loss anomaly classification both valuable and challenging. In this study, PC-LossGNN is proposed—a physics-consistent spatiotemporal graph neural network for edge-level classification into Normal, Infrastructure, Documentation, Metering, and Theft. A static topology prior is fused with a measurement-adaptive graph and confidence-aware multi-source features; power-flow physics is injected via residual-guided attention using active/reactive balance, voltage-drop, and ohmic-loss residuals. A dual-path decoder is employed to yield calibrated probabilities and interpretable class evidence, trained under an uncertainty-weighted curriculum objective. On six months of real utility data, macro-F1 of 0.8503 and accuracy of 0.9915 are achieved, surpassing XGBoost, LSTM, GCN, STGCN, and two recent physics-aware spatiotemporal GNN baselines including ST-RGNN and PA-STGCN. Ablation indicates that physics-consistent regularization is pivotal, while adaptive topology and interactive temporal encoding further improve performance. Robustness tests with injected Gaussian noise show more graceful degradation than baselines. These results suggest that PC-LossGNN provides accurate, physically plausible, and interpretable five-way line-loss diagnostics suitable for real-world operations. Full article

Weak grid infrastructure and the absence of flexible storage are among the principal barriers to reliable, low-carbon energy access in sub-Saharan transmission systems. This paper proposes a hierarchical multi-objective framework for the optimal siting and sizing of battery energy storage systems (BESSs), applied to the 130-bus Mali transmission network within the EMERGE project. The upper level employs NSGA-II to simultaneously maximize daily price arbitrage revenue and minimize active power losses; the lower level solves a network-constrained DC optimal power flow with thermal branch limits enforced as hard linear inequalities via the Power Transfer Distribution Factor (PTDF) matrix. Over 500 generations, the framework identifies Bus 91 (SIRAKORO II, 150 kV) as the dominant storage location, achieving a maximum daily revenue of approximately €10,033 at a marginal loss increment of $6.7\times {10}^{-3}$ MWh. The resulting Pareto front gives Mali system planners a quantitative tool for trading off private investment returns against grid-level environmental impact, demonstrating that rigorous network-constrained BESS planning is technically tractable and economically viable in the resource-constrained context of sub-Saharan energy transitions. Full article

The toroidal propeller, as a high-performance propulsor with a unique geometric configuration, presents challenges in parameterizing its complex geometry and conducting design optimization. This paper proposes a hybrid Free-Form Deformation (FFD) based parametric method, which integrates global FFD control with local parameters to achieve flexible and efficient description of the complex surfaces of toroidal propellers. Building upon this, an automated design framework integrating Computational Fluid Dynamics (CFD), a Kriging surrogate model, and a data-driven optimization algorithm is constructed to explore a high-dimensional design space comprising 14 variables. The goal is to minimize torque while satisfying thrust and geometric constraints. Optimization results show that the optimized propeller achieves approximately 3.63% higher propulsive efficiency at the design condition and requires about 4.32% less power for the required thrust, compared with the best design from Design of Experiments (DOE) sampling. Further flow field analysis reveals that the optimized design achieves a more gradual pressure distribution, which effectively suppresses flow separation and cavitation risk, thereby maintaining better performance across a wider operational range. This study provides a systematic parametric modeling method and optimization strategy for the efficient design of toroidal propellers, demonstrating clear engineering application value. Full article

Electric dipole antennas can be designed in a variety of geometries and applied across a wide range of configurations. Appropriately designed dipole antennas can provide deep tissue penetration and low radiofrequency (RF) power deposition in magnetic resonance imaging (MRI), making them attractive for applications requiring safe and effective RF transmission in deep regions. On clinical 3 T MRI systems, however, conventional dipoles are too large in size for practical imaging of the head. Inspired by telecommunications designs, the present work adapts meandered dipoles (where the conductor is folded to shorten the antenna) with the resonance frequency controlled through trace geometry. Additionally, multi-channel configurations are considered to improve RF power transmission. A straight dipole was progressively transformed into meandered geometries and characterized using benchtop measurements and electromagnetic simulations. Analyses evaluated frequency response, near-field behavior, power-flow directionality, and distributions of local tissue heating and transmitted RF magnetic field in multi-channel arrays. A four-channel parallel-transmit (pTx) prototype was also used to show the feasibility of dipole-based head imaging at 3 T. The present work demonstrates a practical implementation of compact, low-heating dipole arrays for head MRI, with potential for extension to ultra-high-field or multinuclear imaging. Full article

This paper proposes a data-driven framework for simulating turbocharger (TC) failure scenarios and modelling specific fuel oil consumption (SFOC) degradation in two-stroke low-speed marine diesel engines. A GaussianCopula model was fitted to the joint distribution of fifteen variables, using approximately eleven months of operational sensor data (n = 480 clean records, 4 h interval, January–December 2014) taken from a container ship. Three physically motivated failure scenarios were produced: turbine blade fouling, bearing wear and compressor surge. Predictive models trained on the real dataset achieved R² = 0.9998 for TC RPM and R² = 0.984 for fuel flow when using Gradient Boosting with 5-fold cross-validation. Feature importance analysis showed that the dominant determinants of TC speed were scavenging air intake pressure (35.3%) and engine power (MCR, 31.3%). Shaft power (45.5%) and TC RPM (19.3%) together explained most of the fuel consumption variance. Simulated failure scenarios produced SFOC increases of +6.6% (fouling), +9.6% (surge), and +13.3% (bearing wear) when compared to a normal operating baseline of 202 g/kWh, which is in line with published empirical data from MAN B&W engine performance curves. An IsolationForest anomaly detector trained only on normal operating samples flagged failure scenario records at a rate of 17.5–23.7%, which demonstrates that moderate-sensitivity early warning detection is feasible from routine sensor streams. The results show that TC condition monitoring could serve as a leading indicator of fuel-efficiency degradation. This has significant implications for condition-based maintenance planning and CII (Carbon Intensity Indicator) compliance. Full article

High penetration of distributed photovoltaic (PV) generation has intensified voltage violations and stochastic voltage fluctuations in distribution networks, while existing voltage–var control methods still have limitations in terms of communication dependence, scalability, and edge deployment. To address these issues, this paper proposes a stability-constrained voltage–var self-optimizing control method for distribution networks based on the Bandit-Guided Online Self-Tuning Group Relative Policy Optimization (BOST-GRPO) algorithm. First, based on the LinDistFlow linearized power-flow model, a communication-free, decentralized, and locally observable reinforcement learning control environment is constructed, enabling each node to independently generate reactive power regulation commands using only local voltage measurements. Second, a contraction-mapping-based stability constraint is embedded into the policy output layer, theoretically guaranteeing the local exponential convergence of nodal voltage deviations around the equilibrium point and reducing the risk of voltage instability caused by overly aggressive policy actions. Meanwhile, device capacity constraints are incorporated into the policy output through a tanh-based action mapping, ensuring the physical feasibility of control commands. On this basis, BOST-GRPO realizes the online self-tuning of key hyperparameters within a single training process through a Bandit-guided mechanism, thereby avoiding the repeated training overhead caused by traditional offline hyperparameter tuning. Simulation results on the IEEE 33-bus system show that the proposed method outperforms benchmark reinforcement learning algorithms in final test cost, voltage deviation suppression, steady-state error, and regulation speed. Further tests under sensitivity matrix mismatch, different initial voltage disturbance intensities, and the extended IEEE 69-bus system demonstrate that the proposed method achieves good robustness and scalability. Full article

With the rising integration of high-performance GPUs, localized hotspots induced by discrete heat sources present severe thermal challenges. Traditional single-inlet–single-outlet liquid cold plates can scarcely meet the heat dissipation requirements of inhomogeneous high heat fluxes. This study systematically investigates the effects of nine multiple-inlet–multiple-outlet (MIMO) configurations, ranging from single-inlet–single-outlet to three-inlet–three-outlet, on cold plate hydrothermal performance. An innovative stepwise optimization strategy, topology optimization (TO)-driven channel layout combined with fin-enhancement (FE)-based fine regulation, is proposed and verified to precisely regulate surface temperature distribution of discrete heat sources. The results show that the three-inlet–three-outlet configuration C-3 exhibits the optimal comprehensive performance among the nine configurations. Compared with the worst configuration A-2, C-3 reduces the pressure drop by 58.37% to only 147.18 Pa and yields the highest PEC, striking the optimum trade-off between heat transfer enhancement and fluid flow resistance. Through multi-inlet flow distribution and multi-outlet heat extraction, C-3 accurately suppresses heat accumulation in high heat flux regions, limiting the maximum temperature to merely 29.82 °C and drastically narrowing the substrate temperature difference from 8.69 °C to 2.12 °C. In comparison with the traditional cold plate (TCP), the optimized cold plate (OCP) realizes a 17.42% increase in performance evaluation criterion (PEC). Furthermore, the fin-enhanced optimized cold plate (FEOCP) reduces the temperature standard deviation by 54.15% relative to TCP, significantly enhancing temperature uniformity with only an additional pressure drop penalty of 5.43%. This study reveals the regulation mechanism of MIMO configurations on the flow field distribution of liquid cold plates and verifies the effectiveness of the TO-FE optimization framework, thus providing highly valuable engineering solutions for the high-efficiency, uniform-temperature and low-resistance heat dissipation of high-power electronic devices. Full article

To address the operational challenges posed by the high penetration of photovoltaic systems in distribution networks—such as system congestion, voltage violations, and increased distribution losses—this study proposes a spatiotemporal sparsified dynamic reconfiguration scheduling method considering practical implementation in real distribution system operations. The proposed framework comprises two complementary sparsification mechanisms. Spatial sparsification is achieved by clustering hourly net-load distributions in a high-dimensional net-load space to aggregate characteristic net-load patterns, thereby restricting power flow evaluations and configuration screening to a small set of representative patterns and substantially reducing the computational burden. Temporal sparsification is realized by solving an integer linear programming problem to optimize the reconfiguration schedule under a daily reconfiguration frequency constraint, which optimizes the reconfiguration timing while mitigating excessive switching operations. Numerical experiments under deterministic forecast assumptions demonstrated that the proposed method can effectively eliminate congestion and voltage violations while achieving loss reduction by 4.56% and 27.4% respectively in two scenarios from the conventional method with the computational scalability significantly improved. Full article