Search Results (342)

With the large-scale development of renewable energy such as wind, solar and ocean energy, the demand for energy storage is more urgent. Pumped hydro energy storage (PHES) is one of the fundamental solutions to the problem of intermittent supply of renewable energy. The large-capacity/low-head pumped hydro energy storage (LL-PHES) system with the use of tubular pump turbine is a beneficial extension of traditional PHES systems owing to large flow rate and cheaper civil structures. However, the continuous competition between the “static water pressure difference caused by gravity” and the “pressure increase caused by accelerated impeller rotation” leads to prominent instability in the start-up process of the LL-PHES system under pump conditions. An explicit coupling algorithm is proposed for analyzing the transient characteristics in the start-up process of the LL-PHES system under pump conditions. This algorithm is based on the idea of dimensional transformation, and performs 3D flow calculations and 2D rigid body dynamics equation solution in the pump domain and the flap gate domain, respectively. This algorithm avoids the problems of high computational cost and poor convergence that exist in existing fully three-dimensional coupling algorithms and ensures the efficiency of transient hydraulic characteristic calculation. A comprehensive analysis of the transient characteristics of the LL-PHES system during pump start-up process is conducted using the proposed new algorithm. The entire process of the increase in rotational speed, valve opening, flow rate, and the continuous evolution of blade surface pressure during the start-up process is quantitatively described. The amplitude and spectral characteristics of the alternating pressure on multiple blades are clarified. The evolution law of blade load during the stage of severe pressure fluctuations during the start-up process is explained. The load distribution characteristics of “high in the leading and trailing edge areas and low in the middle” in the blade stream direction is presented. The research results have a direct guiding role in improving the hydraulic design and enhancing the operational stability of LL-PHES systems. Full article

To address the bottlenecks of conventional valve-controlled marine steering systems—characterized by high throttling losses, low efficiency, and high leakage risk—as well as the insufficient power density and impact resistance of electro-mechanical actuators (EMAs) for high-load steering of large vessels, this paper proposes and validates a high-performance integrated solution for an electro-hydrostatic actuator (EHA) for ship steering. First, a fifth-order electro–hydraulic–mechanical coupled dynamic model comprising a permanent magnet synchronous motor, hydraulic pump, hydraulic cylinder, and load is established. The validity and applicability boundaries of three simplifying assumptions—neglecting leakage, pipeline pressure losses, and steady-state fluid compressibility effects—are quantitatively analysed, with a total introduced error ≤3%. These assumptions are justified under medium-pressure, short-pipeline, and well-sealed conditions typical of marine EHA systems. Second, a composite control architecture combining outer-loop sliding mode control with inner-loop motor PID dual-loop control is proposed. Parameter tuning is performed using pole placement for the sliding surface and the Ziegler–Nichols critical ratio method for the inner loops, effectively suppressing hydraulic system parameter perturbations and random wave-induced load disturbances. Quantitative comparisons show that the proposed method reduces overshoot by 11.63% and improves sinusoidal tracking accuracy by 90.13% compared to conventional single-loop PID control. An integrated drive-control structure is designed, and a three-phase full-bridge inverter main circuit with wide-voltage input capability—including EMI filtering, soft-start, and LC filtering—is developed to accommodate the ±20% voltage fluctuations typical of ship power grids, thereby enhancing system integration and grid adaptability. Phased bench tests demonstrate that the settling time from no-load start-up to 200 r/min is only 0.01 s. When a sudden 20 N·m load is applied, the speed drop is less than 3%, and the recovery time is less than 0.025 s. The steady-state steering angle error does not exceed 0.12°, the maximum average steering rate reaches 3.33°/s, and the steering response time is within 0.3 s. All core performance indicators exceed the general technical standards for marine steering systems, with a 65.7% improvement in steady-state accuracy and a 62.5% improvement in response speed over conventional PID control. The research findings provide an effective general technical solution and experimental data support for the performance optimization and engineering application of marine EHA systems. Full article

Slow convergence remains a critical limitation hindering the practical deployment of consensus-based time synchronization protocols (CTSPs). Increasing algebraic connectivity is a key mechanism for improving the convergence speed of distributed algorithms. However, existing strategies inevitably introduce redundant packet communication, while forwarding stale timing information may degrade synchronization accuracy. To address this challenge, this paper proposes a high-order consensus time synchronization protocol (HCTSP). Unlike traditional CTSPs, HCTSP incorporates previous clock states from two-hop neighboring nodes to establish a virtual topology and further employs this information to enhance the estimation of logical clock parameters, thereby achieving fast estimation of clock parameters while effectively suppressing fluctuations in parameter estimation caused by a large initial synchronization error. Although the proposed method utilizes single-hop communication to relay information from non-adjacent nodes—an indirect transmission mechanism that inherently introduces additional communication overhead—we further develop an accumulator-based redundant information optimization scheme. Furthermore, the consensus algorithm integrates both an accumulator-based update mechanism and multi-hop historical memory, partially alleviating the impact of Gaussian communication delay jitter on clock correction. Theoretical proofs have verified the fast convergence of the proposed protocol. Extensive simulation experiments also demonstrate the superior efficiency of HCTSP in terms of convergence speed, communication overhead, and synchronization accuracy. Specifically, in random networks with 25 nodes, there is an approximately 50% reduction in single-round synchronization message length and a 66.67% decrease in total packet exchange volume compared to the virtual topology-based time synchronization protocol (VTSP). In the typical ring topology, where the convergence speed of the consensus algorithm is slow, HCTSP has a 59.33% increase in convergence speed compared to VTSP and a 75.29% increase compared to the gradient time synchronization protocol (GTSP). Full article

Interior permanent magnet synchronous motor (IPMSM) has advantages with high power density, wide speed range, small size, and high efficiency, and is widely used in the drive system of electric vehicles. Compared to other types of motors, permanent magnet synchronous motors (PMSMs) have some irreplaceable advantages, but there are also some disadvantages. As a type of PMSM, IPMSMs have problems with large fluctuations in permanent magnet (PM) magnetic field and demagnetization. At present, irreversible demagnetization of PMs is the most serious problem faced by IPMSMs. Once irreversible demagnetization of PMs occurs, it can cause a decrease in the performance of IPMSMs and can even damage the entire drive system. This paper takes an IPMSM with 48 slots, 8 poles, and 66 kW as the research object. Based on the reasons for PM demagnetization, a PM demagnetization model is established to obtain the demagnetization law of PMs. Firstly, the magnetic properties of PM materials were described based on their characteristic curves. The demagnetization mechanism of PMs was analyzed, and the demagnetization process of PMs was studied in combination with the reasons for demagnetization. Secondly, the basic parameters and torque performance of IPMSMs were calculated and analyzed. We analyzed the demagnetization curves of PM materials at different temperatures, calculated the operating points of PMs under various working conditions, and analyzed whether PMs undergo irreversible demagnetization based on the relationship between the operating points of PMs and the knee points of demagnetization curves. A high-fidelity electromagnetic–thermal coupling simulation model has been established, combined with the characteristics of electric vehicle driving conditions, to accurately characterize the temperature rise distribution and electromagnetic parameter changes of IPMSMs under different operating conditions and achieve multi-physics field collaborative analysis. Finally, a finite element model is adopted to simulate uniform and local demagnetization of PMs, and the changing characteristics of motor performance parameters under demagnetization are summarized. Different magnitudes of d-axis reverse current are applied as demagnetization excitation to analyze PM behaviors under various demagnetization degrees. The variations in magnetic flux density, output torque, and no-load back electromotive force (EMF) before and after demagnetization are simulated and analyzed. For the investigated motor and specific magnet grade, this work summarizes the irreversible demagnetization characteristics and corresponding practical judgment references. Full article

This study aims to determine the validity of the short-term scaling exponent of detrended fluctuation analysis (DFA-a1), respiration rate, and the ratio between respiration rate and the DFA-a1 (RRa1) in the estimation of the load associated with the first and second ventilatory thresholds (VT1, VT2) during running in a sample of adolescents. Twenty-two adolescents (11 males and 11 females) performed an incremental graded exercise test, monitored through the Polar H10 chest strap, synchronized with the Garmin Forerunner 965 equipped with the alphaHRV app. In the estimation of the load associated with the VT1, there was no significant HRV indices effect (F_(2,36) = 1.528; p = 0.231), nor a HRV indices and sex interaction effect (F_(2,36) = 0.319; p = 0.729). In the estimation of the VT2, there was a significant HRV indices effect (F_(2,36) = 20.3; p ≤ 0.001). Specifically, DFA-a1 displayed a moderate overestimation of the speed associated with the VT2 (0.41 [0.14 to 0.68] km/h); meanwhile, the respiration rate (−0.56 [−1.03 to −0.09] km/h) and RRa1 (−0.87 [−1.38 to −0.36] km/h) displayed a small and large underestimation, respectively. No significant HRV indices and sex interaction effect was observed (F_(2,36) = 1.626; p = 0.211). In conclusion, DFA-a1, respiration rate and RRa1 are valid HRV indices to estimate the load associated with the VT1. However, DFA-a1 displayed a moderate overestimation of the load associated with the VT2, while the respiration rate and RRa1 displayed a small and large underestimation, respectively. Full article

Ship transit in vertical ship lift chambers represents a highly confined flow regime characterized by extreme blockage (N < 2), where ship-induced piston effects can significantly influence navigational safety and structural loads. This study presents an experimental investigation of the unsteady hydrodynamic responses of a 1000 t class ship operating in the Baise vertical ship lift. A 1:10 large-scale physical model was constructed to reproduce the ship lift chamber and auxiliary lock geometry under Froude similarity. Tests were conducted for prototype water depths of 3.7–3.9 m and sailing velocities between 0.4 and 1.1 m/s. Ship sinkage, free-surface oscillations, and dynamic chamber weight variations were synchronously measured. Results revealed a profound process asymmetry: the exit maneuver induced significantly higher sinkage (0.92 m at 1.1 m/s) and chamber weight fluctuations (810 t) than the entry process due to restricted return flow replenishment. A non-dimensional predictive P–K relationship was derived with a regression coefficient α = 1.9121. Based on safety margins and mechanical load limits, critical speed thresholds were established at 0.6 m/s for exit and 0.7 m/s for entry to ensure a minimum safety clearance of 0.48 m even under docking error conditions. Full article

Accurate wind power forecasting is essential for ensuring power grid stability and facilitating the large-scale integration of renewable energy, yet it faces significant challenges due to the randomness, variability, and intermittency of wind resources and the increasing frequency of extreme weather events. Existing data-driven approaches often struggle to balance temporal continuity with meteorological sensitivity, leading to lag effects during rapid fluctuations, and frequently generate predictions that violate physical domain knowledge. To address these limitations, this paper proposes a dual-stream architecture to decouple temporal dependencies and spatial–meteorological mappings, utilizing a Physics-Informed GRU (PI-GRU) and an Enhanced Random Forest (ERF). Both streams are strictly bounded by physical constraints. Furthermore, a scenario-aware adaptive fusion mechanism is introduced to dynamically adjust the model’s reliance on each stream based on real-time wind speed gradients and volatility indices. Extensive experiments were conducted using a comprehensive dataset from three coastal wind farms over 8 months, encompassing stable regimes and extreme weather events. Evaluating across both 1-day and 4-day forecast horizons, the results demonstrate that our method significantly outperforms state-of-the-art baselines, proving its robustness and practical value for grid security and dispatch optimization. Full article

Reliable altitude and vertical speed estimation are fundamental for unmanned aerial vehicle (UAV) autonomous flight, especially during low-altitude operations such as takeoff and landing. Barometric altimeters are widely used due to their low cost, high availability, and good long-term stability, providing smooth altitude trends over a wide operating range. However, barometric measurements are indirectly inferred from static pressure and are therefore sensitive to local airflow disturbances. In particular, rotor downwash and ground effect-induced pressure perturbations near the surface can introduce significant biases and short-term fluctuations in barometric altitude, which propagate into erroneous vertical speed estimates during critical flight phases. Time-of-flight (TOF) altimeters, such as radar or laser sensors, provide direct above-ground-level (AGL) measurements and are largely insensitive to ground effect-related pressure disturbances. Within their limited operational range, TOF altimeters typically offer higher accuracy and lower short-term noise compared with barometric altitude. Nevertheless, TOF sensors are characterized by a restricted valid measurement range and frequently exhibit non-ideal behaviors in real-world UAV operations, including out-of-range outputs, frozen measurements, and in-range biased readings. These anomalies violate the nominal sensor assumptions used in conventional Kalman filter-based fusion and can significantly degrade estimation performance if not properly handled. This paper proposes a hybrid Kalman–rule-based altitude estimation framework that fuses barometric and TOF altitude measurements to exploit their complementary characteristics while mitigating their respective limitations. A vertical dynamic state-space model is formulated to jointly estimate altitude, vertical velocity, accelerometer bias, and ground height offset. A rule-based anomaly detection and classification module is developed to identify multiple TOF altimeter failure modes observed in operational UAV flights. The detected anomaly states are incorporated into the Kalman filter to adaptively weight, accept, or reject TOF measurements, thereby improving robustness against sensor non-idealities. The proposed approach is validated using 39 real UAV flight logs covering diverse flight regimes, including low-altitude maneuvers, cruise, and autonomous landing. Experimental results show that the proposed framework provides more stable and robust altitude and vertical speed estimation under practical sensor anomaly conditions compared with conventional barometer-only and standard Kalman fusion configurations. These results demonstrate the practical effectiveness of the proposed method for fault-aware altitude estimation in UAV autonomous flight. Full article

During the worldwide energy transition, wind power has become a leading development direction. Compared to large-scale horizontal-axis wind turbines (HAWTs), small-scale vertical-axis wind turbines (VAWTs) show potential, lack yaw mechanisms, adapt to wind direction changes, and are cost-effective. However, small-scale VAWTs operate in the near-surface atmospheric boundary layer and are sensitive to low-temperature and high-humidity climates, which cause blade icing. Ice buildup leads to fluctuations in aerodynamic loads, reduces power output, and diminishes stability. This study focuses on the NACA-0018 airfoil, using a low-temperature wind tunnel platform to simulate freezing durations to obtain ice characteristics on the blade surface. Based on ice profiles, numerical models were developed. Computational fluid dynamics (CFD) techniques were used to perform unsteady simulations of aerodynamic performance at various icing durations, investigating the influence on the power coefficient. The results indicate that the effect of icing duration on the average power coefficient depends on TSR. At the 5 min icing stage, the optimal tip-speed ratio decreases. Icing deteriorates aerodynamic performance at high tip-speed ratios, while producing positive optimization effects at low tip-speed ratios. This paper reveals the variation patterns of aerodynamic performance and differentiated mechanisms during the icing process of small vertical-axis wind turbine blades, providing a theoretical basis and data support for the development of surface anti-icing technologies and safe, efficient operation in low-temperature environments. Full article

With the large-scale integration of electric vehicles (EVs) into the power grid, uncoordinated charging behavior has aggravated load fluctuations in the power system. Deep reinforcement learning can optimize EV charging and discharging strategies through dynamic decision-making, thereby alleviating the operational pressure imposed on the grid by load variations. However, under large-scale EV integration scenarios, challenges still remain, including the excessively high dimensionality of the state space and the resulting decline in training efficiency. In addition, the coupling between existing clustering methods and dynamic scheduling mechanisms is still insufficiently tight. To address these issues, this study proposes a cluster-based deep reinforcement learning method for EV charging and discharging scheduling, referred to as CDRL. First, a probabilistic behavioral model is constructed based on EV charging transaction data to characterize the stochasticity of user charging behavior. A Density–Centroid Hybrid Clustering (DCHC) method is then adopted to cluster the charging behavior characteristics of EVs. Subsequently, at the cluster level, a day-ahead base load forecasting model is introduced, and the forecasting results are fed into a mixed-integer linear programming (MILP) model to generate the charging and discharging power allocation tasks for each cluster. At the individual level, the EV charging and discharging process is formulated as a Markov decision process (MDP), and a deep Q-network (DQN) is employed for policy learning, thereby achieving the decomposition of cluster-level tasks into individual scheduling decisions. The simulation results demonstrate that the proposed method can effectively reduce charging costs and smooth system load fluctuations while improving training convergence speed and policy stability. Full article

To address the current surge and speed fluctuation that occur when high-speed surface-mounted permanent magnet synchronous motors (HSPMSMs) switch from I-f open-loop control to sensorless closed-loop control, an adaptive switching method based on the cosine of the angle error is proposed. In this method, the angle error between the I-f open-loop reference angle and the angle estimated by the sensorless observer serves as the regulating variable, and its cosine is introduced to construct an adaptive attenuation factor, so that the rate of current reduction can vary continuously with the angle error. Specifically, a relatively large rate of current reduction is generated in the early stage of the switching process, when the angle error is large, to shorten the switching time. As the angle error decreases, the rate of current reduction is gradually lowered, allowing the current regulation process to better match the convergence process of the angle error and thereby improving switching stability. The proposed switching method is validated on a high-speed air compressor experimental platform. The experimental results show that the proposed method can shorten the switching time, reduce the current surge and speed fluctuation at switching, and exhibit good robustness under varying operating conditions. Full article

In petroleum drilling, conventional automatic drilling systems still rely heavily on manual intervention, which often leads to poor stability, limited multivariable coordination, and large fluctuations in drilling pressure. To address this problem, this study develops a hydraulic drawworks-based autodriller system with integrated power, drive, actuation, and control units, and establishes a mechanical-hydraulic-control co-simulation model for the coordinated regulation of drill-string hoisting speed and surface weight-on-bit (SWOB). Based on this platform, a dual-loop control framework is developed in which the inner loop uses linear active disturbance rejection control (LADRC) for rapid disturbance estimation and compensation, while the outer loop uses PID control for tracking regulation. Feedforward compensation is introduced to handle predictable load variation, and PSO-assisted fuzzy tuning is used to improve adaptability under varying operating conditions. Simulation results show that, compared with conventional cascaded PID control, the proposed controller reduces drawworks speed and SWOB overshoot by 12.5% and 40%, respectively, while the corresponding settling times are shortened by 1.805 s and 2.443 s. Prototype experiments on a scaled test platform further show that the proposed controller can be implemented on physical hardware and can maintain stable real-time regulation under laboratory conditions. These results support the feasibility of the proposed framework for coordinated hydraulic drawworks control under the simulated and laboratory-scale conditions considered in this study. Full article

Wheelchair users face significant mobility limitations related to both medical issues (e.g., musculoskeletal strain, pressure ulcers) and urban accessibility challenges. This pilot study introduces a sensor system integrating an inertial measurement unit (IMU), GPS (Global Positioning System), and a pressure-measuring seat to monitor distance travelled, speed, and posture in relation to real-world conditions. Seven participants navigated an approximately 800-metre outdoor course, divided into 13 sections, while real-time data were recorded. The results showed an average speed of 1.24 ± 0.41 m/s with peak speeds of up to 2.67 m/s. The centre of pressure on the seat fluctuated by an average of 25 mm in the x and y directions (left-right: COPx, back-forward: COPy). The data for average speed, COPx, and COPy showed significant differences between most of the 13 sections, with large, very large, and huge effect sizes. Comparing the speed, COPx, and COPy data with respect to distance travelled, and correlating them between the seven participants by applying the rank-sum method to the mean R² and calculating Kendall’s W, revealed that speed, COPx, and COPy were influenced by course conditions (R² medians between 0.013 and 0.499; W = 0.7857, strong agreement; χ²p = 0.0281). Small R² values indicate more individualised participant behaviour, while large R² values highlight the stronger influence of course conditions on the parameters. This non-invasive and cost-effective system provides objective motion data that can be used for future research in wheelchair design and rehabilitation strategies. Despite its advantages, this study was limited to able-bodied participants, so further clinical trials with individuals with mobility impairments are needed. Full article

Parallel, data-intensive applications are now commonly executed on infrastructures that combine Cloud, Fog, and Edge resources. In these environments, execution takes place on devices with markedly different computational power and over networks whose latency and bandwidth can fluctuate over time. Under these conditions, overall performance is influenced not only by processing speed but also by communication delays arising from data dependencies between tasks. This leads to a basic issue: whether scheduling strategies developed under computation-focused assumptions continue to perform well once communication costs are made explicit. This work examines the behavior of simple and widely adopted scheduling heuristics when network effects are modeled directly within the system. No new scheduling algorithms are introduced. Instead, the analysis focuses on how execution time and monetary cost change for deterministic parallel workloads deployed on hierarchical Cloud–Edge infrastructures exposed to stochastic latency and bandwidth variations. For this purpose, we introduce CLOWNSim, a lightweight discrete-event simulation framework that supports large-scale Monte Carlo experiments on fixed task graphs, allowing infrastructural and scheduling effects to be examined independently of workload variability. The experimental analysis covers fully centralized Cloud deployments, intermediate Fog configurations, and resource-constrained IoT scenarios. Scheduling policies based on computational speed, execution cost, or random device selection are evaluated across these settings. In Cloud and Fog environments, communication latency and data transfers represent a substantial portion of the overall makespan, weakening the impact of scheduling decisions driven primarily by computation. In IoT scenarios, limited processing capacity becomes the main limiting factor, while communication overhead remains present but less influential in comparison. The results indicate that performance trends across the Cloud–Edge continuum cannot be attributed to scheduler choice alone. Execution behavior arises from the combined effects of workload structure, placement decisions, and network properties, with different elements becoming dominant depending on the deployment context. The proposed simulation framework offers a practical way to study these interactions and to assess cost–performance trade-offs under communication conditions that reflect realistic operating environments. Full article

To address the strongly nonlinear hydrodynamic coupling and complex maneuvering challenges encountered by large ships during berthing operations in restricted waters, this paper proposes a high-precision autonomous berthing control system incorporating four-quadrant propeller hydrodynamics. Based on an improved Mathematical Maneuvering Group (MMG) framework, a three-degree-of-freedom (3-DOF) dynamic model is established to accurately capture the transient thrust and torque mappings of the propeller over all four quadrants. A dynamic line-of-sight (LOS) guidance system with a nonlinearly decaying acceptance radius is tightly coupled with PD/PI controllers to coordinate and regulate the rudder angle and propeller rotational speed. The numerical solver was rigorously validated against turning-test data for the S-175 container ship, with the errors of the key parameters all controlled within 15%. Subsequently, under the environmental conditions of Yangshan Port, full-condition path-planning and berthing simulations were conducted for the novel B-573 container ship under steady-current disturbances. These simulations evaluated multiple flow directions, namely due south, due north, due west, and due east defined in the Earth-fixed coordinate system, as well as multiple intensity levels ranging from 0 to 1.5 m/s that were specifically tested under the due north current. Quantitative evaluation shows that, under the highly challenging current condition of 1.0 m/s, the dynamic corrective mechanism effectively drives the global mean absolute error (MAE) to converge to 85.50 m, representing a 62% statistical reduction relative to the transient peak value. In addition, a parameter sensitivity analysis based on the cumulative cross-track error confirms that, when subject to variations in the underlying hydrodynamic parameters, the proposed system can suppress fluctuations in trajectory error to a very low level, thereby demonstrating a certain degree of control robustness. During the terminal berthing stage, the vessel smoothly completed an extreme deceleration from an initial speed of 6.4 m/s to a full stop within 588 s, while constraining the maximum astern rotational speed to −2 rps and seamlessly passing through all four propeller quadrants. The results confirm that the proposed autopilot framework possesses a certain degree of engineering feasibility in complex maritime environments. Full article