Search Results (395)

During load rejection in pumped-storage power stations, the rotational speed of the pump-turbine increases abruptly. The consequent structural deterioration of the internal flow induces high-amplitude hydraulic excitations, posing a serious threat to the operational stability of the unit. This study investigates a Francis pump-turbine to elucidate its flow evolution and instability mechanisms during load rejection. The fluid is modeled as weakly compressible water to capture finite pressure wave propagation. Dynamic mesh simulates guide vane closure, while vortex identification and short-time Fourier transform analyze transient pressure pulsations. The results indicate that the transient process can be sequentially divided into four typical stages—turbine mode, turbine-braking mode, reverse-pump mode, and return-to-turbine mode—to account for the most critical periods during the load rejection transient. The unit exhibits the poorest stability near the maximum rotational speed (443.34 r/min), where flow reversal and the full development of vortex structures significantly amplify fluctuations in hydraulic thrust. The vaneless space is identified as the primary source of pressure pulsations, whose characteristics are dominated by rotor–stator interaction mechanisms, and such disturbances decay rapidly in the downstream direction. Under turbine-braking and reverse-pump conditions, vortex rings, backflow, and asymmetric vortex structures generated within the spiral casing collectively contribute to the severe deterioration of the internal flow field quality. Full article

The present study explores the effect of incorrect tire mounting on vehicle braking performance with a specific focus on its interaction with the Anti-Lock Braking System (ABS). Experimental testing was conducted utilizing a passenger vehicle under controlled conditions according to ISO 21994. Two tire configurations were studied: a complying axle-wise configuration and a non-compliant asymmetric arrangement with the same tires mounted longitudinally on each side of the vehicle. ABS was switched on and off throughout the braking testing. The dynamics of the vehicle and its brake parameters were recorded and analyzed using nonlinear statistical modelling. The obtained coefficients of determination of the suggested nonlinear model were in the range of 0.8265 to 0.9938 and showed a satisfactory agreement with the experimental data. Chow-type statistical testing revealed significant differences across all tire designs and ABS operational modes tested (p < 0.001). Ljung–Box test indicated the lateral acceleration signals were significantly temporally dependent. The results indicate that the incorrect asymmetric tire mounting has a negative influence on the braking dynamics and this effect is more significant when the ABS is turned off. While ABS mitigates these effects to some degree, we still observe statistically significant disparities in performance. The results give empirically validated proof of the need for proper tire installation for the preservation of braking performance and vehicle safety and contribute to a better understanding of tire–road interaction and braking dynamics under asymmetric tire configurations. Full article

This study proposes an integrated self-aligning-torque energy recovery and DC-bus voltage stabilization strategy for a permanent-magnet synchronous motor (PMSM)-driven steer-by-wire system in new energy vehicles. During the front-wheel return-to-center process, self-aligning torque may provide excess mechanical energy to the steering actuator. Instead of dissipating this energy through a braking resistor, the proposed strategy converts part of the self-aligning-torque-induced mechanical energy into electrical energy and feeds it back to the low-voltage DC bus. To avoid ambiguity in the operating-mode description, this paper distinguishes the standard PMSM torque–speed quadrants from the mechanical stages of the steering process. Regenerative operation is defined according to the condition ($T_e{\omega }_m<0$), corresponding to the second or fourth quadrant of the PMSM torque–speed plane, whereas the return-to-center regenerative stage refers to the self-aligning-torque-dominated stage of the steer-by-wire motion. Based on this definition, an electromechanical energy-flow model is established to describe the transfer path from self-aligning torque to the PMSM and then to the DC bus. Considering that regenerative energy injection may cause DC-bus voltage fluctuation or braking-resistor activation, a single-loop bus-voltage stabilization method based on active disturbance rejection control is developed. A third-order linear extended state observer is adopted to estimate the lumped disturbance caused by self-aligning-torque variation, current coupling, load variation, parameter uncertainty, and inverter loss. The observer bandwidth, controller gains, current limitation, and overvoltage protection mechanisms are further discussed to improve the practical implementability of the proposed control strategy. In addition, an energy-accounting method is introduced to distinguish total steering energy consumption, available self-aligning-torque mechanical energy, gross recovered electrical energy, system losses, net recovered energy, and recovery efficiency. Simulation and experimental results show that the proposed strategy can suppress DC-bus voltage rise, reduce braking-resistor energy dissipation, and achieve measurable steering-actuator-level energy recovery during repeated return-to-center maneuvers. The results verify the feasibility of using self-aligning-torque-induced regenerative energy in PMSM-driven steer-by-wire systems, while the actual vehicle-level energy benefit depends on the driving cycle, low-voltage load demand, battery charging acceptance, and converter efficiency. Full article

Road traffic accidents continue to be a major global cause of fatalities, disproportionately affecting pedestrians and other vulnerable road users. While deep reinforcement learning has proven effective in handling complex navigation tasks, providing formal stability and safety guarantees during both training and deployment remains a significant challenge. This paper introduces a dual-layer safety-aware framework for pedestrian avoidance in occluded urban driving. During training, a first-order Control Lyapunov–Barrier Function is integrated with Proximal Policy Optimization to promote goal-reaching stability and obstacle avoidance: the analytic Lie derivatives of the Lyapunov and barrier functions are embedded as a modifier in the advantage estimate, providing explicit stability and safety signals that accelerate convergence toward safe, goal-reaching behavior without disrupting the standard policy update. At deployment, a higher-order Control Lyapunov–Barrier Function, realized through a quadratic programming safety filter, acts as a safety shield that projects the nominal acceleration onto the intersection of the second-order Lyapunov and barrier feasibility sets; the barrier function is further extended with relative velocity terms to account for dynamic pedestrian motion. Experiments with a four-wheeled vehicle in the Webots simulator show that the framework reliably reaches the goal, avoids an occluded pedestrian across a range of crossing speeds, and improves task success rates and safety-constraint adherence relative to Proximal Policy Optimization and a conventional higher-order safety filter baseline, particularly during emergency braking maneuvers. Full article

This study presented a comprehensive optimization of a piezoelectric injector specifically designed for pure methanol compression-ignition engines. As a fuel for compression-ignition engines, methanol exhibits broad application prospects. To overcome the challenges posed by methanol’s low cetane number and energy density, a co-optimization strategy was implemented, targeting the actuator, drive waveform, and internal flow geometry. The redesigned injector exhibited superior dynamic performance, featuring significantly faster response times and enhanced operational stability, which were critical for precise fuel delivery control. Furthermore, the optimized internal flow path increased the effective flow rate, ensuring sufficient fuel supply across all engine operating conditions. The upgraded injector was rigorously tested on an engine bench, demonstrating substantial performance gains. Brake thermal efficiency improved from 38.9% to 40.4% at low load and from 43.68% to 46.07% at high load. Emissions of CO, formaldehyde, acetaldehyde, and unburned methanol were consistently reduced, with the maximum reduction reaching 23.1%, confirming markedly enhanced combustion completeness. This improvement was directly attributed to the injector’s refined spray characteristics and precise control, although it led to a slight increase in NOx emissions due to higher peak combustion temperatures. Full article

The capability of recovering a portion of braking energy during vehicle deceleration is one of the distinctive advantages of new energy vehicles (EVs) over Conventional Internal Combustion Engine Vehicles (ICEVs). In existing production vehicles, regenerative braking control is commonly implemented using rule-based lookup table methods. Although such approaches are simple, reliable, and easy to implement, they lack the ability to adaptively adjust the braking force allocation according to varying driving conditions, thereby limiting the potential for high efficiency energy recovery. To improve regenerative energy recovery while simultaneously maintaining braking stability, this study introduces an ANFIS-optimized Sliding Mode–Fuzzy Joint Hierarchical Control Strategy (S-FJHCS) for regenerative braking systems. In the upper control layer, an improved tire road friction coefficient estimation algorithm is integrated with a sliding mode controller to ensure consistent slip ratio regulation between the front and rear wheels. In the lower control layer, a fuzzy control algorithm is employed to coordinate the distribution of braking torque between the hydraulic braking system and the hub motors. Furthermore, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized to perform offline optimization of the fuzzy controller, enabling the adaptive adjustment of fuzzy rules and membership functions based on historical operating conditions. Simulation and experimental results demonstrate that the proposed regenerative braking control strategy can improve regenerative energy recovery efficiency by approximately 5–10% compared with a conventional rule based regenerative braking strategy, while maintaining satisfactory braking performance and vehicle stability under various driving conditions. Full article

Corner-module electric vehicles equipped with four-wheel independent drive, four-wheel independent steering, and electromechanical braking (EMB) actuators provide a flexible platform for software-defined chassis control, but braking-in-turn maneuvers impose severe longitudinal–lateral coupling and competition for tire adhesion resources. This paper proposes an actuator-oriented hierarchical coordinated control strategy for EMB-based corner-module vehicles. At the upper level, a Model Predictive Controller optimizes lateral tire force allocation under a tire-friction-ellipse hard constraint and coordinates the four-wheel steering response. At the lower level, a three-intensity adaptive braking-force distribution algorithm converts the vehicle-level demand into wheel-level EMB clamping-force commands while considering braking intensity, steering intensity, load transfer, and yaw stability. To improve actuator tracking accuracy, the EMB subsystem combines nonlinear actuator modeling, offline parameter identification, online recursive-least-squares correction, and force–speed–position cascade control. MATLAB (R2025b)/Simulink-CarSim co-simulation and EMB hardware-in-the-loop (HIL) tests verify the proposed strategy under fixed-angle emergency braking and lane-change braking conditions with high, low, and variable-adhesion roads. The results show improved trajectory tracking and yaw stability, reduced braking-torque fluctuation, and faster EMB clamping-force response, demonstrating the suitability of the proposed actuator-level coordination method for intelligent electric chassis applications. Full article

Lane-changing and overtaking constitute a typical complex driving manoeuvre for intelligent vehicles operating on structured roads; this task demands that the vehicle not only plan a safe and smooth lane-change trajectory but also requires the control system to maintain high tracking accuracy and lateral stability. Addressing the challenges of real-time path planning and stable tracking control inherent in lane-changing and overtaking scenarios, this paper proposes a trajectory planning and control method that integrates an improved artificial potential field (APF) approach with a lateral–longitudinal cooperative controller. Regarding path planning, the proposed method constructs attractive and repulsive fields based on the APF framework, while introducing virtual target points, elliptical obstacle models, and velocity-dependent repulsive fields to mitigate the risk of local minima and enhance dynamic obstacle avoidance capabilities. To ensure trajectory continuity and trackability, a fifth-order polynomial is employed to smooth the planned path. Regarding control, the method utilises a Linear Quadratic Regulator (LQR)—optimised via a genetic algorithm—for lateral control; this is coupled with a dual-PID longitudinal controller that generates throttle and braking commands based on vehicle speed errors, thereby establishing a cooperative lateral–longitudinal tracking control strategy. The proposed method is validated using a CarSim–MATLAB/Simulink co-simulation platform. Simulation results demonstrate that the proposed method significantly improves trajectory-tracking accuracy and vehicle stability during lane-changing and overtaking manoeuvres. In a single lane-change scenario, the maximum lateral error is reduced from approximately 0.62 m to 0.22 m, and the heading angle error decreases from about 0.058 rad to 0.01 rad; in a continuous lane-changing scenario, the maximum lateral error drops from approximately 0.30 m to 0.04 m, while the heading angle error falls from about 0.016 rad to 0.005 rad. Furthermore, the yaw rate, sideslip angle, and lateral acceleration are reduced by 39.1%, 22.2%, and 28.9%, respectively. These results confirm that, under the specified simulation conditions, the proposed method exhibits superior tracking performance and stability. Future research could further explore more complex driving scenarios, such as curved roads, multi-vehicle interactions, sensor uncertainties, actuator delays, and real-vehicle field experiments. Full article

The performance of the aircraft anti-skid braking system is critical to the ground operational safety of an aircraft. Conventional Pressure Bias Modulation (PBM) can suffer from deep skidding under low runway friction coefficients or low aircraft speeds. To address these issues, a composite control strategy based on Gaussian Quantum Particle Swarm Optimization (GQPSO) is proposed. This strategy employs the GQPSO algorithm for offline Proportional–Integral–Derivative (PID) parameter optimization, followed by real-time adaptive scheduling through a lookup table to accommodate varying speed domains and runway conditions. Simultaneously, by integrating the main-wheel dynamics model and friction characteristics, a runway identification function based on a Back Propagation Neural Network (BPNN) is designed to provide runway status information. The stability of the controller is verified via phase-plane analysis and Monte Carlo simulation. Subsequently, comparative Hardware-in-the-Loop (HIL) tests are conducted among PBM, PSO-PID, and the proposed GQPSO-PID controller under various runway conditions. The experimental results demonstrate that this composite controller can adapt to different speed domains and runway conditions, stably track the target slip ratio, effectively suppress skidding, and significantly improve braking efficiency, as well as exhibiting excellent robustness and control performance. Full article

Background: The Forward Lunge is a representative squash-specific footwork movement involving rapid acceleration, braking, postural stabilization, and return propulsion. This study examined whether pre-competition neuromuscular electrical stimulation (NMES) combined with weighted squats was associated with differences in Forward Lunge performance and neuromuscular control in squash athletes. Methods: Thirty-six male squash athletes were randomly assigned to three groups: Weighted Squats, Fake Stimulation, and Real Stimulation, with 12 participants in each group. After the assigned acute intervention, all participants completed the squash-specific star test. Completion time was recorded using a Microgate Witty photocell timing system, while surface electromyographic (sEMG) signals from 14 right-side muscles were collected using a Delsys Trigno wireless electromyography system. High-speed video was used to identify the Forward Lunge movement cycle, and transistor–transistor logic (TTL) synchronization enabled temporal alignment among timing, video, and sEMG signals. Normalized root mean square (RMS), muscle co-activation index (CI), and non-negative matrix factorization (NMF)-based muscle synergy parameters were calculated. Between-group differences were analyzed using one-way analysis of variance (ANOVA) with Bonferroni post hoc comparisons, and false discovery rate (FDR) correction was applied to secondary neuromuscular outcomes. Results: Star test completion time differed significantly among the three groups (F = 28.65, p < 0.001, η² = 0.63). The Real Stimulation group showed a shorter completion time (10.35 ± 0.45 s) than the Weighted Squats group (11.80 ± 0.55 s) and Fake Stimulation group (11.55 ± 0.50 s). During the Forward Lunge movement cycle, normalized RMS values of the rectus abdominis (ABS; F = 18.56, p < 0.001, η² = 0.55) and latissimus dorsi (LD; F = 13.42, p < 0.001, η² = 0.44) were significantly higher in the Real Stimulation group. The gluteus maximus–biceps femoris (GLM–BF) co-activation index also differed significantly among groups (F = 58.42, p < 0.001, η² = 0.78), with higher values in the Real Stimulation group. Muscle synergy analysis showed group differences in selected muscle activation weights and temporal activation parameters. Conclusions: In this parallel-group acute intervention study based on post-intervention measurements, real NMES combined with weighted squats was associated with shorter star test completion time and altered neuromuscular control during the Forward Lunge movement cycle. The integrated use of photocell timing, wireless sEMG, high-speed video, and TTL synchronization provided temporally aligned sensor-based evidence for evaluating acute pre-competition activation strategies. However, due to the absence of baseline measurements, the findings should be interpreted as post-intervention between-group differences rather than definitive evidence of individual improvement. Full article

To address the strong coupling between trolley motion and payload swing, as well as the difficulty of determining optimal braking timing during emergency operations of overhead cranes in complex environments, a model-predictive braking control method integrated with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is proposed. Within the Model Predictive Control (MPC) framework, payload swing angle constraints are explicitly incorporated, and an adaptive braking reference trajectory is constructed to achieve rapid and stable stopping while effectively suppressing load oscillations. Furthermore, the TD3 algorithm is employed for online adaptive optimization of key MPC parameters, enabling a dynamic trade-off between braking performance and swing suppression under varying operating conditions. In addition, a minimum braking distance prediction model based on Support Vector Regression (SVR) is developed, and a state-dependent safety-critical region prediction model is established to quantitatively determine optimal braking timing. Simulation results across multiple operating conditions demonstrate that the proposed TD3–MPC method outperforms conventional MPC in terms of braking efficiency, swing suppression capability, and system stability while satisfying swing angle constraints. Moreover, real-crane experimental results demonstrate the effectiveness of the proposed safety-critical region prediction method in determining appropriate braking trigger timing and achieving safe and smooth stopping of the overhead crane under obstacle-avoidance conditions. Full article

The brake–release stability of electro-hydraulic thrusters (EHTs) significantly affects the safety of hydraulic braking systems, especially under low-temperature conditions with varying fluid viscosity. Most existing studies have focused on macroscopic braking characteristics, while the internal flow field variation and vortex evolution mechanism during the brake–release process remain insufficiently explored. In this work, transient CFD simulations are conducted to investigate vortex formation rules and flow field characteristics inside an EHT. Three typical vortex structures denoted as α, β, and γ are identified, and the independent and coupling influences of fluid dynamic viscosity and motor speed on vortex intensity and piston-bottom pressure are quantitatively analyzed. The results show that vortices α and β trigger flow disorder and additional hydraulic energy loss, while vortex γ optimizes flow uniformity and assists piston extension. Higher fluid viscosity exacerbates vortex development and pressure fluctuation, while increasing motor speed accelerates transient flow field evolution. This study clarifies the internal flow mechanism of EHT brake–release behavior and provides reliable parametric guidance for optimizing the low-temperature performance of electro-hydraulic braking systems. Full article

Adaptive brake force distribution (BFD) is critical for the safety and efficiency of electric commercial vehicles (ECVs), yet the integration of thermal constraints, real-time control, and stability requirements remains fragmented. Following PRISMA 2020, a systematic search in IEEE Xplore and Web of Science (2016–2026) was performed, yielding 156 core studies classified into five thematic groups: fundamentals, MPC-based control, PSO, integrated frameworks, and validation methods. The analysis reveals a tripartite fragmentation: 27 MPC studies neglect thermal constraints; 22 PSO studies lack real-time adaptability; 52 studies target commercial vehicles, but none validate front–rear redistribution under concurrent thermal and stability constraints. Only two studies address thermal-aware distribution, and seven consider pneumatic delay compensation. Publication volume accelerates, indicating rising interest. The literature treats thermal management, real-time redistribution, and stability-constrained control as separate problems, leaving a critical gap: online correction of the ideal distribution curve under coupled thermal-stability constraints for ECVs is absent. To fill this gap, a hierarchical MPC-PSO framework is proposed, where PSO (1–5 Hz) adaptively tunes MPC weights based on vehicle state, and MPC (20–50 Hz) executes optimal control integrating regenerative, magnetic, and eddy current braking. This research aims to achieve two main objectives: to propose a new integrated framework that addresses the overlooked interrelationship between the stability constraints of ECVs and thermal constraints and to systematically reveal the state of integrative constraints on adaptive brake force distribution in electric commercial vehicles, with a focus on real-time control and stability requirements. Full article

This study reports the first synthesis and full spectroscopic characterization (FT-IR, ¹H NMR, ¹³C NMR) of a novel benzoylthiourea-based compound 2-chloro-N-((2-hydroxy-4-nitrophenyl)carbamothioyl)benzamide (HNCB) and evaluates its behavior as a combustion-modifying additive in diesel–ethanol blends. Blends containing 50, 100, and 200 ppm HNCB were tested in a single-cylinder direct-injection compression ignition engine at five torque levels (0–24 Nm) and four Exhaust gas recirculation rates (0–30%) to assess combustion, performance, and emissions. Ethanol improved mixture formation and combustion stability, while HNCB, particularly at 100 ppm, provided the most favorable overall balance of combustion phasing, heat-release characteristics, and emission control. At 24 Nm and 0% exhaust gas recirculation, Diesel + Ethanol + HNCB (100 ppm) increased maximum cylinder pressure by 4.1% relative to diesel and reduced cyclic indicated mean effective pressure variability. The 50 ppm blend yielded the lowest specific fuel consumption, with reductions of up to 37% at partial loads and the highest brake thermal efficiency values under several exhaust gas recirculation conditions. Nitrogen oxides emissions decreased by up to 65–75%, whereas the 200 ppm blend increased hydrocarbon and soot at 30% exhaust gas recirculation. Overall, HNCB acted as an effective combustion modifier under the tested conditions. Full article

Aggressive riding behavior is a key contributing factor to road accidents, particularly in motorcycling, where rider dynamics directly influence vehicle stability and control. Despite growing interest in objective behavioral assessment, validated classification frameworks specific to motorcycles remain scarce in the literature. This pilot study investigated the feasibility of a standard deviation-based method for classifying aggressive riding behavior in a single experienced motorcyclist navigating two distinct environments: an urban route (UR) and a suburban national route (SNR). The participant completed two 20 min rides under real-world conditions. The UR was characterized by frequent accelerations, braking, speed bumps, and traffic lights, whereas the SNR features low traffic density and minimal interruptions. Longitudinal acceleration data were continuously recorded using a Vicon Blue Trident measurement unit mounted on the motorcycle seat. Drawing on the threshold principles established in automotive research, an environment-specific classification framework was developed to categorize riding events into normal, aggressive, and dangerous levels for both acceleration and deceleration maneuvers. The derived thresholds revealed pronounced environmental differences: UR thresholds (acceleration: 2.122 m/s²; deceleration: −2.134 m/s²) were approximately three times lower than those observed in the SNR (acceleration: 6.16 m/s²; deceleration: −7.09 m/s²). From more than four million recorded data points, approximately 88% of the riding behavior was classified as normal in both routes. In the UR, 9.27% of events were identified as aggressive and 4.37% as dangerous, compared with 7.27% aggressive and 5.35% dangerous events in the SNR. These preliminary findings suggest that environment-specific thresholds may be essential for accurately characterizing motorcycle riding behavior, and caution against the direct application of fixed automotive criteria to motorcycle safety analyses. All findings are specific to one rider on two routes and must not be extrapolated to other motorcyclists, vehicle types, or road contexts without replication. Full article