Search Results (122)

The line-start permanent-magnet (LSPM) motor combines the direct-on-line starting of induction motors with the high efficiency of permanent-magnet (PM) synchronous motors, but conventional interior PM designs are difficult to manufacture and surface PM (SPM) designs often suffer from limited starting torque and reduced efficiency. This paper investigates consequent-pole SPM designs, in which the number of magnets is reduced by half while maintaining equal magnet volume, enabling simple rotor construction and improved starting performance. A prototype is manufactured and tested, confirming smooth synchronization under load. Efficiency is constrained by the non-sinusoidal flux distribution of the consequent-pole structure. Rotor design strategies enlarging the air gap near the iron poles are analyzed, and a finite element method analysis shows improved torque and efficiency without loss of starting capability. Full article

For the combination of finite elements and control circuits, the calculation is complex and time-consuming, making direct optimization impractical. In this paper, a new distributed node and magnetic circuit model is proposed to simulate the spatial and temporal variation of the distributed air-gap magnetic density with the current and rotor angle and solve the electromagnetic force wave variation. Compared to other distributed flux-linkage models, the proposed model not only considers the radial magnetic path but also connects adjacent magnetic paths tangentially. The inclusion of this tangential path enhances the mutual interaction between magnetic circuits, leading to a more accurate model. Based on the control circuit model, the electromagnetic force wave changes caused by the harmonic currents under various circuits and operating conditions are calculated, the topology is analyzed and optimized to mitigate critical harmonics, the electromagnetic force wave is reduced, and finally, the model accuracy is verified experimentally. While most distributed flux-linkage models are applied to the optimization of motor performance metrics such as the magnetomotive force (MMF), power, and torque, this paper applies the model to the optimization of the magnetic field strength, the harmonic content, and the corresponding noise, vibration, and harshness (NVH), demonstrating a broader range of applications. This method can be coupled with the control circuit to analyze the changes in electromagnetic force waves and quickly optimize them, improving the accuracy and efficiency of research and development. Full article

To resolve the inherent coupling conflict between trajectory tracking and lateral stability in distributed electric drive buses, this paper proposes a hierarchical cooperative control framework. A simplified two-degree-of-freedom (2-DOF) vehicle model is first established, and kinematically derived reference states for stable motion are computed. At the upper level, a model predictive controller (MPC) generates real-time steering commands while explicitly minimizing lateral tracking error. At the lower level, a proportional integral derivative (PID)-based roll moment controller and a linear quadratic regulator (LQR)-based direct yaw moment controller are designed, with four-wheel torque distribution achieved via quadratic programming subject to friction circle and vertical load constraints. Co-simulation results using TruckSim and MATLAB/Simulink demonstrate that, during high-speed single-lane-change maneuvers, peak lateral error is reduced by 11.59–18.09%, and root-mean-square (RMS) error by 8.67–14.77%. Under medium-speed double-lane-change conditions, corresponding reductions of 3.85–12.16% and 4.48–11.33% are achieved, respectively. These results fully validate the effectiveness of the proposed strategy. Compared with the existing MPC–direct yaw moment control (DYC) decoupled control framework, the coordinated control strategy proposed in this paper achieves the optimal trade-off between trajectory tracking and lateral stability while maintaining the quadratic programming solution delay below 0.5 milliseconds. Full article

To improve the adaptability of 4WID electric vehicles under various operating conditions, this study introduces a model predictive control approach utilizing a neural network for adaptive weight parameter prediction, which integrates four-wheel steering and longitudinal driving force control. To address the difficulty in adjusting the MPC weight parameters, the neural network undergoes offline training, and the Snake Optimization method is used to iteratively optimize the controller parameters under diverse driving conditions. To further enhance vehicle stability, the real-time stability state of the vehicle is assessed using the $\beta -\dot{\beta }$ phase plane method. The influence of vehicle speed and road adhesion on the instability boundary of the phase plane is comprehensively considered to design a stability controller based on different instability degree zones. This includes an integral sliding mode controller that accounts for both vehicle tracking capability and stability, as well as a PID controller, which calculates the additional yaw moment based on the degree of instability. Finally, an optimal distribution control algorithm coordinates the longitudinal driving torque and direct yaw moment while also considering the vehicle’s understeering characteristics in determining the torque distribution for each wheel. The simulation results show that under various operating conditions, the proposed control strategy achieves smaller tracking errors and more concentrated phase trajectories compared to traditional controllers, thereby improving path tracking precision, vehicle stability, and adaptability to varying conditions. Full article

Hydrogen injection timing (HIT) plays a crucial role in the combustion and emission characteristics of a hydrogen/n-butanol dual-fuel engine with hydrogen direct injection. This study employed an integrated approach combining three-dimensional simulation modeling and engine test bench experiments to investigate the effects of HIT on engine performance. In order to have a more intuitive understanding of the physical and chemical change processes, such as the stratification state and combustion status of hydrogen in the cylinder, and to essentially explore the internal mechanism and fundamental reasons for the improvement in performance of n-butanol engines by hydrogen addition, a numerical study was conducted to examine the effects of HIT on hydrogen stratification and combustion behavior. The simulation results demonstrated that within the investigated range, at 100 °CA BTDC hydrogen injection time, hydrogen forms an ideal hydrogen stratification state in the cylinder; that is, a locally enriched hydrogen zone near the spark plug, while there is a certain distribution of hydrogen in the cylinder. Meanwhile, the combustion state also reaches the optimal level at this hydrogen injection moment. Consequently, 100 °CA BTDC is identified as the optimal HIT for a hydrogen/n-butanol dual-fuel engine. At the same time, an experimental study was performed to capture the actual complex processes and comprehensively evaluate combustion and emission characteristics. The experimental results indicate that both dynamic performance (torque) and combustion characteristics (cylinder pressure, flame development period, etc.) achieve optimal values at the HIT of 100 °CA BTDC. Notably, under lean-burn conditions, the combustion parameters exhibit greater sensitivity to HIT. Regarding emissions, the CO and HC emissions initially decreased slightly, then gradually increased with advanced injection timing. The 100 °CA BTDC timing effectively reduced the CO emissions at λ = 0.9 and 1.0. CO emissions at λ = 1.2, and showed minimal sensitivity to the injection timing variations. Therefore, optimized HIT facilitates enhanced combustion efficiency and emission performance in hydrogen-direct-injection n-butanol engines. Full article

To address the poor trajectory tracking of mining trucks in narrow, high-curvature paths, this study explores the impact of four-wheel steering (4WS) and direct yaw moment control (DYC) on vehicle stability. A validated two-degree-of-freedom 4WS vehicle model was developed. A fuzzy logic controller with dual inputs (yaw rate and yaw angular acceleration) and a single output (compensatory yaw moment) was designed, alongside an optimal torque distribution controller based on tire friction circle theory to allocate the resultant yaw moment. A co-simulation platform integrating TruckSim and MATLAB/Simulink was established, and experiments were conducted under steady-state and double-lane-change conditions. Comparative analysis with traditional front-wheel steering and alternative control methods reveals that the 4WS mining truck with fuzzy-controlled optimal torque distribution achieves a reduced turning radius, enhancing maneuverability and stability. Hardware-in-the-loop (HIL) testing further validates the controller’s effectiveness in real-time applications. Full article

Tire contact imprint characteristics and pressure distribution directly affect their lateral mechanical characteristics under rolling conditions, which are the key influencing factors for vehicle handling stability. Based on the nonlinear finite element method, an explicit dynamic model of radial tires is established using Abaqus, and its contact process is simulated through phased load transfer and kinematic inversion. The modified mathematical model of contact pressure distribution is introduced from the geometric evolution law of contact imprint and the nonlinear characteristics of contact pressure distribution. The corrected lateral force and aligning torque and contact imprint behavior are analyzed. The results show that in the low roll-angle range, with the increase in the roll angle, the contact imprint shrinks asymmetrically, the pressure center shifts to the outer shoulder of the roll direction, and the lateral force and aligning torque show linear growth characteristics. At the critical value ±8°, the growth rate is significantly slowed down due to the stress saturation effect of the shoulder area. The research analyzes the evolution mechanism of the lateral mechanical characteristics of the contact imprint geometry and pressure distribution drive tires under roll conditions, providing theoretical support for vehicle handling stability optimization and tire structure design. Full article

In this study, aluminium matrix hybrid composites reinforced with boron carbide (B₄C) and silicon carbide (SiC) were fabricated using the direct hot-pressing technique under 35 MPa pressure at 600 °C for 5 min. Particle size distribution and scanning electron microscope analysis were conducted for the input powders. The microstructure, mechanical properties, and drillability of the fabricated composites were examined. As the SiC content increased, the density decreased, hardness improved, and transverse rupture strength declined. Drilling experiments were performed based on the Taguchi L₁₈ orthogonal array. The control factors included cutting speed (25 and 50 m/min), feed rate (0.08, 0.16, and 0.24 mm/rev), point angle (100°, 118°, and 136°), and SiC content (0%, 5%, and 10%). Quality characteristics such as thrust force, torque, surface quality indicators, diameter deviation, and circularity deviation were evaluated. The Taguchi method was applied for single-response optimization, while the Entropy-weighted, Taguchi-based CoCoSo method was used for multi-response optimization. Analysis of Variance was conducted to assess factor significance, and regression analysis was used to model relationships between inputs and responses, yielding high R² values. The optimal drilling performance was achieved at 50 m/min, 0.08 mm/rev, 136°, and 10% SiC, and the confirmation tests verified these results within the 95% confidence interval. Full article

As the world increasingly gravitates towards green, environmentally friendly and low-carbon lifestyles, wind power has become one of the most technologically established renewable energy sources. However, with the continuous increase in their output power and height, wind turbine towers are subjected to higher-intensity alternating wind loads. This makes critical components more prone to fatigue failure, potentially leading to major accidents such as tower buckling or turbine collapse. High-strength bolts play a vital role in supporting towers but are susceptible to fatigue crack initiation under long-term cyclic loading, necessitating regular inspection. Types of wind turbine bolts mainly include high-strength bolts, stainless steel bolts, anchor bolts, titanium alloy bolts, and adjustable bolts. These bolts are distributed across different parts of the turbine and perform distinct functions. Among them, high-strength bolts in the tower are particularly critical for structural support, demanding prioritized periodic inspection. Compared to destructive offline inspection methods requiring bolt disassembly, non-destructive testing (NDT) has emerged as a trend in defect detection technologies. Therefore, this review comprehensively examines various types of NDT techniques for wind turbine towers’ high-strength bolts, including disassembly inspection techniques (magnetic particle inspection, penetration inspection, intelligent torque inspection, etc.) and non-disassembly inspection techniques (ultrasonic inspection, radiographic inspection, infrared thermographic inspection, etc.). For each technique, we analyze the fundamental principles, technical characteristics, and limitations, while emphasizing the interconnections between the methodologies. Finally, we discuss potential future research directions for bolt defect NDT technologies. Full article

Hydraulic motors are increasingly pivotal in high-power drive systems for heavy-duty vehicles and industrial machinery due to their high power density. Radial piston hydraulic motors are commonly employed in heavy-load applications, while digital hydraulic motors have surfaced as a potential substitute for traditional hydraulic motors. Yet challenges such as torque pulsation and inefficient flow distribution persist in traditional designs. To improve performance and reliability, this paper proposed a digital radial piston hydraulic motor using several switching valves to distribute hydraulic oil, along with a comprehensive strategy to mitigate flow pulsation and enhance hydraulic transmission efficiency in digital hydraulic motors. The inherent torque pulsation characteristics are systematically investigated, revealing their dependence on valve actuation patterns and load dynamics. A novel torque pulsation mitigation design is introduced. Then, valve modeling and efficiency evaluation are developed; the phase-correction-based flow distribution method is conducted by optimizing valve sequencing; and simulations and experiments are carried out to demonstrate the feasibility. In conclusion, insights have been drawn to direct the design and control of radial piston digital hydraulic motors. This paper presents a potential solution for heavy-duty traction applications. Full article

Micro-scale lubrication flow of a gas between eccentric circular cylinders with an arbitrary temperature difference is studied on the basis of the Navier–Stokes set of equations and the velocity slip and temperature jump boundary conditions. The dimensionless curvature, which is defined as the mean clearance divided by the radius of the inner cylinder, is small, the Knudsen number and the Reynolds number based on the mean clearance are small, and the temperature ratio is arbitrary. The Reynolds-type lubrication equation is derived analytically. For a verification of the equation, an assessment is conducted against the solution of the direct numerical analysis of the Bhatnagar–Gross–Krook–Welander (BGKW) model of the Boltzmann equation in the author’s previous work [Doi, T. Phys. Fluids 2024, 36, 042016]. The solution of the lubrication equation agrees with that of the Boltzmann equation satisfactorily well over the slip flow regime, not only in the eccentric force and the torque but also in the local distribution of the temperature, flow velocity, and the normal stress. A superiority of the lubrication equation over the lubrication model proposed in the author’s previous work is also discussed. Full article

In practical engineering, buildings are predominantly subjected to combined forces, and reinforced concrete (RC) beams serve as the primary load-bearing components of buildings. However, there is a paucity of research on the torsional effects of RC beams, particularly concerning the torsional failure mechanisms of large-size beams. To address this gap, this paper establishes a meso-scale numerical analysis model for RC beams reinforced with Carbon Fiber Reinforced Polymer (CFRP) sheets under combined bending and torsion pressures. The research analyzes how the fiber ratio and torsion-bending ratio govern torsion-induced failure characteristics and size effects in CFRP-strengthened RC beams. The results indicate that an increase in the fiber ratio leads to accumulated damage distribution in the RC beam, a gradual decrease in CFRP sheet strain, and an increase in peak load and peak torque, albeit with diminishing amplitudes; as the torsion-bending ratio increases, crack distribution becomes more concentrated, the angle between cracks and the horizontal direction decreases, overall peak load decreases, peak torque increases, and CFRP sheet strain increases; and the nominal torsional capacity of CFRP-strengthened RC beams declines with increasing size, exhibiting a reduction of 24.1% to 35.6%, which distinctly demonstrates the torsional size effect under bending–torsion coupling conditions. A modified Torque Size Effect Law is formulated, characterizing in quantitative terms the dependence of the fiber ratio and the torsion-bending ratio. Full article

To improve the prediction accuracy of soot load in gasoline particulate filters (GPFs) and the control accuracy during GPF regeneration, this study developed a prediction model to predict the soot mass concentration at the GPF inlet of gasoline direct injection (GDI) engines using advanced machine learning methods. Three machine learning approaches, namely, support vector regression (SVR), deep neural network (DNN), and a Stacking integration model of SVR and DNN, were employed, respectively, to predict the soot mass concentration at the GPF inlet. The input data includes engine speed, torque, ignition timing, throttle valve opening angle, fuel injection pressure, and pulse width. Exhaust gas soot mass concentration at the three-way catalyst (TWC) outlet is obtained by an engine bench test. The results show that the correlation coefficients (R²) of SVR, DNN, and Stacking integration model of SVR and DNN are 0.937, 0.984, and 0.992, respectively, and the prediction ranges of soot mass concentration are 0–0.038 mg/s, 0–0.030 mg/s, and 0–0.07 mg/s, respectively. The distribution, median, and data density of prediction results obtained by the three machine learning approaches fit well with the test results. However, the prediction result of the SVR model is poor when the soot mass concentration exceeds 0.038 mg/s. The median of the prediction result obtained by the DNN model is closer to the test result, specifically for data points in the 25–75% range. However, there are a few negative prediction results in the test dataset due to overfitting. Integrating SVR and DNN models through stacked models extends the predictive range of a single SVR or DNN model while mitigating the overfitting of DNN models. The results of the study can serve as a reference for the development of accurate prediction algorithms to estimate soot loads in GPFs, which in turn can provide some basis for the control of the particulate mass and particle number (PN) emitted from GDI engines. Full article

Coordinating vehicle handling stability and energy consumption remains a key challenge for distributed driving electric vehicles (DDEVs). In this paper, a hierarchical torque vectoring control strategy is proposed to address this issue. First, a tire road friction coefficient (TRFC) estimator based on the fusion of vision and dynamic is developed to accurately and promptly obtain the TRFC in the upper layer. Second, a direct yaw moment control (DYC) strategy based on the adaptive model predictive control (MPC) is designed to ensure vehicle stability in the middle layer, where tire cornering stiffness is updated dynamically based on the estimated TRFC. Then, the lower layer develops the torque vectoring allocation controller, which comprehensively considers handling stability and energy consumption and distributes the driving torques among the wheels. The weight between stability and economy is coordinated according to the stability boundaries derived from an extended phase-plane correlated with the TRFC. Finally, Hardware-in-the-Loop (HIL) simulations are conducted to validate the effectiveness of the proposed strategy. The results demonstrate that compared with the conventional stability torque distribution strategy, the proposed control strategy not only reduces the RMSE of sideslip angle by 44.88% but also decreases the motor power consumption by 24.45% under DLC conditions, which indicates that the proposed method can significantly enhance vehicle handling stability while reducing energy consumption. Full article

Friction Stir Welding (FSW) is widely used for high-strength aluminum alloys due to its solid-state bonding, which ensures superior weld quality and service stability. However, thermo-mechanical interactions during welding can induce complex residual stress distributions, compromising joint integrity. Previous studies have primarily focused on thermal load-driven stress evolution, often neglecting mechanical factors such as the shear force generated by the stirring pin. This study develops a three-dimensional thermo-mechanical coupled finite element model based on a moving heat source. The model incorporates axial pressure from the tool shoulder and torque-derived shear force from the stirring pin. A hybrid surface–volumetric heat source is applied to represent frictional heating, and realistic mechanical boundary conditions are introduced to reflect actual welding conditions. Simulations on AA6061-T6 aluminum alloy show that under stable welding, the peak temperature in the weld zone reaches approximately 453 °C. Residual stress analysis indicates a longitudinal tensile peak of ~170 MPa under thermal loading alone, which reduces to ~150 MPa when mechanical loads are included, forming a characteristic M-shaped distribution. Further comparison with a Coupled Eulerian–Lagrangian (CEL) model reveals stress asymmetry, with higher tensile stress on the advancing side. This is primarily attributed to the directional shear force, which promotes greater plastic deformation on the advancing side than on the retreating side. The consistency between the proposed model and CEL results confirms its validity. This study provides a reliable framework for residual stress prediction in FSW and supports process parameter optimization. Full article