Search Results (316)

To address the stringent insulation safety requirements of modern high-voltage transformers, accurately characterizing the transient electric field is critical. However, a significant problem remains: current engineering models typically rely on static capacitive distributions, failing to capture the dynamic electric field distortion induced by rapid space charge injection under lightning impulses. Therefore, a non-contact spatial electric field measurement method based on the optical Kerr effect was employed to analyze the influence of electrode material, voltage amplitude, and wavefront time. Unlike traditional simulation models that often assume constant mobility and focus solely on the shielding effect, this study reveals a non-monotonic electric field evolution driven by a ‘Static-Dynamic’ mode transition. The proposed model highlights two critical breakthroughs: (1) Mechanism Innovation: It experimentally verifies that charge injection is governed by the ion charge-to-mass ratio rather than just the work function, leading to a newly identified field enhancement phase during the wavefront that overcomes the limitations of capacitive models that underestimate transient stress. (2) Parameter Quantification: Precise spatiotemporal thresholds are established—negative charges traverse the gap within ~200 ns, while positive charges require ~10 μs to reach equilibrium. These findings provide experimentally calibrated time constants for simulation correction and offer new criteria for optimizing electrode materials in UHV transformers to mitigate transient field distortion. Full article

The numerical analysis of dissipative energy in dynamic problems involving impact and contact phenomena relies on the physical principles of classical thermodynamics and on the constitutive equations of the material, supplemented by some additional considerations of potential contact interfaces. From the mathematical perspective, we come to a weak form of partial differential equation(s) of evolution with initial, boundary, and interface conditions, whose numerical analysis is required using the method of discretisation in time and typically the finite element technique. Dissipative energy is an important metric for quantifying the portion of mechanical work that is permanently converted to plastic work and thermal energy, among other applications. Crucially, the localised accumulation of this energy, often expressed as the plastic work density, is the primary physical parameter driving microstructural changes, damage initiation, and crack propagation under intense loading. This paper demonstrates how the dissipative energy resulting from material nonlinearities can be evaluated in dynamic problems involving the impact of one body on another and provides a quantitative comparison of numerically calculated dissipated energy using three types of nonlinear constitutive material models, namely the plastic material model with Rankine–Hill criterion, the Mazars damage model, and the Kelvin–Voigt viscoelastic model. Full article

To solve the problems of high resistance and blockage in stubble-breaking operations, it is necessary to reveal the interaction mechanism between disc coulters and crop root–soil composites. This study developed a discrete element method–multi-body dynamics (DEM-MBD) coupling model of the stubble-breaking operation and verified the accuracy of the model through soil bin tests (error < 20%) and field experiments (error < 32%). The model was used to investigate the effects of different design parameters (coulter type and disc radius) and operating parameters (tillage speed and depth) on the stubble-breaking operation. The results showed that due to the significant strengthening effect of roots on soil, the resistance of disc coulter stubble-breaking operation was high; the number of roots in contact with the blade edge and the amount of root deformation significantly affected the resistance of the disc coulter; irreversible deformation of roots and soil could easily lead to the holes and root hairpin effects in the seeding furrow; compared to plain disc coulters, the difference in the time of deformation and fracture of the roots made the resistance of the notched coulter lower. The wavy disc coulter with a longer edge curve made its resistance higher; the disc coulter with a greater radius, higher tillage speed, and deeper tillage depth significantly increased the tillage resistance. However, the disc coulter with a greater radius or a higher tillage speed was beneficial for improving stubble-breaking performance. This study revealed the interaction mechanism between disc coulters and maize root-soil composites, providing a theoretical basis for the optimization design of no-till stubble-breaking devices. Full article

Low adhesion conditions can lead to significant wheel slip during braking for high-speed trains, resulting in severe wheel–rail rolling contact fatigue issues. The objective of this paper is to reproduce the dynamic wheel–rail adhesion characteristics of high-speed train braking with large creepage using the transient non-Hertzian ECF model under wet conditions and to clarify the underlying mechanisms. The Kik–Piotrowski (KP) model is used to solve the wheel–rail normal contact problem, and the corresponding non-elliptical adaptive method is adopted to modify the ECF model considering time-dependent effects for solving the tangential contact problem. The dynamic large creepage adhesion characteristics of high-speed trains under wet conditions during braking are analyzed. Furthermore, the effect of braking initial speeds and longitudinal creepage variation curves on dynamic adhesion characteristics is discussed. The results indicate that the large creepage adhesion characteristic curve of high-speed trains during braking exhibits a loading stable phase and an unloading stable phase, both of which effectively enhance the utilization of wheel–rail adhesion. Full article

During the switch-off process, the contact erosion generated by the AC contactor will seriously affect its performance, thereby directly influencing the normal operation of the power equipment. Therefore, aiming at the problem of contact erosion caused by contact bounce during the switch-on and switch-off period of AC contactors, this paper designed the driving circuits during the switch-on, holding, and switch-off processes. During the switch-on process, DC excitation was used instead of AC excitation to eliminate or reduce the contact bounce. During the holding process, low-voltage DC was used instead of high-voltage AC to save energy and reduce coil losses. During the switch-off process, the contact current was used as the control factor, and the scheme of shunting control was employed to achieve the goal of few or even no arcs. In addition, in order to detect the high voltage and large current signals in the main circuit, the three-phase voltage acquisition circuit and three-phase current acquisition circuit were designed. Therefore, a whole process dynamic control which included the switch-on, holding, and switch-off was formed. Through simulation testing and relevant experimental testing, the results demonstrated the correctness and effectiveness of the designed circuit. Full article

This research aims to identify a suitable design solution that models the behavior of a human’s center of mass. This solution can be implemented in an exoskeleton structure that is especially designed for children who require walking assistance and rehabilitation. The primary problem posed by exoskeleton designs is representing the effect of ground–foot contact on exoskeleton behavior under kinematic and dynamic conditions. To mitigate this, our main research objective was to develop a mechanical system that demonstrates the human center of mass (CoM) behavior on an exoskeleton designed for children with Duchenne Muscular Dystrophy. The research focuses on modeling human CoM behavior under kinematic circumstances and transferring this into a mechanical system conceptual design. The obtained results validate the proposed mechanical system through a comparative analysis between numerical processing, virtual prototyping, and experimental specific methods and procedures. Full article

The high-performance aeroengine operates under extreme loads. In engineering practice, the vibration problems caused by stator vibrations have become increasingly prominent, with impacts on the rotor dynamic behavior. This paper takes the rotor–stator system of aeroengines as the analysis object and studies the influence of stator modal vibration on the rotor dynamic behavior. The dynamic model of the rotor–stator system has been established, and the influence of the contact state of cylindrical roller bearings (CRBs) has been analyzed by considering bearing clearance. To precisely capture the transient contact state within the CRBs, a numerical method combining the Newmark-β method with the Event Function has been developed. The numerical calculation results show that the collision effect introduced by the bearing clearance will excite a localized stator mode at the supercritical state, which fundamentally alters the rotor dynamic behavior: generating prominent combination frequencies $f_M\pm f_r$ due to modulation between the rotor rotation $f_r$ and the stator vibration $f_M$. Moreover, good consistency between the experimental and calculated results has been obtained. This study demonstrates that the stator modal vibration can critically modify rotor dynamic behavior in supercritical operation, leading to potentially hazardous non-synchronous whirl. The integrated model and numerical method provide a robust framework for analyzing complex rotor–stator interactions, offering significant insights for vibration control and fault diagnosis in high-speed rotating machinery. Full article

Attempting to make machines mimic human walking, grasping, balancing, and other behaviors is a deep exploration of cognitive science and biological principles. Due to the existing prediction lag problem, an error compensation mechanism that integrates historical motion data is proposed. By constructing a humanoid autonomous walking control system, this paper aims to use a three-dimensional linear inverted pendulum model to plan the general framework of motion. Firstly, the landing point coordinates of the single foot support period are preset through gait cycle parameters. In addition, it is substituted into dynamic equation to solve the centroid (COM) trajectory curve that conforms to physical constraints. A hierarchical whole-body control architecture is designed, with a task priority based on quadratic programming solver used at the bottom to decompose high-level motion instructions into joint space control variables and fuse sensor data. Furthermore, the numerical iterative algorithm is used to solve the sequence of driving angles for each joint, forming the control input parameters for driving the robot’s motion. This algorithm solves the limitations of traditional inverted pendulum models on vertical motion constraints by optimizing the centroid motion trajectory online. At the same time, it introduces a contact phase sequence prediction mechanism to ensure a smooth transition of the foot trajectory during the switching process. Simulation results demonstrate that the proposed framework improves disturbance rejection capability by over 30% compared to traditional ZMP tracking and achieves a real-time control loop frequency of 1 kHz, confirming its enhanced robustness and computational efficiency. Full article

This work constructs a new mathematical model for the vibrations of a Bernoulli beam that can come in contact with a reactive obstacle situated below its right end. The obstacle reaction is described by the Damped Normal Compliance (DNC) contact condition. This condition, unlike the usual Normal Compliance (NC) contact condition, takes into account the energy dissipation during the contact process. The steady states of the model are described and the model is studied computationally for different values of obstacle stiffness and damping. The computational scheme is shown numerically to converge with a rate higher than 1. The numerical simulations illustrate how the beam’s end penetration and vibrations differ in soft vs. stiff obstacle environments, and how damping modifies the dynamic behavior. The results may be useful for vibration control and material interaction in settings when collisions or repetitive contacts occur. By providing computational and analytical insights, the study is an addition to the currently maturing Mathematical Theory of Contact Mechanics (MTCM). Full article

In this study, an innovative measurement method integrating lateral confocal technology and composite motion control is proposed to address the physical space constraints and data processing problems in the detection of the shape and position errors in deep holes with large aspect ratios and small diameters. By designing a lateral confocal displacement sensor and a cantilever measuring device, we break through the spatial constraints of $\mathrm{\varnothing }6 \mathrm{m}\mathrm{m}$ deep-hole inspection and solve the problems of rigidity and surface damage in the traditional contact probe. We constructed an axis-rotation coordinated motion control model and found that the measuring points were densely arranged in a helical trajectory along the inner wall of the hole. We developed the “virtual slicing–B-spline reconstruction” algorithm and used the adaptive motion control algorithm to achieve a more efficient measurement of the hole. The innovative “virtual slicing–B-spline reconstruction” algorithm, using adaptive grouping, dynamic slicing, and a fourth-order B-spline-fitting hierarchical processing framework, reached a straightness error assessment result of the $1 \mathsf{\mu }\mathrm{m}$ order. Experiments show that, under $0.5 \mathrm{m}\mathrm{m}∕\mathrm{s}$ feed rate and $12 \mathrm{r}\mathrm{m}\mathrm{p}$ rotational speed, the standard deviation of straightness is ≤$0.0008 \mathrm{m}\mathrm{m}$ and the standard deviation of cylindricity is ≤$0.0064 \mathrm{m}\mathrm{m}$; compared to the CMM (coordinate measuring machine) measurement results, the cylindricity and straightness evaluation errors obtained by the new measurement method are reduced by $4.6\%$ and $4.5\%$, respectively. It provides a technical solution that improves both accuracy and efficiency for the precision inspection of small-diameter deep holes. Full article

This study introduces the Graph-Structured Physics-Informed DeepONet (GS-PI-DeepONet), a novel neural network framework designed to address the challenges of solving parametric Partial Differential Equations (PDEs) in structural analysis, particularly for problems with complex geometries and dynamic boundary conditions. By integrating Graph Neural Networks (GNNs), Deep Operator Networks (DeepONets), and Physics-Informed Neural Networks (PINNs), the proposed method employs graph-structured representations to model unstructured Finite Element (FE) meshes. In this framework, nodes encode physical quantities such as displacements and loads, while edges represent geometric or topological relationships. The framework embeds PDE constraints as soft penalties within the loss function, ensuring adherence to physical laws while reducing reliance on large datasets. Extensive experiments have demonstrated the GS-PI-DeepONet’s superiority over traditional Finite Element Methods (FEMs) and standard DeepONets. For benchmark problems, including cantilever beam bending and Hertz contact, the model achieves high accuracy. In practical applications, such as stiffness analysis of a recliner mechanism and strength analysis of a support bracket, the framework achieves a 7–8 speed-up compared to FEMs, while maintaining fidelity comparable to FEM, with R² values reaching up to 0.9999 for displacement fields. Consequently, the GS-PI-DeepONet offers a resolution-independent, data-efficient, and physics-consistent approach for real-time simulations, making it ideal for rapid parameter sweeps and design optimizations in engineering applications. Full article

In the field of rotating machinery, such as marine propulsion shafting, magnetic bearing-supported propulsion systems have garnered significant attention due to their non-mechanical contact advantages. To address the problem that the design of magnetic bearing controllers, based on theoretical models, neglects the dynamic characteristics of practical components like power amplifiers and displacement sensors, making it difficult to achieve ideal performance in practical applications, this paper proposes a control method for Hybrid Magnetic Bearings (HMBs) that combines a time-domain identification model with robust control. The method first models the power amplifier, HMB, and displacement sensor as an equivalent single system and obtains its high-precision transfer function model by performing system identification on its time-domain data using the least squares method. Based on this foundation, a PID controller is designed using the loop-shaping method to enhance the system’s robustness and control performance. Both simulations and experiments on an HMB test rig confirmed the controller’s effectiveness. The system showed excellent levitation, dynamic stability, and disturbance rejection, with experimental results closely matching simulations. The experimental results are consistent with the simulation results. This method provides a practical and feasible technical approach for enhancing the control performance of magnetic bearing-supported propulsion shafting. Full article

Targeting the problem of lack of theoretical model for wear calculation of key actuators in the knotter, a tripping mechanism of knotter based on the principle of elastic deformation was designed. A rigid–flexible coupling dynamic analysis model of the tripping mechanism was established based on the modal stress method, and the contact force time history curves and dynamic stress results between the groove cam and the ball roller, as well as between the knotter jaw and the tripping plate slot, were simulated. Based on MSC, Marc MENTAT, a finite element wear calculation model of the tripping mechanism, was constructed. Through 600 simulations equivalent to 6000 working cycles, the wear cloud maps of the tripping plate and the large gear groove cam were obtained, and the key wear areas and expansion trends were analyzed. The rapid wear tests were conducted by using a self-made knotter fatigue wear test bench, which showed that the maximum deviation between the measured value and the simulated value of the contact pair wear was less than 10%. This verified that the proposed wear model for the tripping mechanism can be used for calculating the contact pair wear of the mechanism, providing a reference for the heat treatment process of the surface hardness of the parts. Full article

Gear defect detection directly affects the operational reliability of critical equipment in fields such as automotive and aerospace. Gear defect detection technology based on machine vision, leveraging the advantages of non-contact measurement, high efficiency, and cost-effectiveness, has become a key support for quality control in intelligent manufacturing. However, it still faces challenges including difficulties in semantic alignment of multimodal data, the imbalance between real-time detection requirements and computational resources, and poor model generalization in few-shot scenarios. This paper takes the paradigm evolution of gear defect detection technology as the main line, systematically reviews its development from traditional image processing to deep learning, and focuses on the innovative application of intelligent algorithms. A research framework of “technical bottleneck-breakthrough path-application verification” is constructed: for the problem of multimodal fusion, the cross-modal feature alignment mechanism based on Transformer network is deeply analyzed, clarifying its technical path of realizing joint embedding of visual and vibration signals by establishing global correlation mapping; for resource constraints, the performance of lightweight models such as MobileNet and ShuffleNet is quantitatively compared, verifying that these models reduce Parameters by 40–60% while maintaining the mean Average Precision essentially unchanged; for small-sample scenarios, few-shot generation models based on contrastive learning are systematically organized, confirming that their accuracy in the 10-shot scenario can reach 90% of that of fully supervised models, thus enhancing generalization ability. Future research can focus on the collaboration between few-shot generation and physical simulation, edge-cloud dynamic scheduling, defect evolution modeling driven by multiphysics fields, and standardization of explainable artificial intelligence. It aims to construct a gear detection system with autonomous perception capabilities, promoting the development of industrial quality inspection toward high-precision, high-robustness, and low-cost intelligence. Full article

With the development of infrared imaging technology and the integration of intelligent algorithms, the realization of non-contact, dynamic and real-time detection of SF6 gas leakage based on infrared video has been a significant research direction. However, the existing real-time detection algorithms exhibit low accuracy in detecting SF6 leakage and are susceptible to noise, which makes it difficult to meet the actual needs of engineering. To address this problem, this paper proposes a real-time SF6 leakage detection method, VGEC-Net, based on multi-channel fusion and spatiotemporal feature extraction. The proposed method first employs the ViBe-GMM algorithm to extract foreground masks, which are then fused with infrared images to construct a dual-channel input. In the backbone network, a CE-Net structure—integrating CBAM and ECA-Net—is combined with the P3D network to achieve efficient spatiotemporal feature extraction. A Feature Pyramid Network (FPN) and a temporal Transformer module are further integrated to enhance multi-scale feature representation and temporal modeling, thereby significantly improving the detection performance for small-scale targets. Experimental results demonstrate that VGEC-Net achieves a mean average precision (mAP) of 61.7% on the dataset used in this study, with a mAP@50 of 87.3%, which represents a significant improvement over existing methods. These results validate the effectiveness and advancement of the proposed method for infrared video-based gas leakage detection. Furthermore, the model achieves 78.2 frames per second (FPS) during inference, demonstrating good real-time processing capability while maintaining high detection accuracy, exhibiting strong application potential. Full article