Search Results (298)

The growing need for high-efficiency and reliable propulsion systems in naval applications, particularly within the evolving landscape of submarine warfare, has led to an increased interest in multiphase Permanent Magnet AC motors. This study presents a modelling and simulation approach for a 3MW, seventeen-phase Permanent Magnet AC motor designed for submarine propulsion, integrating an AI-based drive control system. Despite the advantages of multiphase motors, such as higher power density and enhanced fault tolerance, significant challenges remain in achieving precise torque and variable speed, especially for externally mounted motors operating under deep-sea conditions. Existing control strategies often struggle with the inherent nonlinearities, unmodelled dynamics, and extreme environmental variations (e.g., pressure, temperature affecting oil viscosity and motor parameters) characteristic of such demanding deep-sea applications, leading to suboptimal performance and compromised reliability. Addressing this gap, this research investigates advanced control methodologies to enhance the performance of such motors. A MATLAB/Simulink framework was developed to model the motor, whose drive system leverages an AI-optimised dual fuzzy-PID controller refined using the Harmony Search Algorithm. Additionally, a combination of Indirect Field-Oriented Control (IFOC) and Space Vector PWM strategies are implemented to optimise inverter switching sequences for precise output modulation. Simulation results demonstrate significant improvements in torque response and control accuracy, validating the efficacy of the proposed system. The results highlight the role of AI-based propulsion systems in revolutionising submarine manoeuvrability and energy efficiency. In particular, during a test case involving a speed transition from 75 RPM to 900 RPM, the proposed AI-based controller achieves a near-zero overshoot compared to an initial control scheme that exhibits 75.89% overshoot. Full article

This study presents a comprehensive mathematical model of a semiconductor structure based on vanadium dioxide (VO₂), specifically in its conductive phase. The model was developed using the finite element method (FEM), enabling detailed simulation of the formation of a conductive channel under the influence of low-frequency alternating voltage (50 Hz). The VO₂ structure under investigation exhibits pronounced electric field concentration at the surface, where the field strength reaches approximately 5 × 10⁴ V/m, while maintaining a more uniform distribution of around 2 × 10⁴ V/m within the bulk of the material. The simulation results were validated experimentally using a test circuit. Minor deviations—no greater than 8%—were observed between the simulated and measured current values, attributed to magnetic core saturation and modeling assumptions. A distinctive feature of the model is its ability to incorporate the nonlinear dependencies of VO₂’s electrical properties on frequency. Analytical expressions were derived for the magnetic permeability and resistivity of VO₂, demonstrating excellent agreement with experimental data. The coefficients of determination (R²) for the frequency dependence of magnetic permeability and resistance were found to be 0.9976 and 0.9999, respectively. The current version of the model focuses exclusively on the conductive phase and does not include the thermally induced metal–insulator phase transition characteristic of VO₂. The study confirms that VO₂-based structures exhibit high responsiveness and nonlinear switching behavior, making them suitable for applications in electronic surge protection, current limiting, and switching elements. The developed model provides a reliable and physically grounded tool for the design and optimization components based on VO₂ in power electronics and protective circuitry. Full article

This study resolves a critical challenge in electromechanical brake system validation: conventional ABS/RBS integrated platforms’ inability to dynamically simulate tire-road adhesion characteristics during braking. We propose a fuzzy adaptive PID-controlled magnetic powder clutch (MPC) system that achieves ground braking force simulation synchronized with slip ratio variations. The innovation encompasses: (1) Dynamic torque calculation model incorporating the curve characteristics of longitudinal friction coefficient ($\phi$) versus slip ratio ($s$), (2) Nonlinear compensation through fuzzy self-tuning PID control, and (3) Multi-scenario validation platform. Experimental validation confirms superior tracking performance across multiple scenarios: (1) Determination coefficients R² of 0.942 (asphalt), 0.926 (sand), and 0.918 (snow) for uniform surfaces, (2) R² = 0.912/0.908 for asphalt-snow/snow-asphalt transitions, demonstrating effective adhesion characteristic simulation. The proposed control strategy achieves remarkable precision improvements, reducing integral time absolute error (ITAE) by 8.3–52.8% compared to conventional methods. Particularly noteworthy is the substantial ITAE reduction in snow conditions (236.47 vs. 500.969), validating enhanced simulation fidelity under extreme road surfaces. The system demonstrates consistently rapid response times. These improvements allow for highly accurate replication of dynamic slip ratio variations, establishing a refined laboratory-grade solution for EV regenerative braking coordination validation that greatly enhances strategy optimization efficiency. Full article

In integrated permanent magnet synchronous motors (IPMSMs) coupled with mechanical devices such as ball screws and reducers, complex nonlinear friction characteristics often arise, leading to asymmetrical distortions such as position “flat-top” and speed “ramp-up”. These phenomena significantly degrade the system’s positioning accuracy. To address this issue, this paper introduces a symmetry-inspired nonlinear predictive speed control approach based on the Stribeck piecewise linearized friction compensation and a generalized proportional integral (GPI) observer. The proposed method leverages the inherent symmetry in the Stribeck friction model to describe the nonlinear behavior, employing online piecewise linearization via the least squares method. A GPI observer was designed to estimate the lumped disturbance, including time-varying components in the speed dynamics, friction model deviations, and external loads. By incorporating these estimates, a nonlinear predictive controller was developed, employing a quadratic cost function to derive the optimal control law. The experimental results demonstrate that, compared to traditional integral NPC and PI controllers, the proposed method effectively restores system symmetry by eliminating the “flat-top” and “ramp-up” distortions while maintaining computational efficiency. Full article

Exploring the dynamics of nonlinear nanofluidic flow-induced vibrations, this work focuses on single-walled branched carbon nanotubes (SWCNTs) operating in a thermal–magnetic environment. Carbon nanotubes (CNTs), renowned for their exceptional strength, conductivity, and flexibility, are modeled using Euler–Bernoulli beam theory alongside Eringen’s nonlocal elasticity to capture nanoscale effects for varying downstream angles. The intricate interactions between nanofluids and SWCNTs are analyzed using the Differential Transform Method (DTM) and validated through ANSYS simulations, where modal analysis reveals the vibrational characteristics of various geometries. To enhance predictive accuracy and system stability, machine learning algorithms, including XGBoost, CATBoost, Random Forest, and Artificial Neural Networks, are employed, offering a robust comparison for optimizing vibrational and thermo-magnetic performance. Key parameters such as nanotube geometry, magnetic flux density, and fluid flow dynamics are identified as critical to minimizing vibrational noise and improving structural stability. These insights advance applications in energy harvesting, biomedical devices like artificial muscles and nanosensors, and nanoscale fluid control systems. Overall, the study demonstrates the significant advantages of integrating machine learning with physics-based simulations for next-generation nanotechnology solutions. Full article

Smart tools are limited by actuation–sensing integration and structural redundancy, making it difficult to achieve compactness, ultra-precision feed, and immediate feedback. This paper proposes a self-sensing giant magnetostrictive actuator-based turning tool (SSGMT), which enables simultaneous actuation and output sensing without external sensors. A multi-objective optimization model is first established to determine the key design parameters of the SSGMT to improve magnetic transfer efficiency, system compactness, and sensing signal quality. Then, a dynamic hysteresis model with a Hammerstein structure is developed to capture its nonlinear characteristics. To ensure accurate positioning and a robust response, a hybrid control strategy combining feedforward compensation and adaptive feedback is implemented. The SSGMT is experimentally validated through a series of tests including self-sensing displacement accuracy and trajectory tracking under various frequencies and temperatures. The prototype achieves nanometer-level resolution, stable output, and precise tracking across different operating conditions. These results confirm the feasibility and effectiveness of integrating actuation and sensing in one structure, providing a promising solution for the application of smart turning tools. Full article

Defects are a direct cause of failure in ferromagnetic components, which can be evaluated via electromagnetic non-destructive testing (ENDT) methods. However, the existing studies exhibit several limitations (e.g., inaccurate quantification, over-reliance on algorithms, and non-intuitive result presentation, among others) in quantitative defect evaluation. To accurately describe the quantitative relationship between ENDT signals and defect dimensional parameters, the electromagnetic model and electromagnetic induction model are introduced in this paper to elucidate the physical mechanism of ENDT, as both models provide a basis for the selection of the constitutive relationship for simulation analysis. Then, a higher precision three-dimensional nonlinear finite element simulation model is established, and the effects of the excitation parameters and detection positions on signal characteristics are investigated. The simulation results indicate that the excitation frequency influences both the detection depth and sensitivity of ENDT, while the voltage amplitude affects the peak strength of the magnetic signal. Consequently, the excitation parameters are determined to be a 10 Hz frequency with a 25 V amplitude. Based on the characterization of signal peaks at positions of 0°, 90°, 180°, and 270°, the characteristic parameter K_A of the peak electrical signal curve is proposed as a quantitative index for evaluating defects. The quantitative experimental results show that K_A is related to the defect dimension. Specifically, the K_A value monotonically decreases from a constant greater than 1 to a constant less than 1 as the defect length increases, K_A is positively correlated with the defect width, and K_A follows a parabolic trend (first increase and then decrease) as the defect depth increases. Notably, K_A values associated with defect width and depth remain below 1. All the above findings provide a basis for evaluating defect dimensions. The results of this paper provide a novel ENDT method for evaluating defects, which is of great significance for improving the accuracy of ENDT and promoting its engineering applications. Full article

Inter-turn short circuits (ITSCs) in permanent magnet synchronous motors (PMSMs) pose significant risks due to their subtle early symptoms and rapid degradation. To address this, we propose an online real-time diagnostic method for assessing the degradation state. This method employs the Sparrow Search Algorithm (SSA) for the online real-time identification of fault characteristic parameters. Following an analysis of the fault mechanisms of inter-turn short circuits, a mathematical model has been developed to include the short-circuit turns ratio and insulation resistance. An evaluation index has also been developed to assess the degree of fault-related degradation. To address the strong nonlinearity of parameters in the fault model, the SSA is employed for the real-time joint identification of parameters that characterize the relationship between fault location and degradation degree. Simulation experiments demonstrate that the SSA achieves convergence within 40 iterations, with a relative error below 5% and absolute error less than 0.007, outperforming traditional algorithms like the PSO, a significant improvement in the early detection of degradation caused by inter-turn short circuits and a step forward in technical support ensuring greater reliability and safety for the traction systems used in rail transit. Full article

The magnetic field of the Sun is formed by the mechanism of hydromagnetic dynamo. In this mechanism, the flow of the conducting medium (plasma) of the convective zone generates a magnetic field, and this field corrects the flow using the Lorentz force, creating feedback. An important role in dynamo is played by memory (hereditary), when a change in the current state of a physical system depends on its states in the past. Taking these effects into account may provide a more accurate description of the generation of the Sun’s magnetic field. This paper generalizes classical dynamo models by including hereditary feedback effects. The feedback parameters such as the presence or absence of delay, delay duration, and memory duration are additional degrees of freedom. This can provide more diverse dynamic modes compared to classical memoryless models. The proposed model is based on the kinematic dynamo problem, where the large-scale velocity field is predetermined. The field in the model is represented as a linear combination of two stationary predetermined modes with time-dependent amplitudes. For these amplitudes, equations are obtained based on the kinematic dynamo equations. The model includes two generators of a large-scale magnetic field. In the first, the field is generated due to large-scale flow of the medium. The second generator has a turbulent nature; in it, generation occurs due to the nonlinear interaction of small-scale pulsations of the magnetic field and velocity. Memory in the system under study is implemented in the form of feedback distributed over all past states of the system. The feedback is represented by an integral term of the type of convolution of a quadratic form of phase variables with a kernel of a fairly general form. The quadratic form models the influence of the Lorentz force. This integral term describes the turbulent generator quenching. Mathematically, this model is written with a system of integro-differential equations for amplitudes of modes. The model was applied to a real space object, namely, the solar dynamo. The model representation of the Sun’s velocity field was constructed based on helioseismological data. Free field decay modes were chosen as components of the magnetic field. The work considered cases when hereditary feedback with the system arose instantly or with a delay. The simulation results showed that the model under study reproduces dynamic modes characteristic of the solar dynamo, if there is a delay in the feedback. Full article

Granite is widely regarded as an ideal material for the construction of underground liquefied natural gas (LNG) storage reservoirs due to its high mechanical strength and broad geological availability. However, the ultra-low storage temperature of LNG (−162 °C) poses potential risks in altering the permeability of granite, which may compromise the long-term safety and integrity of the reservoir. To investigate the permeability characteristics and microstructural degradation of granite under low-temperature conditions, both coarse-grained and fine-grained granite samples were subjected to a series of experiments, including one-dimensional (1D) gas permeability tests (conducted before and after freeze–thaw cycles ranging from −20 °C to −120 °C), nuclear magnetic resonance (NMR) tests, and two-dimensional (2D) gas permeability tests performed under real-time low-temperature conditions. Experimental results indicated that the gas permeability of granite under real-time low-temperature conditions exhibited a linear increase as the temperature decreased. In contrast, the gas permeability after freeze–thaw cycling followed a nonlinear trend: it increased initially, plateaued, and then increased again as the freezing temperature continued to drop. A further analysis of pore structure evolution and permeability changes revealed distinct degradation mechanisms depending on grain size. In coarse-grained granite, freeze–thaw damage was primarily characterized by the initiation and propagation of new microcracks, which originated as micropores and expanded into mesopores. In fine-grained granite, the damage primarily resulted from the progressive widening of existing fissures, with micropores gradually evolving into mesopores over successive cycles. The study’s findings provide a useful theoretical foundation for the secure subterranean storage of LNG. Full article

Plasma arc welding (PAW) is commonly employed for welding medium and thick plates due to its capability of single-side welding and double-side forming. Ensuring welding quality necessitates real-time precise identification of the melting state. However, the intricate interaction between the plasma arc and the molten pool, along with substantial signal noise, poses a significant technical hurdle for achieving accurate real-time melting state identification. This study introduces a magnetically controlled method for identifying plasma arc melt-through, which integrates arc voltage and arc pool pressure. The application of an alternating transverse magnetic field induces regular oscillations in the melt pool by the plasma arc. The frequency characteristics of the arc voltage and pressure signals during these oscillations exhibit distinct mapping relationships with various fusion states. A hybrid feature extraction model combining gray correlation analysis (GRA) and the Pearson correlation coefficient (PCC) is devised to disentangle the nonlinear, non-smooth, and high-dimensional repetitive features of the signals. This model extracts features highly correlated with the fusion state to construct a feature vector. Subsequently, this vector serves as input for the fusion classification model, CNN-SVM, facilitating fusion state identification. The experimental results of melt-through under various welding speeds demonstrate the robustness of the proposed method for identifying melt-through through magnetic field-assisted melt pool oscillation, achieving an accuracy of 96%. This method holds promise for integration into the closed-loop quality control system of plasma arc welding, enabling real-time monitoring and control of melt pool quality. Full article

Quasi-zero stiffness (QZS) has become a promising way of realizing low-frequency vibration isolation, where magnetic springs have been widely adopted for constructing negative stiffness. However, existing single-layer magnetic springs often have a small-amplitude negative stiffness, so the loading capacity is low. In order to address this issue, this paper presents novel Halbach-cylinder magnetic springs (HCMSs) by using the Halbach array. Firstly, stiffness formulas of basic single-layer magnetic springs are analytically built based on the Amperian current model. The stiffness of the HCMS is derived from combining multiple single-layer magnetic springs. Then, nonlinear stiffness characteristics of both single-layer magnetic springs and HCMSs are investigated in terms of the amplitude, the uniformity, and the displacement range of negative stiffness. Analytical results show that HCMSs can generate negative stiffness with different equilibrium positions, and the amplitude of negative stiffness of HCMSs is much larger than that of single-layer magnetic springs. The amplitude of negative stiffness is in conflict with the uniformity, so a trade-off design is needed. In addition, increasing the number of layers of Halbach cylinders can be adopted to realize larger-amplitude and wider-range negative stiffness. This study will provide new insights into designing QZS with heavy-load capacity. Full article

This paper is centered around the autonomous displacement of the maglev ruler system, with the core objective of optimizing the performance of its horizontal control system. The horizontal positioning precision of the maglev ruler’s mover core plays a crucial role in determining the survey accuracy. Moreover, its horizontal system exhibits distinct nonlinear and strongly coupled characteristics. Initially, a mathematical model is meticulously established based on the principles of magnetic circuits and dynamics. By analyzing the relationship between the Ampere force and the coil current, and constructing the dynamic equations, it is clearly demonstrated that this system belongs to a nonlinear and strongly coupled type with two inputs and two outputs. Subsequently, the active disturbance rejection control algorithm is employed to address the decoupling issue, and a novel differential tracker, SYSTD, is designed. SYSTD is constructed using specific elementary functions. Through rigorous theoretical derivation, its favorable stability is verified. The basis for parameter selection is obtained through phase-plane analysis, and a detailed tuning method is provided. Additionally, a sensor-less control technology is proposed, where survey coils are wound along the horizontal control coils to estimate the position of the mover core. The simulation and experimental results indicate that the improved system showcases excellent performance. In the simulation, the positioning accuracy can reach ±5 μm, while, in the experiment, the control accuracy can achieve ±2 μm. It can effectively realize decoupling control and possesses good dynamic, static characteristics, as well as remarkable robustness. This research paves the way for the practical application of the maglev ruler system in fields such as coordinate survey. Full article

This study proposes a rotational speed measurement machine based on the flux-switching principle with a 6-stator-slot/19-rotor-pole (6s/19p) topology. With a rotor shape similar to a variable reluctance sensor (VRS), the proposed machine features a simple and robust structure while ensuring the same output frequency as VRS. Additionally, compared to the conventional 12s/10p topology, the 6s/19p configuration reduces permanent magnet (PM) consumption by half while maintaining high induced voltage characteristics. A nonlinear analytical model (NAM), which incorporates the harmonic modeling (HM) technique and an iterative process, is presented. This model more accurately captures the rectangular shape of the PM and stator teeth while accounting for core saturation effects. Based on this model, the optimal dimensions of the proposed machine are investigated to achieve the best performance for speed measurement applications. A coupling FEA simulation between Ansys Maxwell and Twin Builder further analyzes the machine’s performance. Compared to a commercial product of the same size, the proposed machine achieves 31.5% higher output voltage while ensuring lower linearity errors. Moreover, superior load characteristics are observed, with a voltage drop of only 1.58% at 1500 rpm and 30 mA. The proposed machine and NAM provide an improved solution and analytical tool for speed measurement applications. Full article

This paper introduces an effective method to improve the performance of a proton exchange membrane fuel cell (PEMFC) system powering a switched reluctance motor (SRM). Problems arise in this system due to the inherent torque and current ripples of the SRM, which result from its saliency and nonlinear magnetic characteristics. Another cause for these ripples is the unsmoothed DC voltage applied to the SRM caused by the switching operations of the DC-DC converter. These ripples are reflected in the PEMFC, leading to more losses and a reduced lifespan. Key parameters that can help mitigate torque and current ripples include the appropriate turn-on and turn-off angles of the SRM phases, as well as the DC-link voltage controller gains. This paper investigates three objectives to compare their effects on the PEMFC system: the SRM torque ripple factor, the DC-link voltage ripple factor, and the PEMFC current ripple factor. These objectives are optimized individually using the single-objective particle swarm and stochastic fractal search algorithms. Additionally, the multi-objective Lichtenberg and multi-objective Dragonfly algorithms are applied to optimize the three objectives concurrently. The optimal decision parameters are obtained from the Pareto front solution using the technique of the order of preference by similarity to the ideal solution method. The final results demonstrate that significant enhancement in the PEMFC current ripples and DC-link voltage ripples can be achieved by appropriately selecting the decision parameters using any proposed objective. Full article