Search Results (93)

This paper proposes an efficient analytical modeling method for the air-gap flux density in hydro-generators under eccentricity faults. The method specifically addresses the multi-factor coupling characteristics induced by the combined effects of stator slotting, salient-pole rotor structure, and eccentricity. Based on geometric analytical derivation, individual models for air-gap length considering stator slotting, rotor salient-pole structure, and eccentricity are established, and an analytical expression for the air-gap length under coupled conditions is derived. By incorporating the spatial distribution characteristics of the magnetomotive force, a mathematical model for no-load air-gap flux density is constructed, enabling the rapid calculation of air-gap magnetic field distribution. Finite element verification conducted on an 84-slot, 10-pole hydro-generator demonstrates that the proposed method accurately reflects the periodic fluctuations and amplitude variations in the magnetic flux density under both normal and eccentric conditions. Compared with the finite element method (FEM), the analytical approach significantly enhances computational efficiency while maintaining high accuracy, providing effective theoretical support for the structural optimization and eccentricity fault diagnosis of hydro-generators. Full article

Permanent magnet synchronous motors (PMSMs) operating at low speeds are susceptible to parameter mismatches, periodic harmonics, and various internal and external disturbances, which result in steady-state current errors and low-frequency speed oscillations. To address these issues and improve low-speed PMSM performance, an automatic tuning disturbance rejection incremental deadbeat predictive current control (AT-DR-IDPCC) method is proposed. First, an incremental extended-state observer (IESO) is incorporated into the incremental deadbeat predictive current control (IDPCC) framework to estimate and compensate for lumped disturbances caused by resistance and inductance mismatches, thereby improving parameter robustness. Meanwhile, a quasi-resonant controller (QRC) is connected in parallel with the current loop to selectively suppress sixth-order current harmonics induced by inverter nonlinearities and flux harmonics. Furthermore, a deep deterministic policy gradient (DDPG)-based parameter optimization scheme is introduced to automatically tune the controller parameters, overcoming the limitations of conventional trial-and-error tuning and achieving the coordinated optimization of dynamic response, steady-state accuracy, and disturbance rejection capability. Simulation and experimental results demonstrate that, compared with proportional–integral (PI) control and IDPCC incorporating the IESO (IESO-IDPCC), AT-DR-IDPCC reduces the phase current’s total harmonic distortion (THD) by 56.1% and 23.7% while also decreasing the speed fluctuation amplitude by approximately 50% and 20%, respectively. The proposed method significantly enhances the robustness, harmonic suppression capability, and low-speed control performance of PMSM drives. Full article

This study presents a numerical model of electromagnetic and thermal processes characteristic of a submerged arc furnace. Because direct modeling of a full-scale industrial furnace is complex and difficult to validate experimentally, a laboratory system without an electric arc is considered at this stage. The system reproduces the main features of current supply and energy distribution in the conductive region of the furnace bath. The model is implemented in ANSYS Fluent 2020 R1 using user-defined scalar equations for the electric potential, the components of the magnetic vector potential, and their time derivatives. The implementation was assessed in terms of mesh independence, time-step sensitivity, current and energy balances. The calculations yielded consistent distributions of electric potential, current density, magnetic flux density, Joule heat generation, and temperature. Heating was described using a two-stage scheme: the transient electromagnetic problem is first solved to obtain period-averaged Joule heat generation, which is then used as a source term in the energy equation. The model represents the first stage of a computational framework for submerged arc furnace modeling: at this stage, it is developed and assessed using a simplified laboratory configuration without an electric arc, while in future work it can be supplemented with an arc-channel description and extended to industrial furnace conditions. Full article

Flux linkage observers have been widely employed in permanent magnet synchronous motor (PMSM) sensorless drives, and the back electromotive force (BEMF) estimated by an extended state observer (ESO) can be used to compensate for the position estimation error of the flux linkage observer. However, inverter dead time introduces periodic disturbances into the estimated synchronous reference frame, thereby contaminating the BEMF estimation and degrading the accuracy of position compensation. To this end, a position estimation error compensation strategy based on a fractional-order quasi-resonant extended state observer (FOQR-ESO) is proposed. First, a PMSM voltage model incorporating the inverter dead-time effect is established, and the resulting sixth-order harmonic component in the estimated synchronous reference frame is analyzed. Then, a fractional-order quasi-resonant element is embedded into the ESO to enhance its capability to estimate harmonic components. The proposed FOQR-ESO separates the BEMF-related component from the sixth-order harmonic disturbance, while the fractional-order parameter provides an additional degree of freedom for shaping the observer frequency response. Simulation and experimental results demonstrate that the proposed FOQR-ESO effectively suppresses the sixth-order harmonic disturbance and achieves higher rotor position estimation accuracy than the conventional ESO and the integer-order quasi-resonant extended state observer (IOQR-ESO). Full article

To investigate the nonlinear dynamic characteristics of magnetic suspended dual-rotor systems, this study examines periodic and quasi-periodic responses induced by bearing nonlinearities, including flux leakage and magnetic saturation effects. A nonlinear dynamic model is established using the finite element method, incorporating unbalance excitation and nonlinear bearing forces. A comprehensive parametric analysis is conducted to evaluate the effects of rotational speed, initial stiffness, and initial damping on the system’s dynamic responses and bifurcation behavior. The results reveal the occurrence of period-5 and quasi-periodic vibrations under nonlinear bearing conditions. In the quasi-periodic regime, low-frequency components dominate, and the force–current characteristics of the magnetic bearings spread over a wider band, reflecting a multi-valued force–current relationship. Furthermore, decreasing initial stiffness and increasing damping advance the onset of quasi-periodic responses and reduce the corresponding critical rotational speed. Notably, through real-time control adjustment, quasi-periodic motion can be converted into periodic motion, thereby distinguishing the system from conventional mechanically supported rotor systems. Experimental results obtained from a magnetic suspended dual-rotor test rig validate both the bearing-force model and the dynamic model, and further reveal periodic variations in system response under different speed ratios. Full article

We perform three-dimensional general relativistic magnetohydrodynamic (GRMHD) simulations of a near-maximally spinning black hole (spin parameter $a=0.998$) with varying initial magnetic field geometries, systematically exploring the parameter space connecting magnetically arrested disk (MAD), intermediate (INT), and standard and normal evolution (SANE) accretion states. The magnetic flux threading the black hole horizon emerges as the fundamental state variable controlling jet efficiency, flow magnetization, and radiative output across all three states. We introduce complementary diagnostics—broadband spectral energy distributions spanning radio through hard X-ray frequencies and time-resolved X-ray light curves—that together connect simulation dynamics directly to multiwavelength observables. The radiative output follows a clear MAD > INT > SANE hierarchy in time-averaged luminosity, mean X-ray emission, as well as variability. Furthermore, MAD exhibits the highest fractional variability through quasi-periodic magnetic flux eruption events, and INT and SANE show moderate variability driven by episodic reconnection and stochastic MRI turbulence, respectively. Scaling to GRS 1915+105, Cyg X-1, and HLX-1, we demonstrate that all twelve temporal classes of GRS 1915+105 map naturally onto our three magnetic states, Cyg X-1’s persistent hard state is reproduced by a sustained INT configuration, and HLX-1’s extreme luminosities arise through efficient Blandford–Znajek extraction in MAD states scaled to higher black hole mass. Full article

Nanofluids, which are suspensions of nanoparticles within base fluids, are employed in industries such as electronics, automotives, nuclear power, and defense to enhance thermal management, mass transfer, and microchip cooling. This study investigates heat transfer generation on a stretching sheet incorporating aluminum oxide ${(\mathrm{A}\mathrm{l}}_2{\mathrm{O}}_3)$ and magnetite ${(\mathrm{F}\mathrm{e}}_3{\mathrm{O}}_4)$ nanoparticles under conditions of constant and varying wall temperatures. Key factors considered include variable viscosity, a periodic magnetic field, and thermal radiative flux, underscoring the thermal advantages of nanoparticles in nuclear reactor applications. The Crank–Nicolson method, an implicit finite difference technique, was utilized to solve the mathematical model, with partial differential equations discretized and approximated using an explicit method. An explicit iterative method was employed to solve the momentum and energy equations in a Python solver, while boundary values were analytically resolved based on discretized equations. In the explicit method, values at the subsequent time step (n + 1) were directly computed from the current time step (n) values. This approach necessitated a sufficiently small time step to satisfy the Courant–Friedrichs–Lewy (CFL) condition for numerical stability. The study examined the mass and heat transfer characteristics of a magnetizable nanofluid. While nanoparticles enhanced heat transfer, magnetic interactions, viscosity, and thermal radiation impeded it. A periodic magnetic field was applied perpendicularly to the plates with a constant pressure gradient, utilizing a magnetic phase angle to decelerate and control flow and heat convection modulation. Full article

To address the insufficient accuracy of conventional permanent magnet synchronous motor (PMSM) models caused by neglecting magnetic saturation nonlinearity and periodic parameter disturbances, a nonlinear motor system model integrating a Physics-Informed Neural Network (PINN) is developed. By exploiting the differential relationships among incremental inductance, flux linkage, and magnetic energy, the voltage and torque equations considering rotor position variation are derived, and analytical expressions for the derivatives of incremental inductances are obtained. To reduce the computational burden of PINN in system-level simulations, linear and nonlinear approximation strategies based on incremental inductances and their derivatives are proposed, which significantly reduce the frequency of PINN calls while maintaining model accuracy. CPU/GPU collaborative computation and cross-frequency-domain scheduling are further implemented to improve simulation efficiency. Considering the influence of the test bench mechanical dynamics, an electromechanical–magnetic coupled simulation model is established. The accuracy of the proposed nonlinear motor model is validated through two-phase short-circuit tests as well as simulations and test bench experiments under sinusoidal and non-sinusoidal excitations. The results demonstrate that the proposed model accurately captures the nonlinear electromagnetic characteristics of PMSMs while significantly improving system simulation efficiency. Full article

Pipeline integrity is crucial for ensuring the safe and efficient transportation of hydrocarbons. One of the essential methods for maintaining pipeline integrity is periodic inspection using Pipeline Inspection Gauges (PIGs). These PIGs traverse extensive pipeline networks, collecting critical data related to inertial navigation and inspection technologies, such as geometric, ultrasonic, or magnetic flux inspection. Following an inspection, data is downloaded for post-processing to identify and accurately locate pipeline anomalies. Accurate positioning of indications is crucial for effective repair or maintenance of the identified pipeline section. Thus, ongoing efforts aim to improve the precision of indication positioning. This study introduces an innovative method and model for deriving pipeline trajectory characteristics to enhance positioning accuracy. The method is based on distance sampling of odometers, improving the PIG displacement measurement by implementing multiple odometries. Using the method described in this work can compensate for odometer slip, since the distance measurement error was reduced from 15.67% to 1.38%. The model simulates (three and four) odometer trajectories in curvature and calculates the curvature along the pipeline based on odometer data. The curvature model is evaluated with real data obtained from a test circuit, demonstrating that the proposed method and model technique can yield trajectory characteristics such as curvature detection; we can differentiate linear sections from bend sections in the test circuit. However, the curvature measurement error remains considerable due to odometer slippage. Therefore, future work proposes using additional odometers to improve measurement accuracy. Full article

Variable reluctance sensors are widely adopted for robust and contactless detection of motion in harsh and space-constrained environments. This paper presents a MEMS-based variable reluctance induction sensor for the noncontact characterization of rotating ferromagnetic targets, based on a micromachined planar micro-coil coupled with an external permanent magnet. The rotation of a ferromagnetic object modulates the magnetic circuit reluctance, generating a voltage signal across the micro-coil that encodes information on the target rotational speed, proximity, and cross-sectional shape. Sensor operation is investigated through a lumped-element magnetic–electrical circuit model and finite-element magnetostatic simulations, quantifying the effects of target diameter, distance, and angular position on the linked magnetic flux. Experimental validation is performed using rotating drill bits as representative targets and a dedicated high-gain, high-input-impedance front-end circuit to amplify the induced voltage. Measured results at fixed rotation frequency show periodic voltage waveforms whose amplitude and shape vary consistently with target geometry, proximity and speed. Reliable detection is achieved for rotational speeds up to 1500 rpm, for drill bit diameters as small as 5 mm, and at sensor-to-target distances up to 8 mm. These results demonstrate the potential of MEMS variable reluctance induction sensors for compact speed sensing and target shape detection. Full article

In this study, we extract a 7-day binned $\gamma$-ray light curve from 2008 August to 2019 March in the energy range 0.1–300 GeV and identify four outburst periods with peak flux of >8.0$\times {10}^{-7}$ ph ${\mathrm{cm}}^{-2}{\mathrm{s}}^{-1}$. Four active states in the optical are also marked during this period. The fastest variability timescale suggests the emission region radius is R ∼ 2.4$\times {10}^{16}$ cm, and the observed emission region lies within <0.7 pc distance from the central engine. The majority of short-timescale flares exhibit a symmetric temporal profile, implying that the rise and decay timescales are dominated by disturbances caused by dense plasma blobs passing through the standing shock front in the jet region. To understand the properties of the source jets, we employ a standard one-zone leptonic scenario to model the broadband spectral energy distributions (SEDs) of flaring periods and determine that the $\gamma$-ray spectrum is better reproduced when the dissipation region of the jet is located within the molecular torus (MT). The $\gamma$-ray spectra from the outburst phases show an obvious spectral break with a break energy between 3.00 and 7.08 GeV, which may be attributed to an intrinsic break in the energy distribution of radiating particles. The studies of the survival time of a sheet before being destroyed by the turbulent motions of plasma (${\tau }_{cs}\sim 2.9\times {10}^4$ s), the shock acceleration time ($t_{acc}\sim$$4.3\times {10}^4$ s), and the minimum interaction height ($Z_{min}$ ≈ 2.57–4.55$\times {10}^{17}$ cm > $R_{BLR}$ ∼ 1.0$\times {10}^{17}$ cm) suggest that the $\gamma$-ray flaring event maybe caused by a magnetic reconnection mechanism, but we cannot completely rule out the shock-in-jet model. Full article

The accuracy of online parameter identification for permanent magnet synchronous motors (PMSMs) is constrained by discrete model errors, rank deficiency in the steady-state identification matrix, and voltage deviations resulting from inverter nonlinearities. This paper proposes a multi-parameter identification method acting as a high-precision virtual sensor, based on Zero-Order Hold (ZOH) discretization and an inverter nonlinear voltage compensation scheme utilizing a dual-sampling strategy. First, a discrete model of the PMSM, accounting for rotor position variations within the control period, is established using the ZOH discretization method. Compared with the forward Euler discretization method, this approach effectively minimizes discretization model errors, especially under high-speed operating conditions where rotor position variations are significant. Second, the rank deficiency problem of the steady-state identification matrix is overcome by combining d-axis small-signal injection with a dual-sampling strategy. Furthermore, the Forgetting Factor Recursive Least Squares (FFRLS) algorithm is introduced to achieve online multi-parameter identification. Finally, the influence mechanisms of the dead-time effect, power switch voltage drop, and turn-on delay on the output voltage are analyzed. Consequently, an inverter nonlinear voltage compensation strategy tailored for the dual-sampling mode is proposed. Experimental results demonstrate that the proposed method significantly enhances parameter identification accuracy across the entire speed range. Specifically, under high-speed conditions, the identification errors for resistance, inductance, and flux linkage are maintained within 5.47%, 4.05%, and 2.46%, respectively. Full article

Modulated twelve-voltage-vector model predictive current control (MPCC), which applies two or three voltage vectors per control period, exhibits superior steady-state performance compared to modulated six-active-voltage-vector MPCC and conventional MPCC. However, implementing modulated twelve-voltage-vector MPCC requires accurate knowledge of the permanent magnet synchronous motor drive’s inductance and permanent magnet (PM) flux linkage parameters for selecting suboptimal and optimal voltage vectors, as well as computing the duty cycles of optimal vectors. Consequently, its control performance is more sensitive to model parameter inaccuracies. To mitigate parameter sensitivity, a robust modulated twelve-voltage-vector MPCC algorithm based on a model reference adaptive system (MRAS) is proposed. The MRAS-based observer estimates the inductance and PM flux linkage parameters in real time, enhancing model accuracy. The observer is designed with a stability analysis framework, where the proportional and integral gains of the MRAS are theoretically derived to ensure precise parameter estimation. The effectiveness of the proposed algorithm is validated through simulation results, demonstrating satisfactory control performance even under parameter mismatches. Specifically, the torque ripple is reduced from 1.1 A to 0.6 A, corresponding to a reduction of 45.5%. Similarly, the stator flux ripple decreases from 1.75 A to 1 A (42.9% reduction), while the total harmonic distortion (THD) is reduced from 8.39% to 5.48%, representing a 34.7% improvement. Full article

We investigate the nonrelativistic quantum dynamics of a spinless particle in a screw-type spacetime endowed with two independent twist controls that interpolate between a pure screw dislocation and a homogeneous twist. From the induced spatial metric, we build the covariant Schrödinger operator, separate variables to obtain a single radial eigenproblem, and include a uniform axial magnetic field and an Aharonov–Bohm (AB) flux by minimal coupling. Analytically, we identify a clean separation between a global, AB-like reindexing set by the screw parameter and a local, curvature-driven mixing generated by the distributed twist. We derive the continuity equation and closed expressions for the azimuthal and axial probability currents, establish practical parameter scalings, and recover limiting benchmarks (AB, Landau, and flat space). Numerically, a finite-difference Sturm–Liouville solver (with core excision near the axis and Langer transform) resolves spectra, wave functions, and currents. The results reveal AB periodicity and reindexing with the screw parameter, Landau fan trends, twist-induced level tilts and avoided crossings, and a geometry-induced near-axis backflow of the axial current with negligible weight in cross-section integrals. The framework maps the geometry and fields directly onto measurable spectral shifts, interferometric phases, and persistent-current signals. Full article

The predictive current control for the permanent magnet synchronous machine (PMSM) shows great potential in applications like flywheel energy storage, owing to its fast dynamic response and simple structure. However, under low carrier ratio conditions, conventional deadbeat predictive current control (DPCC) exhibits drawbacks such as significant current prediction error, inaccurate instruction voltage calculation, and severe torque and flux linkage coupling. This paper proposes an improved DPCC method suitable for both high and low carrier ratio operation of the PMSM. First, a modified stator voltage equation is established considering rotor flux orientation error. By treating the dq-coordinates as stationary and accounting for rotor rotation within the control period, a dynamic PMSM model is developed, effectively suppressing cross-axis coupling under low carrier ratios. Simultaneously, a multi-coordinate variable synchronization method is also introduced to eliminate prediction and voltage errors caused by cross-coordinate computation, enabling precise deadbeat control across all carrier ratios. The experimental results demonstrate that the proposed method enhances torque-flux decoupling, improves current prediction and tracking accuracy at low carrier ratios, and offers a reliable solution for dynamic control in flywheel energy storage systems. Full article