Search Results (1,429)

This paper proposes a performance prediction process for a Brushless Direct Current (BLDC) motor used in drone propulsion by considering actual drive current characteristics and mechanical losses. Drone propulsion BLDC motors are required to achieve high power density and high efficiency under limited power supply conditions, and therefore their performance should be predicted under practical operating conditions. However, in an Electronic Speed Controller (ESC)-based drive environment, the current waveform varies depending on the operating condition, and mechanical losses such as windage and bearing losses can affect the estimation of input power and efficiency, particularly in high-speed operation. The proposed process integrates inverter simulation, finite element analysis, and a test-based mechanical loss model. In the proposed process, the current response under BLDC drive conditions is reflected in the electromagnetic analysis, and the input power, output power, and efficiency of the motor are estimated by considering both electromagnetic and mechanical losses. In addition, mechanical losses are separated from load test results and formulated as a speed-dependent loss model for use in the performance prediction process. The validity of the proposed method is examined by comparison with test results obtained under different throttle conditions. The proposed method reduced the input power prediction error from 26.61% to 2.62% and the efficiency prediction error from 36.27%p to 2.55%p, demonstrating its effectiveness for predicting the performance of drone propulsion BLDC motors under practical operating conditions. Full article

A Spiking Neural Network (SNN) is a kind of brain-inspired and event-driven network, which is becoming a promising energy-efficient alternative to Artificial Neural Networks (ANNs). In recent years, SNN methods have been successfully applied in the fields of electromagnetic signal processing and image signal processing, particularly in application scenarios that require low energy consumption. However, the performance of SNNs by direct training is far from satisfactory. In this paper, we study a novel learning method named SAD-SNN (Spatial-Activation Distillation for Spiking Neural Networks), which utilizes the ANN model to guide the SNN model learning. Unlike prior works that rely on element-wise feature alignment approaches, SAD-SNN aligns spatial-activation maps at different resolutions of the teacher and student networks. Specifically, we introduce a direct alignment approach, which defines a spatial-activation loss and normalizes the representation vectors of ANN and SNN, to alleviate the unexpected precision loss. This enables the knowledge of teacher ANNs to be effectively transferred to train student SNNs. On three image classification datasets, our proposed SAD-SNN outperforms other SNN training methods no matter whether homogeneous or heterogeneous teacher ANNs are used. Furthermore, we apply SAD-SNN to the electromagnetic signal detection task, demonstrating strong generalization ability and superior performance. In conclusion, the experimental results on various tasks and SNN architectures demonstrate that our method is a general and effective solution that significantly improves the learning of student SNNs with only two time steps. Full article

Standard pushers for the relativistic equations of motion of a charged particle in an electromagnetic field—Boris, Vay, Higuera–Cary—do not, in general, preserve the full symplectic structure of the underlying Hamiltonian system, while high-order non-symplectic schemes such as Runge–Kutta accumulate secular error over long times. We propose a two-stage, symmetry-preserving framework (SP-PINN) for the 3+1-dimensional relativistic dynamics of a charged particle in a prescribed field, including a focused Gaussian laser pulse, that pairs a physics-informed neural network with an explicit symplectic integrator: the network learns a surrogate relativistic Hamiltonian, while the integrator—which is not itself learned—advances it. In Stage 1, an unsupervised physics-informed neural network learns the surrogate from the covariant equations of motion using a Lorentz-invariant loss that enforces the mass-shell constraint $\mathcal{H}=m{c}^2\gamma$; in Stage 2, the surrogate is advanced with an explicit symplectic map built on Tao’s extended phase space, valid for the non-separable relativistic Hamiltonian. To isolate the geometric integrator from neural-network approximation error, every benchmark figure advances the analytic relativistic Hamiltonian through Stage 2, the learned Stage-1 surrogate being assessed separately. We benchmark against the Boris pusher and Runge–Kutta on three core test problems (adding the Higuera–Cary pusher in the symplecticity diagnostic), supplemented by plane-wave, ensemble, and pulse-family studies, and we measure the first Poincaré–Cartan loop invariant directly as a quantitative diagnostic of symplecticity. The magnetic-field test illustrates the contrast between bounded and secular error growth: Runge–Kutta drifts secularly, the Boris pusher conserves the invariants to machine precision as a volume-preserving gyro-integrator, and the symplectic map keeps the error bounded for all time; on a non-integrable magnetic trap, where no exact volume-preserving rotation exists, the symplectic map alone keeps the energy error bounded. The learned surrogate is the current accuracy bottleneck—not yet competitive with the conventional pushers for the static cases—but for the demanding laser case, a vector-potential light-cone reformulation reduces this surrogate error to $(3.0\pm 0.1)\times {10}^{-4}$ (three seeds) and yields learned trajectories that remain phase-coherent over essentially the whole interaction. The framework targets laser–plasma acceleration, synchrotron-radiation modeling, and particle tracking. Full article

This paper presents the design and experimental characterization of two low-noise amplifiers (LNAs) operating at 5 GHz, implemented in commercial 16 nm and 7 nm FinFET CMOS technologies. Both circuits employ a cascode common-source architecture, but adopt different design approaches. The 7 nm implementation was designed to achieve simultaneous noise and input matching through source degeneration and gate inductive tuning. In contrast, the 16 nm implementation was primarily optimized for the minimum noise figure using an iterative electromagnetic-aware design methodology. The measured results highlight the practical trade-off between theoretical simultaneous noise and impedance matching and the degradation introduced by large integrated passive components in deeply scaled FinFET technologies. While the 7 nm design achieved improved input and output matching around the target frequency, the required inductive network introduced additional passive losses that degraded the measured noise figure. Conversely, the 16 nm implementation achieved a lower measured noise figure through a simplified matching network, at the expense of a reduced input-matching performance. The comparison provides practical design insights into the implementation of RF front-end circuits in advanced FinFET technologies and highlights the impact of passive component losses on the achievable noise performance. Full article

To address high end-winding temperature in permanent magnet synchronous motors under overload, this paper proposes a passive thermal management method using phase change material (PCM) in a capsule tightly contacting the windings. The PCM absorbs latent heat in its phase change range, buffering hotspots and retarding temperature rise. An electromagnetic–thermal-coupled model including loss distribution and PCM nonlinearity is built to analyze thermal characteristics under various overloads and PCMs. Results show that, at 2.0 times rated load, the time to reach insulation limit extends from 2500 s to 3500 s. The PCM benefit decreases with higher overload; an optimal phase change temperature exists, rising with overload. Direct placement improves the heat transfer path and suppresses transient hot-spot temperature, depending on selection. Experiments validate the model. This study provides a feasible cooling structure and theoretical basis for enhancing overload capability and thermal design of permanent magnet motors. Full article

The contactless characterization of dielectric materials at microwave frequencies in terms of permittivity is a long-standing need in materials science and electromagnetic engineering, and free-space techniques are attractive because they require no sample preparation and operate over broadbands. Conventional Nicolson–Ross–Weir inversion and its iterative variants either suffer from the half-wavelength phase-ambiguity problem or require a reliable initial guess to converge; this work introduces a method that avoids both limitations. The permittivity is retrieved from the frequency derivative of the transmission coefficient T, extracted from the measured S-parameters. As the formulation involves only real-valued quantities, it is intrinsically immune to phase ambiguity and needs no initial estimate. Measurements used two patch antennas separated by 15 cm and a Keysight P9375A Vector Network Analyzer, with the derivative computed through a local polynomial fit. After confirming the negligible-loss regime, the real permittivity was extracted for PMMA, Polymethyl methacrylate (PMMA, ε_r ≈ 3.31), dry sand (ε_r ≈ 3.59), and mineral oil (ε_r ≈ 1.85), all of which are consistent with the literature data. Full article

Microwave resonance sensors are promising for dielectric characterization and biochemical detection. However, their sensing performance is constrained by the relatively long wavelength at microwave frequencies and the limited electromagnetic interaction with small-volume samples. In this paper, an exceptional-point (EP)-enhanced chiral spoof localized surface plasmon (SLSP) resonator is proposed for high sensitivity microwave biosensing with sub-microliter sample volumes. By rotating the chiral resonator relative to the microstrip feeding line, two EPs are obtained at α = 49° and α = 210°. The simulated results show that the EP states generate larger frequency splitting than the reference state under both dielectric-constant variation and detection-limit evaluation, confirming the squareroot response to weak perturbations. Experiments further validate the sensing performance using low-loss dielectric materials and high-loss glucose solutions. For dielectric samples with relative permittivities from 2 to 6.15, the frequency splitting increases from 0.287 GHz to 0.359 GHz. For glucose solutions, the frequency splitting decreases from 1.506 GHz to 1.372 GHz as the glucose amount increases from 2.78 nmol to 27.8 nmol. The proposed sensor requires only about 0.5 μL of sample volume and shows higher sensitivity than reported microwave glucose sensors. These results demonstrate that the EP-enhanced chiral SLSP resonator provides a compact and sensitive platform for trace-volume biochemical detection and integrated microwave biosensing. Full article

To establish quantitative criteria for selecting the operating frequency of diode rectifier unit (DRU)-based medium-frequency AC collection and DC transmission systems, this paper proposes a life-cycle frequency-selection method for far-offshore wind power. A power-flow model incorporating wind-turbine Q–f droop control and frequency-dependent submarine-cable parameters is developed to evaluate voltage distribution, reactive-power accumulation, power factor, and steady-state losses. An electromagnetic transient model is then used to quantify energy losses caused by single-phase and three-phase AC-side short-circuit faults. These electrical results are integrated with cable and converter investment costs in a multi-stage life-cycle economic model that accounts for equipment aging and loss growth. For the studied Rudong 500 MW case under the adopted baseline parameters, medium-frequency operation increases submarine-cable reactive-power accumulation and fault losses, while the total life-cycle cost first decreases and then increases with frequency. The minimum cost occurs at 180 Hz. The proposed framework links steady-state performance, transient fault losses, and long-term economics, providing a project-oriented basis for selecting the operating frequency of DRU-based offshore wind transmission systems. Full article

Electromagnetic underwater communications are strongly limited by water conductivity and depth, particularly at ISM frequencies above 900 MHz. Although LoRa technology has been widely adopted in low-power wireless sensor networks, experimental studies of LoRa-based underwater-to-overwater (UW2OW) links at 915 MHz in real freshwater environments remain scarce. This work presents an experimental evaluation of a 915 MHz LoRa UW2OW communication link conducted in three freshwater scenarios with different conductivity conditions: a controlled swimming pool and two natural lakes. The experimental campaign analyzes the influence of water depth and horizontal distance on key performance metrics, including received signal strength indicator (RSSI), signal-to-noise ratio (SNR), packet loss rate, and end-to-end latency. Measurements were carried out at depths between 0.25 m and 0.8 m. Multiple spreading factors were evaluated in the pool scenario, while SF12 was selected for the natural-lake experiments. The results demonstrate that short-range UW2OW communication is feasible, achieving effective distances of up to 13 m at shallow depths. However, increased depth and higher water conductivity lead to significant performance degradation, with packet loss rates exceeding 80% at longer distances. The obtained results provide empirical insights and practical design criteria for short-range freshwater underwater wireless sensor networks based on LoRa technology. Full article

This paper investigates frequency manoeuvring as a method to improve the resilience of unmanned aerial vehicle (UAV) networks operating in contested electromagnetic environments. The study considers scenarios in which the network initially operates on a single channel and is then exposed to intentional jamming or unintentional interference affecting the primary channel, adjacent channels or a wider frequency range. Several response policies are compared, including no channel change, immediate switching after quality degradation is detected, delayed switching after a defined loss-of-connectivity interval, and periodic frequency hopping. In addition to channel switching, the analysis also considers changes in channel bandwidth, comparing narrower channels with lower throughput but potentially higher resistance to interference against wider channels with greater capacity but increased susceptibility to disruption. The evaluation includes the switching cost, which is modelled as temporary packet loss, additional delay and jitter during reconfiguration. Performance is assessed using the packet delivery ratio, latency, jitter, packet loss and communication continuity. The main objective is to identify the interference conditions under which frequency manoeuvring becomes operationally beneficial and to determine which policy offers the best trade-off between resilience and communication performance. In quantitative terms, immediate switching under environmental interference achieved a PDR of 0.961 and a mean latency of 123.6 ms compared with a PDR of 0.946 and a mean latency of 138.7 ms for fixed-channel operation. Manoeuvring gave a substantial 12.2-percentage-point PDR gain under jamming (periodic hopping: 0.780 vs. 0.658) and a 6.7-percentage-point gain under combined interference (0.674 vs. 0.607). These results indicate that manoeuvring is most worthwhile once interference is persistent and channel-focused rather than purely environmental. Full article

The transition toward electric vehicle (EV) architectures exceeding 800 V offers key advantages, including shorter charging times, lower operating currents, and reduced system weight due to smaller conductor cross-sections, all of which enhance overall vehicle performance. However, identifying suitable traction inverter topologies that meet automotive requirements for efficiency, electromagnetic interference (EMI), and reliability remains critical. This study presents a simulation-based converter-level electro-thermal and conducted-EMI benchmark of 2-Level H-Bridge, 3-Level Active Neutral-Point Clamped (ANPC), and 3-Level T-Type inverters under identical output-power operating conditions. The distinguishing feature of this work is the unified evaluation of these topologies under a common external thermal boundary, enabling a consistent comparison of semiconductor losses, junction-temperature behaviour, cooling-burden indicators, conducted-EMI tendencies, and first-order lifetime-oriented thermal indicators. Within this framework, the required effective thermal resistance is used as a cooling-burden indicator, while junction-temperature swing and mean junction temperature are used as relative thermal-stress indicators. Under the considered simplified R–L loading conditions, the results show that multilevel topologies reduce semiconductor losses, peak junction temperature, conducted-EMI excitation, and relative thermal-stress indicators compared with the 2L H-Bridge. These findings are interpreted as comparative topology-level trends under the defined converter-level simulation framework rather than as final vehicle-level EMI compliance or power-module lifetime predictions. Full article

Multidimensional metasurfaces provide a promising platform for terahertz (THz) multifunctional devices used in communication, imaging, and sensing. However, many THz multifunctional devices still rely on metallic or periodic metasurfaces, which may suffer from ohmic loss, unwanted diffraction, channel crosstalk, and energy leakage. To address these limitations, we propose an all-dielectric THz metasurface based on a five-fold rotationally symmetric quasicrystalline aperiodic tiling and verify its performance through full-wave electromagnetic simulations. High-resistivity silicon rectangular pillars are used as anisotropic propagation-phase meta-atoms, enabling independent wavefront encoding for two orthogonal linear polarizations within a single aperture. By mapping the x- and y-polarized phase profiles onto the quasicrystalline lattice, the proposed device realizes polarization-multiplexed bifocal focusing with controllable focal positions. Simulation results show high focusing efficiency, low polarization crosstalk, broadband focusing performance, and robustness under oblique incidence. This work provides a compact all-dielectric route for multifunctional THz wavefront control based on polarization multiplexing and quasicrystalline metasurface design. Full article

We formulate and analyze a 26-dimensional nonlinear dynamical system governing a doubly-fed induction generator (DFIG) wind energy conversion system coupled to an infinite bus through a dynamic transmission line. Seven interacting subsystems—aerodynamics, a two-mass drivetrain, a fourth-order machine, rotor- and grid-side converter controllers, a phase-locked loop, and a pitch regulator—are assembled into a single vector field $\dot{\mathbf{x}}=\mathbf{f}(\mathbf{x},\mathbf{u})$ on ${\mathbb{R}}^{26}$, derived in dimensionless coordinates. Strict positivity of the determinant $\mathrm{\Delta }=L_s{L}_r-L_m^2=\sigma L_s{L}_r$ for every physically admissible machine renders the flux–current map invertible, so the right-hand side is well defined; the nodal Kirchhoff constraint forms a semi-explicit differential-algebraic relation that we eliminate to obtain an explicit ordinary differential equation. The central contribution is a constructive scheme for the equilibria: the 26 stationarity conditions $\mathbf{f}({\mathbf{x}}^{\star },\mathbf{u})=\mathbf{0}$ are solved by an iterative voltage-matching procedure converging to a residual below ${10}^{-11}$ per unit—essentially machine precision—which removes the spurious start-up transients common in reported simulations. Analytically chosen feedback gains induce a hierarchy of well-separated time scales, placing the closed loop in the multiple-time-scale class; the separation is made quantitative through explicit small parameters ${\epsilon }_i$ formed from the ratios of subsystem time constants. Numerical integration of a GE $3.6$ MW configuration confirms the construction: under stationary forcing, the rotor speed stays within $1.32\times {10}^{-5}$ pu of the equilibrium, and under a large-amplitude wind program ($11\to 14\to 9$ m/s) spanning the full operating envelope, it is regulated to within $0.065\%$, while the DC-link voltage deviation remains below $2.4\times {10}^{-5}$ pu and the power balance closes with residual below ${10}^{-3}$ pu, the ≈2% mechanical–electrical gap being the modeled losses. Linearization about the computed equilibrium yields a Jacobian whose spectrum lies entirely in the open left half-plane, establishing local asymptotic stability and exposing the individual electromagnetic, torsional, and control modes. The model furnishes a rigorously initialized, analytically transparent basis for linearization, spectral stability analysis, and bifurcation study. Its practical value is that a consistent equilibrium and a certified spectrum remove the start-up transients and undocumented tuning that otherwise let initialization artifacts masquerade as genuine dynamics, so that the model can serve as a trustworthy building block for weak-grid and wind-farm stability studies. Full article

This study presents the design, manufacturing, and prototype-level evaluation of a high-efficiency interior permanent magnet synchronous motor (IPMSM) developed for compact electric vehicle traction applications. The proposed motor employs a 12-slot/10-pole spoke-type rotor topology and was evaluated in terms of electromagnetic performance, mechanical integrity, and thermal behavior. The slot–pole and winding configuration was assessed as part of the design evaluation, and the manufactured prototype was experimentally tested under different operating conditions. The experimental results were compared with numerical simulations using line-to-line back-EMF, efficiency maps, phase current–torque characteristics, and output power variation. At the nominal operating point of 7000 rpm and 3.5 Nm, the prototype delivered 2.5 kW output power with an experimental efficiency of 90.7%. The deviations between experimental and simulation results were 1.17% for phase current, 0.48% for line-to-line back-EMF, 1.18% for input power, and 1.20% for efficiency. Mechanical static structural finite element analysis indicated a rotor safety factor of 3.61 under the maximum centrifugal loading condition, while the resulting structural deformation remained sufficiently low to avoid adverse effects on air-gap alignment. In addition, the rotor incorporated an adhesive-free, mechanically disassemblable magnet-retention structure, which was mechanically evaluated under centrifugal loading and showed no magnet displacement, structural damage, or bolt-preload loss after testing. Thermal analysis and continuous-load experimental testing showed that the winding temperature remained around 80 °C under passive cooling conditions. Overall, the results demonstrate that the manufactured IPMSM prototype provides consistent electromagnetic performance, adequate mechanical reliability, and thermally safe operation for compact electric vehicle applications. Full article

Flexible metamaterial absorbers have exhibited tremendous potential for applications in intelligent wearable devices and radar stealth protection due to their remarkable electromagnetic response characteristics and mechanical conformal adaptability. However, conventional metamaterial development relies heavily on iterative full-wave simulations, which not only incurs prohibitive computational costs but also hinders the efficient identification of global optima within high-dimensional geometric parameter spaces. To address these challenges, this paper proposes an inverse design framework based on a deep learning-powered adversarial diffusion model. By integrating residual blocks and self-attention mechanisms within the U-Net architecture, the model’s capacity to capture global spectral features is significantly enhanced. Furthermore, the introduction of a discriminator for adversarial fine-tuning optimizes generation quality, resulting in a 22.46% reduction in the target loss function compared with conventional approaches. This method effectively resolves the “one-to-many” inverse mapping challenge between spectral requirements and geometric structures. Experimental results demonstrate that the designed absorber exhibits excellent polarization insensitivity and maintains efficient, stable absorption performance even under large-angle conformal bending. Moreover, a multi-sample collaborative validation strategy is employed to cross-verify measured samples across different frequency bands, establishing the model’s high precision and engineering reliability. Full article