Search Results (1,002)

In response to the severe wear of centrifugal pumps, Workbench workflow is utilized to adjust the blade inlet and outlet angles, aiming to reduce the wear of the impeller and volute of the centrifugal pump and optimize the pump’s efficiency and head. Orthogonal experiments are conducted by varying the inlet and outlet angles. The original sample points are expanded and optimized in combination with the support vector machine and grid search. The optimization results indicate that under the condition of spherical particles, the efficiency at the rated operating condition increases by 1.71%, and the head rises by 0.35%. The appropriate eddy currents formed by increasing the impeller inlet angle alleviate the particle deposition phenomenon in the centrifugal pump, resulting in a smoother particle flow. The wear of the centrifugal pump blades decreases from 40.76 × 10⁻⁷ mm to 7.77 × 10⁻⁷ mm. After optimization, the overall entropy generation rate of the volute decreases, while that of the blade suction surface and the impeller outlet area increases. Additionally, through empirical mode decomposition analysis, it is found that the optimized design reduces high–frequency interference and the pulsation amplitude, making the flow field more stable. The frequency distribution also shifts from being dominated by high–frequency components to concentrating energy in the medium- and low-frequency regions. Full article

With rapid development of 5G/6G communication and high-power electronic devices, electromagnetic interference (EMI) shielding textiles are urgently required to mitigate electromagnetic pollution. Traditional metallic shielding suffered from heavy weight, poor corrosion resistance, and secondary electromagnetic reflection, while continuous basalt fibers (CBFs) exhibit excellent mechanical strength, lightweightness, thermal/chemical resistance, and electrical insulation, which makes CBFs ideal substrates for EMI devices. Herein, a multifunctional waterborne polyurethane (WPU) sizing agent (coating emulsion) integrated with Mn-Zn spinel ferrite was developed for in situ lubrication on the as-spun CBFs. The composite sizing agents consisted of a WPU matrix, water-soluble epoxy, mineral oil lubricant, CTAB surfactant, KH-570 coupling agent, and micro-sized Mn-Zn ferrites. Characterizations including particle size distribution, thermogravimetric analysis, water contact angle (WCA), water absorption, FTIR, XRD, and SEM were conducted to verify uniform anchoring of ferrites on the CBF surfaces. Increasing ferrite dosages induced slight particle aggregation, elevated surface hydrophobicity (WCA = 42.4° → 99.43°), and reduced water absorption (65% → 35%), which greatly improved the moisture resistance of the CBFs. The X-band EMI shielding tests revealed that the total shielding effectiveness (SET) of modified CBF fabrics increased from 0.11 dB (pristine fiber without ferrite) to 58.57 dB at a loading of 8.0 g/L ferrite. The absorption loss (SEA) dominated the shielding performance over reflection loss (SER). The low-to-moderate contents (1.5–3.0 g/L) of ferrite achieved ultra-high absorption, while higher ferrite loading (5.0–8.0 g/L) intensified the impedance mismatch and enhanced surface reflection. This work establishes a scalable fabrication of absorption-prioritized lightweight CBF shielding, which provides a feasible pathway for flexible EMI shielding textiles. Full article

The wake effect inside wind farms reduces the inflow wind speed and increases the turbulence intensity of downstream turbines, resulting in power loss and increased fatigue loads. Active yaw control can mitigate wake interference through collaborative optimization of turbine yaw angles. However, most existing methods rely on steady-state wake models, which fail to capture the dynamic delay characteristics of wakes and usually lead to excessive yaw actuation losses. To address these issues, this paper constructs a dynamic wake model suitable for real-time control based on the OFF dynamic wake framework (OnWARDS, FLORIDyn, and FLORIS), adopting an improved three-dimensional analytical wake model at the lowest level. On this basis, a finite control set model predictive control (MPC) active yaw controller is designed. Aiming to maximize power generation and minimize yaw loss, the controller realizes rolling optimization of yaw actions combined with ARIMA-based wind direction prediction and particle swarm optimization. Simulations on the 4 × 4 turbine array of the Horns Rev I wind farm show that the proposed method increases the total power by 2.25%, which is 0.79% higher than that obtained by the deadband controller. It results in lower power loss for upstream turbines and higher power gain for downstream turbines, reduces the total yaw travel by nearly 1000° compared with the deadband controller, and produces smaller power fluctuations under sharply changing wind directions. Full article

Loss-of-excitation faults are a common fault form of pumped storage units that can threaten both unit safety and grid stability. However, the traditional loss-of-excitation protection based on the impedance principle may exhibit delayed operation or even fail to operate in the case of partial loss of excitation or loss of excitation under light-load conditions, and there is a risk of maloperation in the case of system oscillation. Therefore, this paper analyzes the differences in the characteristics of electrical quantities such as voltage, current, active power, reactive power and power angle during the loss of excitation and system oscillation of a pumped storage unit, and proposes a loss-of-excitation index criterion based on the magnitudes and polarities of variations in terminal voltage, reactive power, and power angle, which constitutes a new method of pumped storage unit loss of excitation fault protection. The simulation results show that compared with the traditional impedance principle loss-of-excitation protection, the proposed method reduces the operating time by 58.6–90.0%. It can reliably and quickly detect the loss-of-excitation fault of pumped storage units under various operating conditions, and shows good anti-maloperation ability for non-loss-of-excitation faults and system oscillation. In addition, the proposed protection method can still maintain reliable operation under the condition of noise interference with a signal-to-noise ratio of 20 dB or communication delay of 0.2 s, which verifies its strong engineering practicability and anti-interference ability. Full article

With the rapid advancement of terahertz technology, electromagnetic interference has become a critical issue that compromises device performance, creating an urgent demand for high-performance terahertz absorbers. Three-dimensional (3D) printing, characterized by flexible structural design, monolithic fabrication, and mold-free processing, has emerged as a promising technique for producing terahertz absorbers. In this work, inspired by the structural and functional characteristics of deep-sea sponges, we propose a bioinspired absorber design that integrates a porous topology with 3D printing. By optimizing the rotation angle and the hollowed array, the absorber establishes multiple internal reflection paths, which, combined with the structural matrix and the graphene conductive coating, enable highly efficient dissipation of electromagnetic energy. Experimental results show that the fabricated sample achieves an absorptivity exceeding 99% over the 0.5–2.0 THz frequency range, while also exhibiting wide-angle absorption and polarization-insensitive performance. The influence of pore size and graphene concentration on the absorption properties is systematically revealed. This work further enhances the performance of 3D-printed terahertz absorbers and provides a novel technical pathway for the design and fabrication of high-performance terahertz absorbers. Full article

Overlapping optical cells create geometry-dependent inter-cell interference, while receiver-geometry and channel-estimation errors can reverse the effective non-orthogonal multiple access (NOMA) decoding order and increase successive interference cancelation (SIC) failures. This paper develops a chance-constrained receiver–scheduler co-design framework for a multi-cell NOMA visible-light communication network with an asymmetrically clipped DC-biased optical orthogonal frequency-division multiplexing physical layer. Correlated position, photodetector-orientation, and channel-estimation errors are propagated through nonlinear geometry-based scenarios. For each SIC direction, a joint three-SINR event defines a layer-, resource-, and direction-labeled probabilistic decodability graph. Candidate NOMA and orthogonal modes are screened on optimization scenarios, admitted by independent one-sided confidence bounds, and selected through resource-constrained mixed-integer linear programming. With the matching fixed, hierarchical powers are adapted under empirical conditional-value-at-risk constraints using trust-region sequential quadratic programming. Because candidate-edge certificates need not remain valid after global matching and power redistribution, the frozen complete assignment is independently recertified before held-out testing. Under the specified uncertainty generator, the proposed method maintains selected-pair outage probabilities of approximately $2.7\times {10}^{-3}$–$3.3\times {10}^{-3}$ over the half-power-angle sweep, compared with $0.027$–$0.060$ for nominal-CSI allocation. Additional experiments quantify network-wide outage, model misspecification, unbalanced deployments, feasibility, and computational cost. The results support reliable slow-timescale scheduling under the adopted link and uncertainty models, without implying distribution-free, waveform-level, or real-time guarantees. Full article

Infrared-selective stealth films, which concurrently offer high visible transmittance, suppressed infrared emission, and selective thermal dissipation, have emerged as compelling candidates for infrared protection and thermal-target stealth. However, traditional multilayer architectures are predominantly designed through empirical trial-and-error protocols, which inherently hinder the synergistic optimisation of multiband spectral performance and yield suboptimal parameter-tuning efficiency. To circumvent this bottleneck, we introduce a reinforcement learning (RL)-driven multi-objective optimisation framework that automates the design of composite thin-film configurations. The optimised multilayer film structure consists of TiO₂/ITO/Ag/ZnO/SiO₂, with layer thicknesses of 180, 656, 10, 33.75 and 50 nm, respectively. Spectral characterisation reveals a weighted average visible transmittance of 79.77% over the 0.38–0.78 μm range, alongside blackbody-weighted average emissivities of 33.93%, 72.93%, and 19.94% in the 3–5, 5–8, and 8–14 μm bands, respectively. Consequently, the spectral profile exhibits high visible transparency, deep suppression of emissivity within the atmospheric windows (3–5 and 8–14 μm), and markedly elevated emissivity in the non-atmospheric band (5–8 μm). Analysis of the electromagnetic field distribution and power-loss density along the thickness direction reveals that the energy transmission and dissipation behaviours across distinct bands are synergistically governed by multilayer interference, interfacial multiple reflections, and lossy interlayer coupling mechanisms. Furthermore, angle-resolved infrared-emissivity analysis calibrated against the normal-incidence FDTD spectrum confirms that the structure retains robust polarisation adaptability and pronounced spectral selectivity at incidence angles up to 80°. The above results demonstrate the effectiveness of the reinforcement learning-driven optimisation framework for the automated co-design of multiband spectral responses. Moreover, the uncovered multilayer interference and loss-coupling mechanisms furnish a solid physical foundation for further performance refinement and rational design of transparent stealth coatings. Full article

In this article, a dual-band frequency-selective surface (FSS) is proposed for wireless communication systems operating in the sub-6 GHz 5G band and emerging upper-mid-band frequencies considered for future wireless applications. The proposed FSS exhibits two passbands centered at 4.8 GHz and 6.9 GHz, with corresponding reflection coefficients of $-33$ dB and $-25$ dB, respectively. The associated $-10$ dB impedance bandwidths extend from 4.6–5.0 GHz and 6.6–7.2 GHz, providing efficient transmission within the desired operating bands. Between these passbands, the proposed structure exhibits a band-stop response centered at 5.5 GHz, where the transmission coefficient ($S_{21}$) reaches a minimum value of $-40$ dB over the frequency range of 5.2–5.8 GHz. This intermediate stopband suppresses unwanted signal transmission, particularly within the Wi-Fi band, thereby improving spectral selectivity and reducing potential interference in multi-band wireless communication environments. Furthermore, the proposed single-layer FSS maintains stable transmission and reflection characteristics under different polarization states and oblique incidence angles up to 60°. The measured results show good agreement with the simulated responses, demonstrating the suitability of the proposed design for applications requiring dual-band transmission and intermediate band-stop filtering in next-generation wireless communication systems. Full article

To address ambiguous maneuver boundaries, background interference, and uneven multi-sensor quality in long-duration flight parameter recordings, this paper proposes an adaptive flight maneuver boundary localization algorithm that integrates spectral entropy-weighted multi-channel spectrogram fusion with attitude-constrained structural correction. Multi-channel Short-Time Fourier Transform (STFT) spectrograms are first constructed from flight parameter time series. Spectral entropy (SE) is introduced to quantify the uncertainty of each channel’s time–frequency energy distribution and is combined with the maneuver activation ratio (MAR) and the linear contrast ratio (LCR) to form objective credibility weights, thereby suppressing channels dominated by aerodynamic turbulence and high frequency structural vibration. Normal overload soft gating and logarithmic noise floor subtraction are then applied to obtain an enhanced fused spectrogram, from which candidate intervals are extracted by low band energy thresholding. Finally, roll and pitch angle steady-state priors refine the event structure through local boundary refinement, cross-segment expansion/chain merging, and semantic post-processing, recovering continuous maneuvers fragmented by instantaneous energy valleys. On the held-out test sorties (SE_018–SE_020; 61 annotated intervals), the proposed algorithm achieves Precision, Recall, and F1-scores of 0.967. On the full primary corpus of 20 sorties (461 intervals), used for ablation and sensitivity analyses, the corresponding figures are Precision 0.934, Recall 0.959, and F1 0.946, with start and end boundary mean absolute errors of 1.484 s and 1.471 s. Under the same IoU protocol, consistent superiority is observed against learning-based baselines, and an independent external set of 10 sorties yields F1 = 0.938. The results indicate that entropy-constrained multi-sensor time–frequency fusion mainly improves maneuver/background separability, whereas attitude-constrained structural correction restores the integrity of long continuous maneuvers. Full article

Integrated Sensing and Communication (ISAC) has emerged as a key enabling technology for future sixth-generation (6G) wireless networks by enabling sensing and communication functionalities to share spectrum, hardware resources, and signal processing infrastructure. However, practical ISAC systems are affected by self-interference, mutual interference between sensing and communication signals, and environmental clutter, which jointly degrade communication reliability and sensing performance. To address these challenges, this paper proposes a novel three-slot interference mitigation framework for downlink ISAC systems. Unlike conventional ISAC approaches that perform joint sensing and communication within a single transmission stage, the proposed framework separates directional sensing, parameter acquisition, and interference-aware joint transmission into three coordinated slots, enabling transmit-side pre-cancellation of sensing-induced mutual interference using estimated interference parameters. Furthermore, joint convex optimization-based beamforming is employed to mitigate self-interference through sidelobe minimization and suppress environmental clutter through spatial null steering while maintaining the desired sensing and communication links. Simulation results demonstrate beam steering, parameter recovery, communication performance after interference cancellation, and target range, velocity, and angle estimation using Range-Doppler and Multiple Signal Classification (MUSIC) processing. The proposed framework presents an interference-aware ISAC architecture that combines a three-slot transmission protocol for mitigating sensing-induced mutual interference with joint beamforming for suppressing self-interference and environmental clutter while supporting simultaneous sensing and communication. Full article

The maritime desert boundary layer over Bahrain often supports anomalous tropospheric propagation. This includes super-refraction and surface-based ducting. These conditions increase the risk of transboundary VHF interference across the Arabian Gulf. This study presents a descriptor-based, empirically anchored propagation-sensitivity framework for assessing these propagation risks under data-limited conditions. It provides a practical alternative in regions where long-term real-world propagation measurements are difficult to obtain. Representative regional refractivity profiles are synthesized from localized thermodynamic variables. Long-term observations from Cyprus are used as an empirical benchmark for severe anomalous propagation. The simulation framework combines ITU-R P.452 anomalous-propagation screening, two-dimensional ray tracing, and wide-angle split-step Fourier parabolic-equation (PE) modeling. Validation against 95.5 MHz field-strength measurements from Limassol, Cyprus, collected during anomalous-propagation interference from Middle East transmitters, shows that matched atmospheric profiles can produce path enhancements of up to 11 dB above free-space predictions. The analysis of critical Gulf links identifies severe low-altitude trapping conditions that exceed classical waveguide thresholds. The Doha–Manama and Abu Dhabi–Manama paths show the highest interference potential. The 98.4 MHz channel emerges as the main same-channel coordination concern. The proposed framework provides a physically grounded and meteorologically bounded method for spectrum coordination and interference assessment in coastal desert environments. Full article

The determination of the angle of arrival (AoA) of Ku-band microwave signals is of great significance in satellite communications, spectrum monitoring, and national defense security, which has created an urgent demand for high-precision and interference-resistant passive detection techniques. In this study, we propose a Rydberg-atom-based passive measurement method for estimating the AoA of Ku-band incident signals. Based on well-established quantum optical techniques, the proposed method converts the EIT-AT splitting intervals induced by waves or signals incident from different directions in Rydberg atoms within a single vapor cell into the electric-field intensity at the atomic sensor. An electric-field-intensity–angle response model is then established for AoA estimation of a 13.6 GHz wave or signal. Simulation results demonstrate that the proposed method can determine the incident direction within a certain angular range, with an estimation error of less than 3.8°. Without the need for self-calibration and free from interference caused by actively emitted probing electromagnetic waves or reference electric fields, the proposed approach enables AoA determination of incident waves or signals using a single vapor cell with a length of 5 cm. Full article

The airborne corner reflector (ACR), a novel radar passive jamming device, has attracted increasing attention from researchers worldwide due to its enhanced interference coverage when suspended by a parachute. However, the directional nature of ACRs renders their effectiveness highly sensitive to in-flight attitude dynamics. By analyzing the parachute body and the corner reflector separately, we propose an improved dynamic model to describe the parachute–payload system. Key innovations include the following: (i) by introducing an 11-degree-of-freedom model for motion analysis of the parachute-mounted double-pyramid octahedron structure, the issue of imprecise analysis in previous methods has been overcome; (ii) explicit modeling of tether tension and geometric constraints is undertaken to capture the parachute–payload coupling mechanism. Numerical simulations of the steady-descent phase demonstrate convergence of the payload’s angular rates and Euler angles, and the results show good agreement with full-scale flight test data. The model strikes a favorable balance between computational efficiency and physical fidelity, and is particularly suited for dynamic analysis of non-axisymmetric payloads in parachute descent systems. Full article

Despite the aim of meeting the demand for long-term online monitoring of structural cracks in fields such as infrastructure, rail transit, aerospace and others, traditional detection methods fail to realize passive wireless, flexible conformal and non-contact measurement. This paper proposes a flexible wireless passive crack sensor based on resonant rings. Taking polyimide (PI) as the substrate, the sensor integrates a sensitive interdigital resonant ring structure. Variations in crack width disturb the electromagnetic field, which further leads to a resonant frequency shift to realize crack width detection. The sensing mechanism is elaborated based on microwave resonance and equivalent circuit theories. Structural optimization and crack width sensitivity analysis are carried out via electromagnetic simulation. Samples are fabricated by flexible printing technology, and a test platform is established. Experiments reveal that the sensor achieves excellent linearity within the crack width range of 0~2.5 mm, with the resonant frequency decreasing monotonically as crack width increases, and a sensitivity of 67.02 MHz/mm. It can operate stably under varying distances, installation angles and bending conditions, demonstrating outstanding flexible conformability. Featuring no power supply requirement, a chip-free design, a simple structure and strong anti-interference capability, the sensor is suitable for long-term crack monitoring of metal structures. Compared with existing studies, the proposed sensor exhibits prominent advantages in flexible adaptability, wireless passive performance and engineering practicability and can provide a novel wireless passive solution for structural health monitoring. Full article

The catenary support component (CSC) is a key part of the electrified railway system, and its operational status directly affects railway operational safety. These components’ images are collected using inspection equipment and detected using computer vision techniques. However, catenary network inspection faces the following issues: (1) due to limitations in the equipment’s shooting angle and changes in viewing distance, the collected images contain multi-scale and multi-class problems, and (2) the railway environment is highly variable, and adverse weather conditions such as fog, rain, and low light affect the imaging devices, leading to degraded image quality. To address these issues, this paper proposes a novel detection framework, FF-DEIM, for detecting catenary support components. First, a dual-channel fusion network (DCFNet) is introduced, which significantly improves image quality by removing foreground interferences such as fog, raindrops, and dynamic blur. Second, a pretraining framework based on contrastive learning, mask image modeling with contrastive learning (MIMCL), is designed to enhance the model’s focus on key regions of the catenary network components, optimizing feature extraction capabilities and improving model convergence speed. Then, a feature-focusing pyramid network (FFPN) is proposed, which uses the focus feature module to fuse cross-level contextual features, enhancing the ability to capture local details and improving the model’s small object detection performance. Finally, a drone-based catenary network image dataset, including scenes with fog, rain, and low light, is constructed, and experiments validate the effectiveness of the proposed method. Full article