Search Results (751)

Dielectric Barrier Discharge (DBD) plasma actuators have shown considerable potential for aerodynamic flow control over fixed wings and helicopter rotors. However, their application to small unmanned aerial vehicle (UAV) propellers operating at high rotational speeds remains largely unexplored. This study experimentally investigates the effectiveness of leading-edge AC-DBD plasma actuators in improving the aerodynamic performance of rotating UAV propellers under hovering conditions. A custom-built experimental test stand was developed to measure thrust, rotational speed, and motor power consumption while supplying high voltage to the rotating blades through high-speed slip rings. A series of 3D-printed propellers with different blade pitch angles was tested at rotational speeds up to 4000 rpm. The results showed negligible performance changes for low-pitch propellers, whereas significant improvements were observed under separated-flow conditions. At nearly constant rotational speed and thrust, plasma actuation reduced the propeller power coefficient by up to 7.66%, resulting in a maximum 9.42% increase in Figure of Merit (FoM). The greatest benefits were obtained for intermediate blade pitch angles, while no measurable improvement was observed under severe separation conditions. These findings demonstrate that plasma actuation is most effective within an intermediate separated-flow regime and highlight its potential as a lightweight active flow-control technology for electrically powered UAVs. Full article

Magnetic particle imaging (MPI), as an emerging radiation-free, high-sensitivity molecular imaging technique, holds broad application prospects in fields such as medical diagnosis, angiography, and targeted drug tracking. However, traditional three-dimensional MPI reconstruction algorithms face a problem in balancing reconstruction speed and image resolution. A hybrid reconstruction algorithm (Full Hybrid) based on spatial density constraints and residual iterative optimization is proposed in this work. This paper simulates Lissajous trajectory scanning and the non-linear response of magnetic particles based on the three-dimensional MPI simulation framework. The proposed hybrid method first utilizes the X-space method to obtain a basic spatial prior, then introduces field-free point (FFP) trajectory density to impose spatial weighting constraints on the reconstructed image. Experimental results demonstrated that this hybrid algorithm performs better in the reconstruction of complex three-dimensional topological structures (an H-shaped phantom). Comprehensive evaluation demonstrated that the reconstructed outputs reach a peak signal-to-noise ratio (PSNR) of 12.85 dB, a structural similarity index measure (SSIM) of 0.7321, and a root mean square error (RMSE) of 0.2278. Ablation experiments and comparison experiments further reinforced the advantages of the proposed method. These results demonstrate the numerical feasibility of the proposed reconstruction method for a three-dimensional phantom and provide a basis for further evaluation under multiple simulation conditions and real-scanner measurements. Full article

MagnetoRheological Brakes (MRBs) can provide wear-free, electrically controllable braking torque, but repeated high-load braking can cause rapid heat accumulation in the narrow rotor–stator gap and degrade MRF performance. This study evaluates rotor-only, stator-only and combined rotor–stator liquid-cooling configurations using transient 3-D conjugate heat-transfer CFD in ANSYS Fluent 2024 R1 for a UN Regulation No. 13-H-based 10-cycle duty profile (8.5 s acceleration, 20 s constant speed and 2.5 s braking per cycle). The activated MRF is modeled as an incompressible laminar Herschel–Bulkley fluid during braking, while the field-OFF phases use a Newtonian viscosity of 0.114 Pa·s; viscous dissipation and coil volumetric heating are included as internal heat sources. Cooling simulations apply water with a 130 kPa (absolute) inlet pressure and a conservative +20% heat-load margin with adiabatic external boundaries. Baseline uncooled dynamometer data (no integrated cooling) verify the thermal implementation, with a 7.06% underprediction of the measured temperature rise. In the uncooled case, the MRF reaches a temperature of 501 K after ten cycles; rotor-only and stator-only cooling reduce temperatures but do not fully suppress cumulative heating, whereas the combined configuration maintains the MRF below 400 K after ten cycles. These results indicate that cooling both dominant heat paths is required for stable MRB thermal operation under severe repeated braking. Full article

With advances in computer vision and modern surveying technologies, intelligent inspection systems and automatic recognition methods are increasingly used in highway tunnel maintenance. However, existing mobile inspection methods still struggle to balance high-speed operation, fine-crack recognition, and comprehensive assessment of multiple defects. This study proposes an automatic recognition and quantitative assessment method for multiple visible defects in highway tunnels based on a vehicle-mounted multisensor inspection system. The system integrates high-resolution imaging, infrared illumination, 3D laser scanning, mileage positioning, and high-speed data storage, enabling continuous full-section data acquisition at speeds up to 80 km/h. A structural-feature-constrained mileage correction strategy is developed to reduce accumulated localization errors. For crack analysis, a multilevel framework combining two-stage CNN screening, cascaded segmentation, crack trajectory tracking, and subpixel edge extraction is established for crack recognition and 0.1 mm-level width measurement. Water leakage and spalling are extracted through visible–infrared image fusion and adaptive boundary refinement, while cross-sectional deformation is calculated using 3D tunnel axis reconstruction, point-cloud filtering, and cross-section fitting. Field tests and controlled experiments demonstrate that the system can rapidly identify, locate, and quantify multiple tunnel defects, providing a practical reference for intelligent tunnel inspection and maintenance. Full article

Forecasting the deformation and movement of cumulus clouds provides an important basis for ultra-short-term solar irradiance nowcasting in photovoltaic (PV) power generation. Existing methods mainly use two-dimensional (2D) ground-based sky images for forecasting, which have limited ability to represent the three-dimensional (3D) spatial structure of cumulus clouds and the influence of wind on cloud motion. In this study, we propose a wind-aware ultra-short-term spatiotemporal forecasting model for 3D cumulus clouds, termed three-dimensional Cloud Long Short-Term Memory with Wind Gate Recurrent Unit (3dCLSTM + WindGRU). The model uses 3dCLSTM to learn the spatial structure and temporal evolution of 3D voxel cumulus cloud sequences, and embeds a WindGRU unit between 3dCLSTM layers to introduce wind speed and wind direction information for wind-driven transient motion modeling. Experiments were conducted on 1-min and 10-min 3D cumulus cloud datasets reconstructed from ground-based sky image datasets collected at sites in California and Colorado, USA. All voxel sequences were resampled to 64 × 64 × 64, with five-step prediction for the 1-min dataset and three-step prediction for the 10-min dataset. The results show that 3dCLSTM achieved a structural similarity index measure (SSIM) of 0.7913 on the 1-min dataset, while 3dCLSTM + WindGRU achieved the best performance on the 10-min dataset, with an SSIM of 0.3512 and a peak signal-to-noise ratio (PSNR) of 18.3625. Compared with 3dCLSTM, introducing WindGRU improved the SSIM by 4.8% on the 10-min dataset, with a more evident improvement under relatively high wind-speed conditions. These results indicate that wind-aware volumetric spatiotemporal modeling can support ultra-short-term 3D cumulus cloud forecasting and provide a useful technical basis for solar irradiance nowcasting. Full article

To address the problems in high-speed railway communication, such as large signal penetration loss through carriages, difficulty in long-distance strip coverage, and limited coverage range of traditional base station antennas, this paper designs a cylindrical Luneburg lens antenna operating at the 1800/FA frequency bands. A dual-polarized feed antenna with a dipole structure is designed, loaded with X-shaped metal strips for out-of-band suppression, and integrated with a four-layer dielectric stratified cylindrical Luneburg lens, which uses its graded permittivity distribution to achieve beam focusing, enhance gain, narrow the horizontal beamwidth, and maintain a wide vertical beamwidth. Simulation results show that the lens can stably improve the gain by about 5 dBi; measured results indicate that the antenna has port isolation higher than 35 dB, good impedance matching, and measured gain of 12.4–13.3 dBi within the 1.7–2.1 GHz band, which is highly consistent with the simulation. This antenna can effectively adapt to the long-distance strip coverage scenario along high-speed railways, reduce the base station deployment density, and provide an engineering solution for the optimization of high-speed railway communication coverage. Full article

The objective of this study is to recover the transient out-of-plane displacement field of clamped circular plates subjected to blast loading using a single high-speed camera, as a low-cost alternative to stereo Digital Image Correlation (DIC) for the specific class of axisymmetrical structural responses of circular plates. The dynamic response of thin metal plates to blast loading is a fundamental problem in protective structural design, traditionally investigated through DIC. Although it provides full-field displacement measurements with high spatial resolution, it requires stereo camera arrangements, controlled illumination, speckle pattern preparation, and elaborate calibration procedures that significantly increase experimental cost and complexity. This study introduces a monocular optical method applicable to axisymmetrically defined material testing applications, such as the response of circularly supported isotropic plates under a uniform impulsive load, to recover the transient out-of-plane displacement field without using DIC. Clamped circular aluminum plates are subjected to blast loading generated by PG-3 charges of variable mass detonated at the closed end of a shock tube, with the exposed face matching the tube cross-section so as to enforce axisymmetric pressure load. A diametral reference line marked on the rear face of each specimen was recorded by a single high-speed camera, and a perspective correction derived from the axisymmetric deformed geometry was then applied to reconstruct the time-resolved displacement profile along the diameter. The permanent post-test deformed shape of each plate was subsequently digitized through 3D scanning and used as ground truth to validate the optical reconstruction. The reconstructed profiles closely matched the scans: for the conventional responses the root-mean-square error was 1.251 mm with a normalized mean residual of 6.57% (Case A) and 1.793 mm (9.20%, Case B), while for the anomalous counterintuitive response it was 1.043 mm (14.93%, Case C). Symmetry can thus be exploited as an active measurement principle to obtain quantitative blast-response data with substantially reduced experimental burden and without specialized stereo-optical instrumentation. Full article

Background: Static (at-rest) and dynamic (expression-linked) wrinkles are complementary hallmarks of facial aging. While static wrinkles are widely studied, the objective quantification of dynamic wrinkles during active facial movement remains a novel and underexplored frontier. Quantifying both phenotypes under real-life product use requires objective, non-invasive endpoints alongside standardized clinical grading. Aim: This study aimed to evaluate the clinical and instrumental efficacy of a multi-active topical cream on static and dynamic wrinkles over 8 weeks of twice-a-day use. Methods: After a 2-week washout, we conducted a monocentric, open-label study on 62 Chinese women (25–55) who used the topical cream twice daily for 8 weeks (per-protocol n = 49; dynamic-wrinkle subset n = 41; dermatologist 0–9 grading at T0/Timm/W4/W8). The instrumental endpoints were PRIMOS-CR wrinkle morphometry (forehead, crow’s feet) and periocular high-frequency ultrasound (UC22). Dynamic wrinkles were assessed via high-speed smile imaging (max P10; mean P1–P10). Statistics comprised Wilcoxon’s tests for dermatologist-graded (ordinal) endpoints and repeated-measures ANOVA with Dunnett’s tests for continuous instrumental endpoints (α = 0.05). Results: Improvements were evident at Timm (periorbital elasticity −17.70%, global-face elasticity −15.23%, firmness −19.47%, smoothness −20.16%, radiance −25.75%; all p < 0.001). By Week 8, dermatologist-graded wrinkles generally decreased: crow’s feet −26.89%, under-eye −33.74%, glabellar −35.30%, forehead −34.69% (all p < 0.001). PRIMOS showed reductions in wrinkle area/length (forehead area −8.69%, length −12.05%; crow’s feet area −8.70%, length −16.03%; all p < 0.001). Ultrasound indicated increased periocular epidermal thickness (+26.57%) and density (+12.69%) (both p = 0.005). Dynamic-wrinkle grades improved during smiling (under-eye: max −12.64%, mean −15.74%; crow’s feet: max −15.97%, mean −16.89%; all p < 0.001), with reductions across P1–P10. Conclusions: In real-life, with twice-daily use, the multi-active cream demonstrated significant within-subject improvements in both static and dynamic (expression-linked) wrinkles, as supported by dermatologist grading, PRIMOS 3D wrinkle morphometry, and periocular high-frequency ultrasound. Full article

This study explored a spatio-temporal deep learning approach for optical soft sensing of combustion emissions in a coal–biomass co-firing scenario. High-speed RGB flame sequences from a 0.5 MW test rig co-firing hard coal with 10% straw were synchronized with extractive measurements of O₂, CO₂, and NO. These sequences were used to train three shallow 3D CNNs and three 3D ResNet-50 architectures with squeeze-and-excitation attention. The proposed 3D CNN/ResNet models performed simultaneous regression of all three gas species from flame image volumes. The best configuration achieves R² values of 0.975, 0.987, and 0.980, accompanied by mean absolute errors of 0.23% by volume, 13.15 mg/m³, and 0.19% by volume for O₂, NO, and CO₂, respectively, at a resolution of 128 × 96 × 96 pixels. Within the scope of the available dataset, comprising a single measurement run and a single fuel mixture, the results indicate that a comprehensive spatio-temporal analysis of flame images can yield accurate estimates of multiple gas concentrations, thereby providing a promising foundation for the future development of soft optical sensors. At the same time, the study is limited to a single combustion experiment, a single biomass fraction, and a single borescope orientation, and the inference delay and hardware requirements were not quantified; therefore, issues regarding the generalizability of the proposed approach to different conditions and its implementation remain open for further work. Full article

Two-dimensional (2D) material terahertz (THz) detectors offer a promising platform for compact, room-temperature detection, yet their performance is fundamentally constrained by weak absorption in atomically thin layers. Here, we demonstrate a graphene plasmon polariton atomic cavity (PPAC) THz detector in which intrinsic graphene plasmon absorption is enhanced through vertical cavity-assisted field redistribution. By incorporating a metallic back reflector beneath a silicon substrate of designed thickness, a Fabry–Pérot (FP) interference cavity is formed that positions the standing-wave antinode near the graphene plasmonic layer. Electromagnetic simulations reveal that the Fabry–Pérot cavity itself primarily redistributes the vertical electromagnetic field, thereby enhancing the local in-plane driving field responsible for intrinsic graphene plasmon excitation. Experimental measurements at the optimized cavity condition confirm a pronounced increase in plasmon-induced photothermoelectric response, consistent with the predicted absorption enhancement. As a result, the detector exhibits an approximately 30-fold increase in responsivity compared with the corresponding structure without the cavity, while maintaining a fast response time below 130 μs. The detector further enables discrimination of concealed polar and nonpolar liquids through continuous-wave THz imaging at 2.52 THz, achieving a discrimination speed 30-fold faster than that of conventional time-domain spectroscopy. This result highlights the potential of cavity-enhanced intrinsic plasmon absorption for compact, high-sensitivity, and high-speed THz photodetection. Full article

To meet the conflicting demands of real-time simulation and high fidelity for thermal modeling of IGBT modules in digital twin applications, this paper presents a dynamic thermal network parameter updating strategy. A hybrid thermal model is constructed by combining a high-fidelity finite-element-method reference model with a 3-D compact network. Initial thermal resistance and capacitance parameters are obtained via offline calibration and validated against the transient thermal impedance curve. A dynamic identification method based on recursive least squares with precomputed sensitivity matrices is then proposed. It dynamically updates each independent thermal branch using only real-time chip junction temperature measurements. The Vincotech full-bridge IGBT module is used for simulation validation. The proposed method achieves steady-state identification errors of 3.2% for the IGBT chip thermal resistance and 4.5% for the freewheeling diode chip thermal resistance, outperforming particle swarm optimization and dual Kalman filter in both convergence speed and steady-state accuracy. Thus, it satisfies the requirements of real-time tracking and dynamic evolution for thermal models in digital twin systems. Full article

Ceramic additive manufacturing offers strong potential for fabricating geometrically complex and application-specific components, yet achieving reliable dimensional fidelity remains challenging because dimensional deviation is governed by highly coupled material, process, thermal, and environmental factors. To address this problem, this study proposes an uncertainty-aware computational framework for dimensional error prediction in ceramic 3D printing under variable material and process conditions. The contribution is positioned as a system-level integration of established learning, uncertainty estimation, calibration, and reliability-interpretation components within a ceramic additive manufacturing dimensional-error prediction workflow, rather than as a fundamental methodological breakthrough. The validation is conducted using the publicly available Ceramic 3D Printing Process Control Dataset, a 1000-sample tabular dataset, and the resulting findings are therefore interpreted as dataset-specific computational evidence rather than direct proof of industrial deployment readiness. The methodology begins with a structured data-driven preprocessing pipeline that transforms the Ceramic 3D Printing Process Control Dataset into a multi-condition feature space through data cleaning, one-hot material encoding, min–max normalization, and engineered descriptors capturing extrusion–speed balance, thermal gradients, cooling intensity, deposition density, and material-conditioned interactions. A multi-branch deep computational architecture is then developed to encode material, process, thermal-environmental, and engineered-feature streams separately, followed by adaptive cross-condition fusion to learn nonlinear dependencies across ceramic printing regimes. To improve reliability beyond deterministic regression, the framework jointly models aleatoric and epistemic uncertainty and incorporates calibration refinement to align predictive confidence with observed error behavior, thereby enabling preliminary reliability-oriented interpretation of stable and high-risk operating conditions. Experimental results demonstrate that the full model achieves the best overall within-dataset performance, with a test MAE of 0.0118, RMSE of 0.0172, $R^2=0.999$, MAPE of 1.74%, calibration error of 0.003, PICP of 0.996, reliability score of 0.992, and a stable prediction rate of 98.7%. Although these values indicate strong predictive behavior under the current structured dataset, the exceptionally high $R^2$ should be interpreted cautiously because external experimental validation, larger measured datasets, and cross-machine ceramic printing trials are still required. These findings show that the proposed framework provides an effective system-level computational strategy for dataset-specific reliability-aware dimensional quality prediction in ceramic additive manufacturing and offers a preliminary data-driven foundation for uncertainty-aware intelligent process optimization. Full article

High-resolution soil moisture monitoring in a lysimeter requires precise Ground-Penetrating Radar (GPR) systems that can provide clean time-domain data for a Full-Waveform Inversion (FWI) algorithm. Using high-speed Radio Frequency System-on-Module (RFSoM) devices provides flexibility in signal generation. To optimize such a system, an appropriate transmit waveform and processing pipeline need to be selected. This paper presents a performance evaluation of three GPR waveforms—impulse, Stepped-Frequency Continuous Wave (SFCW) and non-linear Frequency-Modulated Continuous Wave (FMCW/chirp)—on the same hardware setup. To ensure a fair comparison, all waveforms were tested under an identical total measurement time. Numerical simulations were performed using an electromagnetic model of the system. Physical validation was conducted in an anechoic chamber using a 4 GS/s RFSoM setup and planar elliptical dipole antennas. Simulations showed that both sinewave-based methods provide better signal-to-noise ratios (SNRs) than the impulse GPR, with the non-linear chirp achieving the best results (20.7 dB improvement compared to impulse). Experimental measurements supported these results, showing better SNR across the frequency band for the SFCW and chirp waveforms. Because of its high SNR and simple hardware implementation, the non-linear chirp was identified as the most suitable waveform for this RFSoM-based GPR system. Full article

High-speed press systems operate under severe dynamic loading conditions, where bushing components are subject to accelerated wear that directly affects system reliability and maintenance cost. Despite extensive studies on bearing wear in automotive and aerospace applications, wear behavior under high-speed press conditions remains insufficiently explored. This study proposes a wear prediction model that integrates experimental measurements with finite element analysis (FEA). A key hypothesis is that bushing wear under high-speed press conditions can be accurately described by an extended Archard wear model incorporating contact pressure distribution and shaft misalignment effects. A controlled experimental setup was developed to replicate real operating conditions. Wear profiles were measured using high-resolution profilometry, while corresponding contact pressure distributions were obtained via 3D FEA simulations. Model parameters were calibrated using a subset of experimental data and validated against independent test cases. The proposed model demonstrates strong predictive capability, achieving an RMSE of 0.98 $\mathsf{\mu }$m and an MAE of 0.57 $\mathsf{\mu }$m across the 30-min calibration cases under the average (AVG) load-cell calibration. The extended formulation captures the asymmetric wear patterns induced by misalignment and resolves the high-pressure peak underestimation observed in the plain Archard baseline. Full article

Real-time telemetry is essential for performance optimization and safety in motorcycle racing, yet commercial solutions remain proprietary, expensive, and poorly extensible. This paper presents the design, implementation, and experimental evaluation of an open-architecture embedded telemetry unit built around the STM32H745 dual-core microcontroller. The system integrates a u-blox ZED-F9P RTK-GNSS receiver, a Bosch BNO085 9-DoF IMU with on-chip sensor fusion, a CAN-FD interface for powertrain data acquisition, and a SIM7600E-H 4G/LTE module for real-time remote streaming, all housed in a 3D-printed vibration-resistant enclosure. The firmware employs deterministic dual-core task partitioning: the Cortex-M7 core handles sensor fusion and CAN-FD at high frequency, while the Cortex-M4 core manages 4G communication and microSD logging. We explicitly delimit the scope of the evidence presented: CAN-FD powertrain acquisition and end-to-end operational reliability are experimentally validated on real circuit data spanning four campaigns, over 100 laps, and 5.8 h of logging—with sustained acquisition of 13 powertrain channels at speeds up to 185 km/h and zero system resets or data-integrity errors. In contrast, RTK positioning accuracy (2.5 cm CEP), sensor-fusion latency (sub-2 ms at the 99th percentile), 4G-uplink reliability, and thermal margins are characterized through manufacturer specifications, Monte Carlo simulation, and analytical models, with a fully instrumented end-to-end measurement campaign identified as the immediate next step. The 50 Hz effective positioning rate combines 25 Hz GNSS with IMU interpolation. With a bill of materials of approximately EUR 265, the platform offers an order-of-magnitude cost reduction over commercial alternatives while providing full openness and extensibility for distributed intelligence applications. Full article