Search Results (758)

Understanding the mechanisms by which sand and dust particles detach from the land surface has always been one of the most fundamental and critical issues in aeolian physics and dust-storm forecasting. In this study, large-eddy simulation (LES) was employed to resolve the near-wall turbulence structures. Turbulent bursting events were identified using the second-quadrant method, and a force-balance equation for dust-particle entrainment was formulated at burst locations to numerically simulate the entrainment process of particles of different sizes under bursting conditions. By integrating the latest observational data on near-wall turbulent coherent structures during dust storms both the accuracy of flow-field simulations and the physical consistency of particle force analyses were enhanced. The results suggest that, within the present idealized force-balance framework, near-wall turbulent bursting can provide aerodynamic forcing that contributes to the entrainment of sand and dust particles over the simulated parameter range. Under the same friction velocity, the mean number of lifted particles first increases and then decreases with particle size, exhibiting a parabolic trend. For particles of the same size, the number of lifted particles increases significantly with friction velocity. Under identical incoming wind speeds, the number flux of lifted particles decreases nonlinearly with increasing particle size, whereas the mass flux continues to rise with both friction velocity and particle size. These findings further confirm the critical contribution of aerodynamic entrainment to aeolian transport and provide numerical support for refining the dual-mechanism theory of sand entrainment. Full article

Skin aging stems from intrinsic decline and external stressors that induce oxidative stress and mitochondrial damage, ultimately lowering cellular energy production and slowing epidermal turnover to cause wrinkles, dryness, and pigment imbalances. While disodium adenosine monophosphate (AMP2Na) is hypothesized to enhance cellular adenosine triphosphate production and restore epidermal metabolism, its broader anti-aging effects have remained underexplored. To address this, a multi-tiered study integrating in vitro, ex vivo, and clinical investigations was conducted. Specifically, a 12-week exploratory clinical trial involving female participants with facial hyperpigmentation (n = 23), alongside a short-term forearm study (n = 22), suggested that the AMP2Na-containing product could reduce wrinkles and hyperpigmentation while safely improving hydration, barrier function, skin lifting, and epidermal turnover with high participant satisfaction. Mechanistically, in vitro assays on human dermal fibroblasts showed that the formulation restored antioxidant enzyme activity and mitigated senescence. Ex vivo UVB-irradiated skin explant models corroborated these findings by revealing reduced melanin levels, preserved collagen and elastin networks, and an upregulation of key structural and barrier-related proteins. Ultimately, by potentially supporting epidermal turnover and restoring barrier function through this biomimetic mechanism, the AMP2Na-containing product might offer a promising option for alleviating wrinkles, dryness, and hyperpigmentation. Future randomized, vehicle-controlled clinical trials and comprehensive laboratory studies are warranted to validate its true potential in skin rejuvenation. Full article

Vertical axis wind turbines (VAWTs) have regained attention for distributed, urban, and floating offshore applications, yet the literature remains fragmented across competing rotor concepts and modelling traditions. This review consolidates the principal archetypes—Savonius, H-Darrieus, troposkein Darrieus, helical Darrieus, and Savonius–Darrieus hybrids—through five governing parameters: drag-versus-lift-driven operating principle, tip speed ratio $\mathsf{\lambda }=\mathsf{\omega }\mathrm{R}/{\mathrm{V}}_{\infty }$ (0.6–1.2 for Savonius; 2.5–5.0 for Darrieus), solidity $\mathsf{\sigma }=\mathrm{N}\mathrm{c}/\mathrm{R}$ (0.1–0.4), chord-based Reynolds number ${\mathrm{R}}_{\mathrm{e}\_\mathrm{c}}$ (${10}^5–{10}^6$), and peak power coefficient ${\mathrm{C}}_{\mathrm{p}\_\mathrm{m}\mathrm{a}\mathrm{x}}$ (0.15–0.25 for Savonius; 0.35–0.45 for optimized H-Darrieus). Off-design performance is dominated by unsteady mechanisms that quasi-steady streamtube models cannot resolve—leading edge vortex shedding, dynamic stall hysteresis, blade–wake interaction, and flow-curvature-induced virtual camber—each examined for its contribution to the instantaneous torque ${\mathrm{C}}_{\mathrm{T}}\left(\mathsf{\theta }\right)$ and the cycle-averaged ${\mathrm{C}}_{\mathrm{p}}$. Turbulence closures are benchmarked against phase-locked PIV and torque measurements: $\mathrm{k}–\mathsf{\omega }\mathrm{S}\mathrm{S}\mathrm{T}$ URANS captures peak-region ${\mathrm{C}}_{\mathrm{p}}$ to within $\pm 5–10\%$ but over-predicts torque below ${\mathsf{\lambda }}_{\mathrm{o}\mathrm{p}\mathrm{t}}$; the $\mathsf{\gamma }–{\mathrm{R}}_{\mathrm{e}\_\mathsf{\theta }}$ transition SST model reduces this error to $\pm 3–5\%$; DES, DDES, and LES reach $\pm 2–3\%$ at one to two orders of magnitude higher cost. Best practice computational fluid dynamics (CFD) guidelines are consolidated: domain extents of 15D upstream, 10D downstream, and 20D lateral; rotating sub-domain ${\mathrm{D}}_{\mathrm{r}\mathrm{o}\mathrm{t}}$ $\approx 1.5\mathrm{D}$; ${\mathrm{y}}^{+}\le 1$; $\Delta \mathsf{\theta }\le 0.1°$; and 20–30 revolutions before sampling. Performance enhancement strategies (variable pitch, guide vanes, helical twist, and hybridization) are reviewed quantitatively, with reported ${\mathrm{C}}_{\mathrm{p}}$ gains of $5–30\%$. Four research priorities are identified: (i) transition-sensitive turbulence closures validated below ${\mathrm{R}}_{\mathrm{e}\_\mathrm{c}}$ = $5\times {10}^5$; (ii) coupled aero-hydro-servo-elastic models for floating offshore VAWTs; (iii) machine-learning-augmented turbulence modelling—including physics-informed neural networks (PINNs) and neural-network-corrected RANS closures—to improve unsteady flow prediction at sub-LES cost; and (iv) integrated aeroacoustic–aeroelastic frameworks for urban and building-integrated deployment. Full article

Raindrop size distribution (DSD) is a crucial parameter for microphysics parameterizations and radar quantitative precipitation estimation (QPE). Using disdrometer and ERA5 reanalysis data collected during the rainy season (July–September 2021) in the Liupan Mountains (LP), this study investigated how the two dominant airflow transport pathway types—the deep warm-moist monsoon (C1) and deep dry-cold continental (C2) types—modulated DSDs in the LP. The results showed that C1 had maritime characteristics, with higher number concentrations and a smaller mass-weighted mean diameter (D_m). C2 showed continental characteristics: low-level evaporation preferentially depleted small drops and increased the contribution of large drops (>2.38 mm), resulting in a larger D_m. Under both types, convective precipitation had broader DSDs than stratiform precipitation. Triggered by orographic lifting, C2 convective precipitation enhanced large-drop growth, making its D_m much larger than that of C1. The Z–R relationships were highly sensitive to airflow transport pathways. Dominated by small drops, C1 yielded a smaller Z–R coefficient A than C2, whereas reflectivity in C2 was more sensitive to the enhanced large-drop tail. These findings provide an observational basis for improving regional radar QPE accuracy, hydrometeorological forecasting, and water-resource assessment over complex terrain. Full article

Achieving climate-neutral aviation requires propulsion systems capable of reducing emissions and noise while maintaining high aerodynamic efficiency. Distributed Electric Propulsion (DEP) represents a promising solution; however, accurately predicting the unsteady aerodynamic interactions between multiple propellers and lifting surfaces remains challenging. This work investigates the aerodynamic performance of two Distributed Propulsion (DP) configurations using FLOWUnsteady, a meshless Computational Fluid Dynamics (CFD) solver based on the reformulated Vortex Particle Method (rVPM) within a Large-Eddy Simulation (LES) framework. The Lagrangian particle formulation eliminates mesh generation and limits numerical dissipation. Two layouts—a twin wingtip-mounted arrangement and a four-propeller configuration including inboard units are analyzed and compared with a clean wing baseline as functions of propeller position, inflow speed (20 and 33 m/s), and angle of attack. Beyond global aerodynamic performance metrics, the rVPM–LES framework provides a time-resolved and spatially resolved characterization of local propeller–wing interference in multi-propulsor configurations, highlighting differences in loading and torque demand between inboard and wingtip propellers that are not typically captured by low- to mid-fidelity modeling approaches. The results show that distributed propulsion increases lift and reduces drag relative to the clean wing by accelerating the local flow, delaying separation, and enhancing wing circulation. Thrust and torque coefficients exhibit a clear dependence on rotational speed and angle of attack: inboard propellers experience stronger aerodynamic interference and higher torque demand, whereas wingtip propellers maintain more uniform loading. These findings confirm the capability of the meshless rVPM approach to accurately and efficiently capture unsteady interactions in distributed propulsion systems, supporting its application to the analysis and design of future DEP aircraft. Full article

Association rule mining is a fundamental data mining technique for uncovering latent relationships among variables in large-scale datasets. However, conventional approaches rely on single-metric filtering strategies, which are insufficient for capturing the inherent multi-criteria nature of rule quality. To address this limitation, this study formulates ARM as a many-objective optimization problem and proposes a hybrid algorithm, NSGA-II/DE-ARM, that simultaneously optimizes four rule-quality measures: support, confidence, lift, and NetConf. The proposed algorithm enhances the NSGA-II framework by integrating binary differential evolution operators, an adaptive operator selection mechanism, lift-weighted tournament selection, and a constraint-domination principle combined with a dynamic minimum support threshold. Its performance was evaluated using two datasets: a SIPRI–World Bank panel dataset consisting of defense industry and macroeconomic indicators covering 46 items over the 2002–2023 period, and the UCI Mushroom benchmark dataset consisting of 118 items. Across 30 independent runs on the SIPRI–World Bank dataset, NSGA-II/DE-ARM outperformed the Apriori baseline in all four metrics (mean lift = 4.748, confidence = 0.853, support = 0.146, NetConf = 0.789), with large effect sizes (Cohen’s d = 1.77–5.77, p < 0.001 in each case). On the Mushroom benchmark dataset, the proposed method also achieved substantial improvements, with Cohen’s d values ranging from 0.93 to 6.16. NSGA-II/DE-ARM generated 68 Pareto-optimal rules in a representative run and achieved the highest hypervolume values on both datasets, with HV = 3.231 for SIPRI–World Bank and HV = 6.262 for Mushroom. These results suggest that NSGA-II/DE-ARM offers decision-makers a broader and more balanced multi-criteria solution set than single-metric filtering approaches. Full article

The highly nonlinear aerodynamic interference in tandem-airfoil configurations significantly hinders the precise exploitation of their aerodynamic potential. To address this issue, this study establishes a high-fidelity computational fluid dynamics benchmark. A high-quality sample set is constructed using Latin hypercube sampling combined with an intra-layer replacement strategy. Subsequently, a Gaussian process surrogate model and Bayesian optimization are employed to maximize the total system lift coefficient across a four-dimensional design space comprising longitudinal and vertical separations, fore airfoil angle of attack, and angle of attack difference. Global sensitivity analysis indicates that longitudinal separation dominates the interference modes. Optimization reveals a distinct mode switching phenomenon using a longitudinal separation of twice the chord length as the critical threshold. In the close-coupled configuration, a negative optimal angle of attack difference enhances the slot effect and upwash induction, thereby delaying rear airfoil stall and achieving synergistic lift enhancement. Conversely, in the distant-coupled configuration, the system transitions to a decoupled compensation mode, where a positive angle of attack difference compensates for the effective angle of attack loss induced by wake downwash. This research elucidates the competitive mechanisms between inter-airfoil slot flow and wake interference, providing a theoretical reference for the aerodynamic layout optimization of tandem-airfoil aircraft. Full article

Natural-language-driven robot navigation in complex indoor environments requires the joint capability of high-fidelity scene representation, structured semantic reasoning, and executable path planning. To address this challenge, this paper proposes HOSG-Nav, a unified framework for natural-language-driven global navigation that integrates open-vocabulary 3D Gaussian scene representation, hierarchical semantic scene graph construction, and large-language-model-driven planning. First, an open-vocabulary 3D Gaussian field is constructed to jointly encode scene geometry, appearance, and semantic information, where compressed CLIP features are lifted into continuous 3D space and depth supervision is introduced to enhance geometric stability and metric-scale consistency. Second, the optimized Gaussian primitives are further abstracted into a semantic scene graph with a region–object hierarchical structure and traversable topological relations to support structured environment understanding. Finally, for natural language instructions, hierarchical semantic parsing is performed with the assistance of a large language model, and executable global navigation paths are generated through cross-modal target retrieval and graph-search-based planning. Experimental results on the Replica dataset demonstrate that HOSG-Nav achieves competitive performance in scene representation, semantic target retrieval, and global navigation, validating the effectiveness of jointly integrating multimodal 3D representation, hierarchical semantic abstraction, and language-guided planning. Full article

This paper proposes a digital twin framework for robust parallel control of the mobile gin pole in ultra-high voltage (UHV) transmission line construction, aiming to improve safety and operational efficiency under uncertain conditions. The new framework integrates kinetic analysis, machine learning models, and multi-objective optimization algorithms to address the challenges of heavy-lifting operations in complex terrains. The method conducts finite-element kinetic analysis based on the actual structure of the mobile gin pole. A Tyrannosaurus Rex Optimization Algorithm (TROA) is employed to enhance the performance of the Extra Randomized Trees (ET) model for predicting key parameters such as maximum axial stress and shear stress. The framework leverages the Non-Dominated Sorting Genetic Algorithm III (NSGA-III) to optimize safety and efficiency metrics by adjusting key control parameters. A digital twin system for the mobile gin pole was constructed to validate the proposed approach. Results indicate that: (1) The proposed prediction model achieved performance improvements with R², RMSE, and MSE of 0.9642, 19.6, and 7.42, respectively. Compared with baseline machine learning models, the proposed model achieved significant improvements of 21.5%, 19.2%, and 5.1% in R², RMSE, and MSE, respectively. (2) Experiments confirm that the proposed model maintains high prediction accuracy under noise interference and missing data scenarios, indicating strong robustness. (3) Under various operation conditions, the method reduces safety risks by up to 32.30% and improves operational efficiency by up to 42.73%. Case studies further verify the effectiveness of the proposed framework, demonstrating superior prediction accuracy, noise resistance, and computational efficiency compared to conventional control methods. The core methodological novelty of this study lies in integrating TROA, ET, NSGA-III, and digital twin technology into a unified framework for mobile gin poles. This framework adopts TROA-ET to convert finite-element-based kinetic analysis into a behavior–mechanics surrogate model. It further embeds the constructed surrogate model into an NSGA-III-driven digital twin parallel control architecture. In this way, the study contributes an integrated and computationally efficient solution for safety–efficiency co-optimization of mobile gin pole operations under uncertainty. Full article

With the steady progress of the global energy transition and the pursuit of “dual carbon” goals, Offshore Carbon Sequestration (OCS) has emerged as a pivotal strategic pathway within Carbon Capture and Storage (CCS) initiatives aimed at mitigating climate warming. Nevertheless, the drilling of OCS injection wells faces severe challenges, including narrow geological pressure windows, high risks of shallow geohazards, stringent environmental protection standards, and prohibitive construction costs. Riserless Mud Recovery (RMR) technology, as a novel and eco-friendly deepwater drilling technique, provides innovative technical support for OCS by establishing a closed-loop seafloor circulation system that achieves dual-gradient pressure control and “near-zero discharge” of drilling fluids. This paper systematically reviews the development history and technical principles of RMR. By integrating the specific requirements of OCS injection well drilling—such as wellbore integrity, environmental protection, and shallow hazard mitigation—the study provides an in-depth analysis of the application potential of RMR in drilling CO₂ injection wells within shallow formations. Furthermore, it demonstrates the engineering feasibility of RMR across technical, environmental, and economic dimensions. Building on this analysis, the paper discusses current technical challenges regarding key equipment research and development, adaptability to complex operating conditions, enhancement of intelligent control systems, and the establishment of technical standards. It also outlines the prospects for the integrated development of RMR with emerging fields, including hydrate-based carbon sequestration, intelligent drilling and completion, and carbon sequestration in far-reaching deep-sea areas. The research indicates that RMR technology can effectively resolve the dual constraints of cost control and environmental protection in OCS drilling. With breakthroughs in critical hardware, such as high-displacement subsea lift pumps, and the deepening of cross-disciplinary integration, RMR is poised to become an essential technical pillar in the field of offshore carbon sequestration. Full article

As a key component of sugarcane harvesting machinery, the spiral sugarcane lifter (SSL) enhances harvesting quality by lifting lodged sugarcane (LSC) into a posture suitable for stalk-base cutting and feeding. To improve the SSL’s lifting performance for LSC, this study developed a rigid–flexible coupling (RFC) simulation model of the sugarcane–SSL interaction and conducted kinematic and force analyses to identify the main shortcomings of the original design. Critical structural and operational parameters affecting lifting performance–including the lifting roller pitch, roller diameter, roller inclination angle, and lifter shoe length—were redesigned using mechanism-based constraints and simulation-assisted evaluation. The optimized SSL exhibited increased lifting speed and stability under low–speed, severe–lodging conditions. Under side-forward lodging (side deflection angle = 30°), the average maximum vertical height of the centroid (VHC) increased by 40.36%, and paired comparisons across three simulated lodging-angle scenarios showed significant improvement. Field tests under severe lodging at 0.55 m/s (≈2 km/h) yielded an average absolute simulation–to–field error of 5.37%. These findings support the effectiveness of the proposed parameter redesign for the tested medium-size harvester, although further validation is required under higher forward speeds, greater biomass throughput, and more variable soil conditions. Full article

Background/Objectives: The patency and anatomical location of the maxillary sinus ostium are critical for preventing postoperative complications in dental implant planning and sinus lift surgeries in the posterior maxilla. Narrowing or obstruction of the ostium carries risks, including the development of acute/chronic sinusitis and bone graft failure after surgery. These risks must be carefully evaluated using preoperative radiographic images. It is time-consuming for physicians to manually perform this process, and details are overlooked due to a lack of clinical experience, which can increase surgical risks. Methods: This study aims to overcome these clinical challenges and improve the reliability of radiographic evaluation. In this study, a hybrid deep learning model is proposed for the automatic detection of the maxillary sinus ostium. The proposed model combines the local feature extraction power of CNN-based models with the global context modeling capabilities of transformer-based models, creating an effective model. Additionally, the gated fusion technique efficiently combines features from various designs, significantly enhancing classification performance. Results: The proposed model was compared with six different ViT and CNN architectures established in the literature. While the highest test accuracy among pre-trained models was 89.36%, the proposed hybrid model achieved 95.03%, demonstrating strong clinical diagnostic performance. Conclusions: Based on the performance metrics obtained, we believe the proposed model can be used to determine the patency of the maxillary sinus ostium. This will lighten the workload for specialists and minimize traditional errors. Full article

Ice accumulation on aircraft surfaces severely affects aerodynamic performance by increasing drag and reducing lift, leading to stall conditions. Conventional thermal and pneumatic anti-/de-icing systems, although widely used, have some disadvantages, including high cost, inefficiency, and environmental unsustainability. Hydrophobic and icephobic coatings have emerged as a promising alternative to reduce ice adhesion and delay ice formation. This paper reviews the use of silane agents in epoxy-based coatings, incorporating functional additives such as natural fibers, quantum dots, and nanoparticles, to enhance hydrophobicity. Results demonstrated that the combination of silanes and functional additives affects surface features and wettability, improving hydrophobicity. These case studies show the potential of this approach in the development of coatings for advanced aircraft ice-protection applications. Full article

Magnetic flux leakage (MFL) testing is a critical non-destructive testing (NDT) method for ensuring the safety of ferromagnetic storage and transportation equipment. However, existing research has predominantly focused on weak or saturated magnetization states, leaving the characteristic laws and physical mechanisms of defect signals under supersaturated magnetization conditions unclear. To address this gap, this paper systematically investigates the MFL signal evolution mechanism and develops a high-precision quantitative identification method for defects under supersaturated magnetization conditions through finite element simulation, theoretical modeling, and experimental validation. First, a three-dimensional (3D) finite element model for MFL testing is established using COMSOL Multiphysics. The regulatory effects of key parameters—sensor lift-off value, defect burial depth, length, and depth—on the peak values and distribution characteristics of axial and radial MFL signals are revealed, a signal peak characterization model for each parameter and their adjusted $R^2$ is obtained via fitting, and the detection capability of the detector for defects with different shapes is simultaneously verified. Furthermore, actual detection is conducted on three crack defects of different sizes, and the analysis results indicate that the characterization models of each parameter obtained from the simulation exhibit high accuracy. The results show that MFL signal intensity under supersaturated magnetization conditions is significantly enhanced compared to that under saturated magnetization conditions. Furthermore, to improve defect length measurement accuracy, a signal correction method based on the midpoint of extreme values of the second derivative of axial signals is proposed. By compensating for peak offsets caused by factors like magnetic field diffusion, this method reduces the maximum defect length identification error from 14.25% (pre-correction) to below 0.3%. This study elucidates the coupling influence mechanism of multi-physical parameters on MFL signals under supersaturated magnetization conditions. The proposed high-precision signal correction method provides a novel theoretical basis and technical approach for the accurate quantification and inversion of defects in complex operating conditions. Full article

Continuous monitoring and nowcasting of tornadic near-storm environments remain challenging, particularly in regions with limited ground-based weather radar coverage. High-spatiotemporal-resolution geostationary satellite remote sensing offers a valuable approach to track the evolution of severe convective storms. Combining 10 min cloud-top brightness temperature (TBB) data from the Himawari-8 satellite and ERA5 reanalysis, this study investigates the atmospheric environments of 177 documented tornadoes in China from 2016 to 2023. Tracking storm convective centers using TBB minima reveals clear regional differences in tornadogenesis paradigms. Southern China tornadoes exhibit a “dynamically driven” pattern within quasi-steady, warm, and moist environments. These environments feature low Lifted Condensation Levels (LCL; ~790 m) and weak Convective Inhibition (CIN). Intense low-level wind shear and storm-relative helicity (SRH) dominate the convective triggering. Northern China tornadoes follow a “coupled thermodynamic-kinematic” paradigm under relatively drier and cooler backgrounds. Their initiation relies on the rapid, synchronized accumulation of Mixed-Layer convective available potential energy (MLCAPE) and deep-layer SRH. Furthermore, intensity-based comparative analysis indicates that significant tornadoes (Enhanced Fujita [EF] scale, EF ≥ 2) are favored by higher MLCAPE, deep-layer shear, and lower LCLs compared to weak ones (EF ≤ 1). Himawari-8 TBB data capture a more rapid pre-storm convective cloud-top cooling for strong tornadoes, with medians reaching −73 °C. This study demonstrates that combining high-frequency satellite observations with reanalysis data provides quantitative precursor signals for regional severe tornado nowcasting. Full article