Multiscale wind velocity fluctuation and complex inertial response of sand saltation to wind variations give rise to intermittent and nonuniform sand transport, which greatly challenges the prediction of transport rate under wind conditions around saltation threshold. In this paper, three‐dimensional sand saltation within simulation domain of wind tunnel scale is modeled based on large eddy simulation of turbulent wind. Special attention is paid to the prediction of surface shear stress and consequently aerodynamic entrainment. When the wall stress model considering the influence of large‐scale turbulent structures was employed in case of relatively small resistance level for calculating particle aerodynamic entrainment, the model results predict sand transport below the impact threshold. The sand transport rate in this regime increases exponentially with mean wall stress, in contrast to the standard power law‐like behavior above the impact threshold. Aerodynamic entrainment and strong transport events lag behind high‐speed fluids sweep on a large scale even in wind tunnel, but the spatiotemporal variability of small‐scale sand transport is still unclear and quite complex due to transport inertia.

The impact and lift‐off parameters of particles play a key role in the understanding of the saltation mechanisms in aeolian environment. However, limitations of experimental instruments and numerical simulation may result in unreliable motion parameters near the bed surface. Here, we report on the impact and lift‐off parameters of saltating particles in wind‐blown sand predicted with Reynolds‐averaged Navier–Stokes (RANS) or large eddy simulation (LES) and the three‐dimensional stochastic particle‐bed collision model. Simulation results showed new phenomena, including: (a) a reduced mean impact velocity and an increased probability of lift‐off velocities distribution at left extreme with increasing free stream velocity, and (b) two peaks of the impact angle distribution. These phenomena disappear when the statistical height is increased to five times the particle diameter, suggesting that the near‐wall motions are not negligible. In addition, when turbulence is considered and wind velocity increases, the extreme value of velocity distribution increases, impact angle distribution shows a longer tail, and mean impact and lift‐off parameters increase (except impact velocity). We conclude that new velocity distribution with near‐wall motions and turbulence will significantly improve the prediction of aeolian transport flux.

This study addresses the need for precise analysis and fault prediction in hydropower units by introducing an enhanced framework that integrates the Newton–Raphson-Based Optimizer (NRBO) with K-means clustering and random forest (RF) models. This framework dynamically optimizes both cluster numbers and hyperparameters, yielding significant improvements in clustering accuracy. The findings demonstrate that the NRBO-optimized K-means model achieves precise operational state classification without predefined cluster numbers, thereby enhancing intra-cluster compactness and inter-cluster separation. Experimental validation confirms the effectiveness of the proposed method in complex operational scenarios. Furthermore, the optimized RF model identifies critical features such as Active Power and Guide Vane Opening, which are crucial for risk detection under high-load conditions. This approach provides strong support for advanced state analysis and intelligent management of hydropower units, highlighting substantial potential for real-world applications.

This study develops a complex terrain model using real elevation data to systematically investigate the interactions between a photovoltaic (PV) array and particle transport. The results indicate that the complex terrain creates a highly non-uniform flow field, vertically dividing the average wind speed into four zones: speed reduction, high-speed, low-speed above the panels, and flow recovery, whose boundaries rise with increasing terrain height. Fewer disturbed PV panels result in a faster flow recovery to the logarithmic profile. The complex terrain also forces vortices to form closer to panels, breaks their symmetry, increases their shedding frequency, and enhances the spatial dispersion of turbulent kinetic energy. Both the high-turbulence zone behind panels and convex terrain elevate particle saltation height. Furthermore, over complex terrain, the particle transport flux first increases and then decreases with height, with its peak value decaying downstream. In concave regions, near-wall particle flux remains low, and its peak shifts toward the windward edge. Conversely, convex terrain raises both the flux and its peaks. Particles rebounding from panel surfaces generate a secondary flux peak, with a more obvious suppressive effect by panels over the convex terrain, where the second peak is higher. This investigation provides theoretical and technical support for predicting particle deposition around PV array and optimizing their layout in complex terrain.