Tip leakage flow is one of the significant factors influencing the internal flow stability of mixed-flow pumps, and in severe cases, it can lead to channel blockage and energy loss. In order to gain a deeper understanding of the energy dissipation mechanism induced by tip leakage vortex cavitation, this study is based on the Wray–Agarwal (WA) turbulence model and the homogeneous flow model, investigating the cavitation flow characteristics of mixed-flow pumps. Additionally, the entropy production theory is employed to evaluate the energy losses within the mixed-flow pump and analyze the components of energy loss in the impeller and guide vanes. The research results reveal that with increasing cavitation intensity, the low-pressure region at the leading edge of the blade extends toward the trailing edge, influencing the static pressure distribution on the blade's pressure side. Leakage flow and the spatial distribution of leakage vortices move closer to the suction side of the blade with increasing cavitation intensity. Cavitation primarily affects the energy losses in the impeller region, with turbulent dissipation being the main source of energy loss. High turbulent dissipation zones are concentrated at the trailing edge of the blade, correlating with recirculation vortices and trailing-edge vortices. This study provides theoretical insights with practical implications for enhancing the cavitation performance of mixed-flow pumps, offering valuable guidance for design and operation.

To study the spatiotemporal evolution process of particle wakes behind the impeller in the centrifugal pump, this paper utilized high-speed photography to capture the particle motion characteristics under different solid-phase particle concentrations (1%, 1.5%, and 2%). First, this paper studies the changes in hydraulic performance of the centrifugal pump under solid–liquid two-phase flow conditions. It then introduces the evolution process of the impeller particle wake, comparing the differences in particle wake evolution under varying solid-phase concentrations. Finally, the impact of the solid-phase concentration on the wear of the volute's partitions is investigated. This study found that as the solid-phase particle concentration increases, the hydraulic performance of the pump gradually declines. Under the design conditions, when the solid-phase concentration increases by 0.5%, the efficiency of the centrifugal pump decreases by 0.56% and 0.35%. There is mutual transport of particles between adjacent wakes, and the movement of particle wakes within the volute passage is not equidistant over time. As the solid-phase particle concentration increases, wake cutting occurs at the volute partitions, and there is a significant solid–liquid separation between the particle wakes. The spatial evolution of the particle wakes is significantly influenced by the solid-phase concentration. Wear at the volute partitions intensifies with increasing solid-phase concentration and is also affected by changes in the particle wakes. The research results provide a basis for further exploration of the solid–liquid two-phase flow dynamics within centrifugal pumps.

Taking the AquaCrop crop model as the research object, considering the complexity and uncertainty of the crop growth process, the crop model can only achieve more accurate simulation on a single point scale. In order to improve the application scale of the crop model, this study inverted the canopy coverage of a tea garden based on UAV multispectral technology, adopted the particle swarm optimization algorithm to assimilate the canopy coverage and crop model, constructed the AquaCrop-PSO assimilation model, and compared the canopy coverage and yield simulation results with the localized model simulation results. It is found that there is a significant regression relationship between all vegetation indices and canopy coverage. Among the single vegetation index regression models, the logarithmic model constructed by OSAVI has the highest inversion accuracy, with an R² of 0.855 and RMSE of 5.75. The tea yield was simulated by the AquaCrop-PSO model and the measured values of R² and RMSE were 0.927 and 0.12, respectively. The canopy coverage R² of each simulated growth period basically exceeded 0.9, and the accuracy of the simulation results was improved by about 19.8% compared with that of the localized model. The results show that the accuracy of crop model simulation can be improved effectively by retrieving crop parameters and assimilating crop models through UAV remote sensing.

Gearbox fault diagnosis under strong-noise conditions remains challenging due to the difficulty of extracting weak fault-related features from noise-dominated vibration signals, inefficient modeling of multi-scale impulsive characteristics under limited computational resources, and degraded diagnostic stability across varying noise levels. To address these issues, this paper proposes a lightweight fault diagnosis model (DSMC-ECA) that integrates an improved multi-scale depthwise separable convolution scheme with efficient channel attention. The proposed model adopts a dual-branch parallel feature extraction architecture: the SMC branch captures local fine-grained impulsive features, while the SMDC branch expands the receptive field via multi-scale separable dilated convolutions to model long-range dependencies. Meanwhile, ECA is embedded into the multi-scale features for channel-wise recalibration, highlighting fault-relevant discriminative information and suppressing noise disturbances. The model contains only 0.204 M parameters and requires 10.037 M FLOPs, achieving a favorable trade-off between performance and efficiency. Experimental results on the XJTU and SEU datasets demonstrate that DSMC-ECA consistently outperforms baseline methods across a wide range of signal-to-noise ratios (from −6 dB to noise-free conditions). Notably, under the most challenging −6 dB setting, it achieves the highest average diagnostic accuracies of 95.11% (XJTU) and 86.84% (SEU).

This research presents the new techniques and practical experiences of using unmanned aerial vehicles (UAVs) precision agriculture mapping. UAV-based remote sensing systems should be cost-effective, fast-producing, have high geometric accuracy, and be simple to operate by local staff. This work aims to: (1) precisely use high-resolution UAV thermal multi-spectral sensors and machine learning approaches to reliably assess crop water status on a field scale; (2) capture on-field images for quantitative study from the multi-spectral sensors; (3) establish workflows for digital agriculture applications; (4) interpret the intelligent irrigation decision model using UAV indices, maps, and multi-source heterogeneous data integration. This research gives us new methods to set an intelligent method for precision agriculture, which greatly improves the level of agricultural intelligence.

In recent years, it has been demonstrated for many two- and three-dimensional external and internal turbulent flows that the one-equation Wray-Agarwal turbulence model can compute the complex turbulent flow fields with high computational accuracy, excellent computational convergence and efficiency. In this paper, Wray-Agarwal (WA) turbulence model is employed as part of a detached eddy simulation (DES) method to predict the performance of a mixed-flow pump. By comparing the computations with the experimental results, the differences and similarities between the WA-DES model and the Shear Stress Transfer (SST) k-ω model in predicting the internal and external flow characteristics of the pump are analyzed. The results show that both the SST k-ω model and the WA-DES model can reasonably predict the performance of the pump between 0.6 Q and 1.2 Q, where Q is the design flow rate; however, they have their own merits and deficiencies in predicting head and efficiency of the pump at low and high flow rates. For the velocity field in the rotor-stator interaction region, the WA-DES model shows better prediction accuracy since it can accurately predict the large-scale recirculating vortex structure at the inlet of the guide vane. The SST k-ω model over-predicts the separated flow region, which leads to the emergence of a small vortex structure before the backflow region of the pump. Although the turbulent eddy viscosity predicted by the WA-DES model is higher than that of the SST k-ω model and there is small difference in the results for the scale of the tip leakage vortex (TLV) between the two models, the overall simulation results of the WA-DES model for the high turbulent viscosity region and the pressure increase in the impeller are consistent with the SST k-ω model results. The results of this paper demonstrate the potential of WA-DES model for prediction of flows in pumps.