To study the effect of sediment on the cavitation in the axial-flow pump, the method of CFD is employed to experiment on the internal flow field of the pump in the case of cavitation in clean water and sandy water separately. The calculation is done with different particle sizes and sediment concentrations. The results show that as the sediment concentration increases, the vortex range and cavitation area of the blade will further increase, and the flow pattern in the impeller becomes more disordered. The mechanism of action of particle size on cavitation is similar to that of sediment concentration. However, cavitation in clean water is quite different from that in sandy water in that the cavitation value and range in sandy water are significantly larger than that in clean water. By contrast, the particle size has little effect on the cavitation value and range. It proves that particle size does not have a great influence on cavitation distribution, but sediment concentration is the main factor affecting the cavitation performance of the pump. Moreover, sediment will disrupt the internal flow pattern of the pump, promote the development of cavitation and further reduce the cavitation performance of the pump.

This article studies the flow trajectories and the wear law of sediment particles in a pump considering multiple variables, such as sediment concentration, particle size, and cavitation stage. In addition, the mechanism of joint effects of cavitation and sediment wear of the axial-flow pump is explored. In this work, the characteristics of cavitation and sediment wear in an axial-flow pump are investigated by the numerical simulation using shear stress transport k–ω turbulence model with experimental validation. The external characteristics of experimental results and numerical simulations are in agreement. The results show that the sediment concentration exerts a profound influence on the vacuole distribution in the pump, while the particle size has little effect on it. Cavitation can increase the volume fraction of the solid, accelerate the wear on the components, and affect the sediment distribution in the impeller. Cavitation and sediment wear are mutually worsening, and their joint effects will form a vicious circle. With the decrease in inlet pressure and the increase in sediment concentration and particle size, the maximum wear rate will gradually increase, which proves that cavitation, sediment concentration, and particle size are the main factors that influence the maximum wear rate.

With increasing demands for energy efficiency and environmental sustainability in pump stations, the development of digital and intelligent pump systems has become crucial. In this study, we focus on three speed ratios of pumps and propose a classification algorithm for pump performance curve fitting using the polyfit function based on the least squares principle. Software programs were developed to model and analyze pump performance curves. Additionally, the impact of seal ring clearance on pump performance and energy consumption was analyzed for pumps with specific speeds of 96.2, 185.5, and 493.3. External characteristic tests were conducted to evaluate these effects. When the clearance value of the sealing ring increases, the head and efficiency of the submersible sewage pump gradually decrease, while the shaft power of the pump presents different variation trends with an increase in the specific speed of the pump. When the specific speed is low–medium, the shaft power of the pump gradually increases. However, when the specific speed is relatively high (n_s > 300), the shaft power initially decreases, then increases, and finally decreases again. For intelligent monitoring, key variables and auxiliary power parameters were identified and mathematical models (Q-P and Q-H) were established as the basis for a soft measurement system. The results show that the Q-H, Q-P, and Q-η curves are generally fitted with polynomial curves of the third~fourth order. The maximum polynomial degree without inflection points is 3, while the maximum number of inflection points is 4, the two inflection points are fitted by segmentation. The pump monitoring system can integrates with an industrial cloud platform, enabling real-time parameter display, control, alarm of functions, recording and analyzing the historical data and operation trends. This research is significant for safe operation monitoring and energy conservation in pump systems.