During the starting up of the pump mode in pump turbines, the axial hydraulic force acting on the runner would develop with the guide vane opening. It causes deformation and stress on the support bracket, main shaft and runner, which influence the operation security. In this case, the axial hydraulic force of the pump turbine is studied during the starting up of pump mode. Its influences on the support bracket and main shaft are investigated in detail. Based on the prediction results of axial hydraulic force, the starting-up process can be divided into “unsteady region” and “Q flat region” with obviously different features. The mechanism is also discussed by analyzing pressure distributions and streamlines. The deformation of the support bracket and main shaft are found to have a relationship with the resultant force on the crown and band. A deflection is found on the deformation of the runner with the nodal diameter as the midline in the later stages of the starting-up process. The reason is discussed according to pressure distributions. The stress concentration of the support bracket is found on the connection between thrust seating and support plates. The stress of the runner is mainly on the connection between the crown and the blade’s leading-edge. This work will provide more useful information and strong references for similar cases. It will also help in the design of pump turbine units with more stabilized systems for reducing over-loaded hydraulic force, and in the solving of problems related to structural characteristics.

As an important type of centrifugal pump, the double-suction pump has been widely used due to its high efficiency region and large flow rate. In the present study, the complex flow in volute of a double-suction centrifugal pump is investigated by numerical simulation using a re-normalization group (RNG) k-ε model with experimental validation. Axial flows are observed in volute near the impeller outlet and compared with four staggered angles. The net area-weighted average axial velocities decrease as the staggered angle increases. The axial flows are mainly caused by the different circumferential pressure distribution at the twin impeller outlet. The dominant frequencies of the axial velocities for different staggered angles are f_BP and its harmonic. The pressure fluctuations in most regions of the volute are obtained by superimposing the pressure generated by the two impellers.

Optimization design of an impeller is critical for the energy performance of a fan. This paper takes the promotion of fan efficiency and pressure rise as the optimization objectives to carry out multiparameter and multiobjective optimization research. Firstly, an experimental test bench is built to measure the energy performance of the original fan and verify the accuracy of the numerical method. Then, the hub outlet angle of impeller β₁, the impeller outlet angle increment Δβ₁, the wrap angle φ, the hub outlet angle of diffuser β₂, and the diffuser outlet angle increment Δβ₂ are set as the optimal parameters to conduct orthogonal optimal design. The results show that the efficiency of the optimal fan increases by 11.71%, and the pressure rise increases by 50.15%. The pressure and velocity distributions in an optimal fan are uniform, the internal flow separation is weakened, and the influence of tip leakage flow is reduced, which makes for the improvement of energy performance for the fan.

Deep learning technique is an effective mean of processing complex data that has emerged in recent years, which has been applied to fault diagnosis of a wide range of equipment. In the present study, three types of deep learning techniques, namely, stacked autoencoder (SAE) network, long short term memory (LSTM) network, and convolutional neural network (CNN) are applied to fault diagnosis of a mixed-flow pump under cavitation conditions. Vibration signals of the mixed-flowed pump are collected from experiment measurements, and then employed as input datasets for deep learning networks. The operation status is clarified into normal, minor cavitation, and severe cavitation conditions according to visualized bubble density. The techniques of FFT and dropout algorithms are also applied to improve diagnosis accuracy. The results show that the diagnosis accuracy based on SAE and LSTM networks is lower than 50%, while is higher than 68% when using CNN. The maximum accuracy can reach 87.2% by mean of a combination of CNN, BN, MLP, and using frequency domain data by FFT as inputs, which validates the feasibility of applying CNN in mixed-flow pumps.

As the use of Darrieus turbines in water is becoming increasingly popular in the field of renewable energy, it is essential to explore and evaluate existing research efforts. The situation of the Darrieus water turbine in water still requires further discussion. This paper aims to provide a comprehensive review of optimization methods for Darrieus water turbines, addressing the challenges associated with their efficiency, start-up, and stability. This work summarizes and evaluates the findings of previous studies, focusing on the features of experimental and numerical methods. Influence of geometric parameters, including height-diameter ratio, solidity, torsional angle, and airfoil are also talked into. The existing research adopts solidity values ranging from 0.1 to 0.4, but the design experience is not as extensive as that of the Darrieus wind turbine. Further discussions are still needed on the optimal power coefficient and tip speed ratio of the Darrieus water turbine. The research with a power coefficient ranging from about zero to above the Betz limit needs further summarization. Various optimization strategies, such as multi-turbine arrangement, coupling with Savonius turbines, and blade pitching, are also discussed. By offering insights into the current state of optimization works for Darrieus water turbines, this review aims to facilitate future research, bridge existing gaps in the field, further enrich the utilization of ocean currents, and improve the structure of renewable energy.