This work aimed to study the bubble distribution in a multiphase pump. A Euler-Euler inhomogeneous two-phase flow model coupled with a discrete particle population balance model (PBM) was used to simulate the whole flow channel of a three-stage gas-liquid two-phase centrifugal pump. Comparison of the computational fluid dynamic (CFD) simulation results with experimental data shows that the model can accurately predict the performance of the pump under various operating conditions. In addition, the liquid phase velocity distribution, gas-phase distribution, and pressure distribution of the second stage impeller at a 0.5 span of blade height under three typical working conditions were compared. Results show that the region with high local gas volume fraction (LGVF) mainly appears on the suction surface (SS) of the blade. With the increase in inlet gas volume fraction (IGVF), vortices and low velocity recirculation regions are generated at the impeller outlet and SS of the blade, the area with high LGVF increases, and gas–liquid separation occurs at the SS of the blade. The liquid phase flows out of the impeller at high velocity along the pressure surface of the blade, and the limited pressurization of fluid mainly happens at the impeller outlet. The average bubble size at the impeller outlet is the smallest while that at the impeller inlet is the largest. Under low IGVF conditions, bubbles tend to break into smaller ones, and the broken bubbles mainly concentrate at the blade pressure surface (PS) and the impeller outlet. Bubbles tend to coalesce into larger ones under high IGVF conditions. With the increase in IGVF, the bubble aggregation zone diffuses from the blade SS to the PS.

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For the purpose of improving the transport capability of the mixed transport pump, a new self-made three-stage deep-sea multiphase pump was taken as the research object. Based on the Euler-Euler heterogeneous flow model, liquid (water) and gas (air) are used as the mixed media to study the external characteristics and internal flow identities of the mixed pump under different gas volume fraction (GVF) conditions. According to the simulation results, a local optimal design scheme of the diversion cavity in the dynamic and static connection section is proposed. The numerical results before and after the optimization are compared and analyzed to explore the effect of the diversion cavity optimization on the performance, blade load and internal flow identities of the pump. The results show that the head and efficiency are obviously improved when the inner wall of the diversion cavity is reduced by 4 mm along the radial direction. After optimization, under the condition of 10% gas content, the head and efficiency is increased by 3.73% and 2.91% respectively. Meanwhile, the hydraulic losses of the diversion cavity and diffuser are reduced by 9.11% and 4.32% respectively. The gas distribution in the impeller is improved obviously and the phenomenon of a large amount of gas phase accumulation is eliminated in the channel. In addition, the abnormal pressure load on the blade surface is eliminated and the turbulent flow energy intensity is reduced. The average turbulent kinetic energy ( T K ) at i = 0.51 of the first stage impeller passage is reduced by 35%. Finally, the reliability of the numerical method is verified by the experimental results. To sum up, the performance and internal flow identities of the optimized mixed transport pump are improved, which verifies the availability and applicability of the optimization results. This provides a reference for the research and design of a multiphase mixed transport pump in the future.

Integrated systems required for renewable energy use are under development. These systems impose more stringent control requirements. It is quite challenging to control a pumped storage system (PSS), which is a key component of such power systems. Because of the S-characteristic area of the PSS pump turbine, traditional proportional-integral-derivative (PID) control induces considerable speed oscillation under medium and low water heads. PSSs are difficult to model because of their nonlinear characteristics. Therefore, we propose a machine learning (ML)-based model predictive control (MPC) method. The ML algorithm is based on Koopman theory and experimental data that includes PSS state variables, and is used to establish linear relationships between the variables in high-dimensional space. Subsequently, a simple, accurate mathematical PSS model is obtained. This mathematical model is used via the MPC method to obtain the predicted control quantity value quickly and accurately. The feasibility and effectiveness of this method are simulated and tested under various operating conditions. The results demonstrate that the proposed MPC method is feasible. The MPC method can reduce the speed oscillation amplitude and improve the system response speed more effectively than PID control.

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