Double-suction centrifugal pumps are widely used in industrial and agricultural applications since their flow rate is twice that of single-suction pumps with the same impeller diameter. They usually run for longer, which makes them susceptible to cavitation, putting the downstream components at risk. A fast approach to predicting the Net Positive Suction Head required was applied to perform a multi-objective optimization on the double-suction centrifugal pump. An L₃₂ (8⁴) orthogonal array was designed to evaluate 8 geometrical parameters at 4 levels each. A two-layer feedforward neural network and genetic algorithm was applied to solve the multi-objective problem into pareto solutions. The results were validated by numerical simulation and compared to the original design. The suction performance was improved by 7.26%, 3.9%, 4.5% and 3.8% at flow conditions 0.6Q_d, 0.8Q_d, 1.0Q_d and 1.2Q_d respectively. The efficiency increased by 1.53% 1.0Q_d and 1.1% at 0.8Q_d. The streamline on the blade surface was improved and the vapor volume fraction of the optimized impeller was much smaller than that of the original impeller. This study established a fast approach to cavitation optimization and a parametric database for both hub and shroud blade angles for double suction centrifugal pump optimization design.

Double-suction centrifugal pumps form an integral part of power plant systems in maintaining operational stability. However, there has been a common problem of achieving a better cavitation performance over a wider operating range because the traditional approach for impeller design often leads to the design effect not meeting the operational needs at off-design conditions. In addressing the problem, an optimization scheme was designed with the hub and shroud inlet angles of the double-suction impeller to minimize the suction performance at non-design flow conditions. A practical approach that speeds up the cavitation simulation process was applied to solve the experimental design, and a multi-layer feed forward artificial neural network (ANN) was combined with the non-dominated sorting genetic algorithm II to solve the multi-objective problem into three-dimensional (3D) Pareto optimal solutions that meet the optimization objective. At the design point, the suction performance was improved by 6.9%. At non-design flow conditions, the cavitation performance was improved by 3.5% at 1.2Q_d overload condition, 4% at 0.8Q_d, and 5% at 0.6Q_d. Additionally, there was significant reduction in the attached cavity distribution in the impeller and suction domains when the optimized model was compared to the original model at off-design points. Finally, the optimization established a faster method for a three-objective optimization of cavitation performance using ANN and 3D Pareto solutions.

The vertical inline pump is a single-suction single-stage centrifugal pump with a curved inlet pipe before the impeller, which usually causes a significant increase of hydraulic losses in the inline pump. Considering the matching relationship between the inlet pipe and impeller, a multi-objective direct optimization based on the MOPSO of the inlet pipe and impeller was carried out to broaden the efficient operating area of the vertical inline pump. Bezier curves were adopted to control the profiles of the inlet pipe and impeller and 39 coordinates of the control points and the blade number were selected as the optimization variables. The efficiencies of the inline pump at the part-load and nominal conditions were chosen as the objective functions, which were obtained by the automatic simulation program. A dramatic improvement in pump performance was found after optimization. In the set of Pareto solutions, the maximum increases of efficiency at part-load and nominal conditions were 8.06% and 7.33% respectively. It also reported that the inlet pipe with longer horizontal length and lower bend curvature could reduce the hydraulic losses of the inlet pipe and increase the pump performance.

The axial flow pump is a low head, high discharge pump usually applicable in drainage and irrigation facilities. A certain gap should be reserved between the impeller blade root and the impeller hub to ensure the blade adjustability to broaden the high-efficiency area. The pressure difference between its blade surface induces leakage flow in the root clearance region, which decreases hydraulic performance and operational stability. Therefore, this study was carried out to investigate the effect of root clearance on mechanical energy dissipation using numerical simulation and entropy production methods. The numerical model was validated with an external characteristics test, and unsteady flow simulations were conducted on the axial flow pump under four different root clearance radii. The maximum reductions of 15.5% and 6.8% for head and hydraulic efficiency are obtained for the largest root clearance of 8 mm, respectively. The dissipation based on entropy theory consists of indirect dissipation and neglectable direct dissipation. The leakage flow in the root clearance led to the distortion of the impeller’s flow pattern, and the indirect dissipation rate and overall dissipation of the impeller increased with increasing root clearance radius. The inflow pattern in the diffuser was also distorted by leakage flow. The diffuser’s overall dissipation, indirect dissipation rate on the blade surface, and indirect dissipation rate near inlet increased with increasing root clearance radius. The research could serve as a theoretical reference for the axial flow pump’s root clearance design for performance improvement and operational stability.

This study proposed a kind of optimization design for a reversible axial-flow pump based on an ordinary one-way pump. Three-dimensional (3D) Reynolds-averaged Navier–Stokes (RANS) equations was used to predict the pump performance, and the optimized design was validated by an external characteristic test. Six main geometry parameters of an impeller and diffuser based on an orthogonal experiment were set as design variables. The efficiency and head under forward and reverse design conditions were set as the optimization objective. Based on 120 groups of sample designs obtained from Latin hypercube sampling (LHS), a two-layer artificial neural network (ANN) was used to build a non-linear function with high accuracy between the design variables and optimization objective. The optimized design was obtained from 300 groups of Pareto-optimal solutions using the non-dominated based genetic algorithm (NSGA) for multiobjective optimization. After optimization, there was a slight decrease in the forward pump efficiency and head. The reverse pump efficiency and head on the other hand was largely improved and the high efficiency range was also widened.

The high-pressure multistage centrifugal pump is the main piece of energy-consuming equipment in the reverse osmosis desalination process, and it consumes about 35% of the entire system’s operating cost. The optimization process of multi-stage pumps undoubtedly requires the performance comparison of multiple schemes in order to verify the effectiveness of the optimized design and the optimization method. Therefore, based on ANSYS Workbench and an improved bat algorithm, an intelligent optimization scheme was designed and carried out on a three-stage reverse osmosis desalination high-pressure pump for efficiency improvement by optimizing the matching relationship between the impeller and the guide vane. An external characteristic test was carried out in an open test rig system in order to verify the numerical model. After modifying the positive guide vane structure, the efficiency was improved for both the rated design and the non-design flow conditions without obvious separation and backflow. With the improved bat algorithm, there was a 3.98% increase in the design point efficiency after the final optimization. Under the design conditions, all of the large vortices disappeared after the optimization. The study provides a reference for the optimization design of the impeller–guide vane matching effect in a multistage pump using an improved bat algorithm.