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 0.6Qd, 0.8Qd, 1.0Qd and 1.2Qd respectively. The efficiency increased by 1.53% 1.0Qd and 1.1% at 0.8Qd. 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.
The recent advances in centrifugal pump design do not only require a better suction performance but also there have been attempts to reduce design time at a lower cost. The traditional trial-and-error optimization design method, however, depends on the designer's experience, which requires longer cycles. This is because the computational process of calculating the net positive suction head required (NPSHr) involves several calculation steps and this consumes a lot of computational time. An investigation was therefore carried out to test a novel NPSHr prediction method in a double-suction centrifugal pump using unsteady numerical simulations. In the new approach, a new boundary pair was introduced and an algorithm was used to estimate a good value for a static pressure value that correlates to a 3% drop in pump head to determine the critical cavitation point. Experiments were conducted to validate the hydraulic performance and the cavitation model. The NPSHr and the characteristic “sudden” head-drop were very well predicted by the novel approach in only three simulation steps. The internal flow analysis showed that for 0.6 Q d, the flow around the volute tongue was uneven at NPSH = 10.06 m, inducing flow separation and recirculation at the tongue region. Attached cavities were also observed around the suction ring in the spiral suction domain. The pressure fluctuations were analyzed also and the dominant frequency at the pump outlet and tongue region was the blade passing frequency. Consequently, the novel approach proved very robust and efficient in NPSHr prediction and would be a good alternative to shorten simulation time during cavitation optimization design process in centrifugal pumps.
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 1.2Qd overload condition, 4% at 0.8Qd, and 5% at 0.6Qd. 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.
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.was validated by an experiment. Liu et al. [11] used the optimal Latin hypercube sampling (LHS) method in the multicondition optimization of a mixed-flow pump and the optimization objective was chosen as weighted average efficiency at three flow rates. However, optimized design obtained from DOE is the optimal solution within the discrete design domain. Combination of the approximation model and intelligent algorithm can get the optimal solution within the continuous design domain. An approximation model is therefore being to construct a function between design variables and optimization objective. This function can then be solved by an optimization algorithm to obtain optimal optimized solutions. Pei et al. [12] therefore combined LHS, the artificial neural network (ANN) and modified particle swarm optimization (PSO) to obtain higher centrifugal pump efficiency at three flowrates. Miao et al. [13] applied the combination of neural networks and modified PSO algorithms to improve the pump efficiency and cavitation performance. Shim et al. [14,15] completed the multiobjective optimization based on approximation model and non-dominated based genetic algorithm (NSGA) to improve stability, efficiency and cavitation performance for different types of centrifugal pump. Wang et al. [16,17] used different surrogate models to optimize the impeller and diffuser of a centrifugal pump based on CFD. However, there is not much optimization design on a reversible axial-flow pump.The reversible axial blade pump generally has two-way impeller airfoils, which can be grouped into an arc, S-shape and polynomial curve. The S-shaped impeller can obtain similar pump performance under the forward and reverse condition. In some actual engineering, the pump operation time under the forward condition is much longer than that under the reverse condition. To obtain high pump efficiency under the forward co...
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