The low head pump is widely used in many important water conservation projects for drainage and irrigation purposes. A new type of low head pump, the bidirectional shaft tubular pump, consists of an S-shaped impeller, a straight-guide vane, an inlet section, and an outlet section. As known from the practical project, the distance between the impeller and guide vane of the bidirectional shaft tubular pump greatly affects a pump's performance; however, because of its unique impeller and guide vane structure, the distance between the two cannot be easily determined by general empirical formulas. Therefore, in this research, three-dimensional unsteady numerical simulations were performed for six guide vane positions under positive and negative rotation conditions, and the accuracy of the results was experimentally verified. The entropy production method based on numerical results was used to evaluate the effect of the distance on the internal flow-loss distribution and overall power loss, and as a result, a better understanding of the hydraulic loss mechanisms was obtained. The results show that the distance between the impeller and guide vane of a bidirectional shaft tubular pump can affect the pump's efficiency under a positive rotation condition, and turbulence dissipation is the dominant loss. This study can provide theoretical guidance to improve the hydraulic efficiency of low head pumps.
Axial-flow pump with a two-way passage has been widely employed in irrigation and drainage projects. Because of the shape of the two-way inlet passage, the impeller easily induces vibration due to unstable turbulent flow. This vibration results in structural cracks and even hinders the safe operation of the pump. Deformation and stress distributions in the impeller were calculated using two-way coupled fluid-structure interaction simulations, and a quantitative analysis of blade deformation and stress is carried out to determine the structure critical region. The results show that the values of deformation and stress significantly decrease with an increasing flow rate and a decreasing head, and the maximum total deformation can be found in the impeller rim, while the maximum equivalent stress can be obtained near the impeller hub. The total deformations in the blade rim decrease from blade leading edge to trailing edge, and the equivalent stress in the blade hub initially increases and then declines, and in the end, it rapidly increases from the blade outlet to inlet. These results reveal the deformation and stress in the impeller to ensure reliability and specific theoretical guidance for the structural optimization design of a pump device.
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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