High-efficiency design optimization of a centrifugal pump

Heo, Man-Woong; Ma, Sang-Bum; Shim, Hyeon-Seok; Kim, Kwang‐Yong

doi:10.1007/s12206-016-0803-4

Cited by 29 publications

(18 citation statements)

References 20 publications

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“…The optimization objective can be predicted by this function. Common approximation models include RSM [26], Kriging Model [27], radial basis function (RBF) [28] and ANN [29].…”

Section: Multilayer Artificial Neural Networkmentioning

confidence: 99%

“…In both the original and optimized design, the high velocity was observed near the suction side and the low velocity near the leadging edge due to the presence of the stagnation point. After Figure 12 shows the turbo surface of the impeller with different span, and the span was defined as Equation (28). Span = (r − r h )/(r t − r h ) (28) where, r is the calculated ring radius, r h is the hub radius and r t is the radius of the impeller rim.…”

Section: Analysis Of Inner Flowmentioning

confidence: 99%

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Multiobjective Combination Optimization of an Impeller and Diffuser in a Reversible Axial-Flow Pump Based on a Two-Layer Artificial Neural Network

Meng

Yuan

et al. 2020

Processes

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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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“…The optimization objective can be predicted by this function. Common approximation models include RSM [26], Kriging Model [27], radial basis function (RBF) [28] and ANN [29].…”

Section: Multilayer Artificial Neural Networkmentioning

confidence: 99%

Section: Analysis Of Inner Flowmentioning

confidence: 99%

Multiobjective Combination Optimization of an Impeller and Diffuser in a Reversible Axial-Flow Pump Based on a Two-Layer Artificial Neural Network

Meng

Yuan

et al. 2020

Processes

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show abstract

“…However, all the researches mentioned above were limited to the impeller optimization design. The literatures about optimal design concerning the impeller and diffuser together were rare [8][9] . However, fluid machinery is complex, and the best performance of impeller can't guarantee the best of whole pump.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Pump Hydraulic Performance Based on the Response Surface Method

Xu¹,

Zhong²,

Zhang³

2018

Preprint

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This paper studies the optimization method of pump hydraulic performance based on the response surface method. A parametric model of impeller and diffuser is established. Three-dimensional optimization is carried out on the basis of the initial model obtained by one-dimensional design method. We select the pump hydraulic efficiency and the head as objective function and constraint function. Response surface models are constructed to analyze the relationship between the objectives and the design variables, and the global optimization of hydraulic performance is realized. According to the internal flow characteristics of pump, this paper proposes the strategy of two steps optimization, which aims at meridional plane and blade shape, respectively, to solve the problem of large numbers of design parameters and computational cost. The optimization results show that the hydraulic efficiency of pump increased by 3.7%, and the head is nearly the same.

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“…Number of researchers has studied the application of CFD in fluid mechanics and turbo machinery over the years [1][2][3][4][5][6][7]. However, there are few researchers used this CFD tool to predict the performance of pump impeller.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the progress in computer capability and numerical methods, the design analysis of centrifugal pumps by using 3D-Navier stokes program or CFD software has become easy. The impeller could be redesigned if the final result from the CFD differs from the designed value [1].Number of researchers has studied the application of CFD in fluid mechanics and turbo machinery over the years [1][2][3][4][5][6][7]. However, there are few researchers used this CFD tool to predict the performance of pump impeller.…”

mentioning

confidence: 99%

CFD Analysis of Double Suction Centrifugal Pump with Double Volute

Vyavahare

Rao

Patil³

2017

Period. Polytech. Mech. Eng.

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In this study, Computational Fluid Dynamics (CFD) Analysis is used to investigate the flow in the centrifugal pump impeller using the ANSYS-CFX. Impeller is designed for head of Keywords CFD, vertical pump, head, impeller, volute casing IntroductionPump is designed as per API 610 standards. Modelling of impeller, volute geometry and all pump components is done in software CREO 3.0 by assuming the required duty parameters. Double suction impeller is designed to meet required head and flow at operating speed. Double volute casing is used to transfer fluid from inlet to outlet pipe.Nowadays the use of CFD plays an important role in fluid mechanics. Due to the progress in computer capability and numerical methods, the design analysis of centrifugal pumps by using 3D-Navier stokes program or CFD software has become easy. The impeller could be redesigned if the final result from the CFD differs from the designed value [1].Number of researchers has studied the application of CFD in fluid mechanics and turbo machinery over the years [1][2][3][4][5][6][7]. However, there are few researchers used this CFD tool to predict the performance of pump impeller. Until now, there have been only three works carried out by Rajendran and Purushothaman

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High-efficiency design optimization of a centrifugal pump

Cited by 29 publications

References 20 publications

Multiobjective Combination Optimization of an Impeller and Diffuser in a Reversible Axial-Flow Pump Based on a Two-Layer Artificial Neural Network

Multiobjective Combination Optimization of an Impeller and Diffuser in a Reversible Axial-Flow Pump Based on a Two-Layer Artificial Neural Network

Optimization of Pump Hydraulic Performance Based on the Response Surface Method

CFD Analysis of Double Suction Centrifugal Pump with Double Volute

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