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Two-stage design optimization of groove flow control technique to improve energy performance of an axial-flow pump

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Abstract

Groove flow control technique is an effective method to improve the stall characteristics of axial-flow pumps. Determining the values of groove parameters reasonably is essential to maximize the energy performance of axial-flow pumps. This paper presents a two-stage optimization framework, which combines the non-dominated sorting genetic algorithm-II (NSGA-II) and modified technique for order preference by similarity to an ideal solution (TOPSIS) based on the Shannon entropy, to explore the optimal design of grooves in axial-flow pumps. The analysis of the Pareto frontier reveals the trade-off relationship between the objectives, and some useful distribution laws of optimal groove parameters are found. Numerical simulation results of the final design show that the energy performance under stall conditions is significantly improved, and there is almost no negative effect on the efficiency under the design condition, which demonstrates the effectiveness of the proposed framework. This study provides a reference for the design optimization of groove flow control technique and other flow control techniques.

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Abbreviations

C a * :

Relative proximities

d i +/d i − :

Euclidean distance

D J :

Groove depth (mm)

D Y :

Impeller diameter (mm)

e a i +1,1 :

Approximate relative error

e j :

Information entropy

f i :

Variable value of the ith mesh case

H :

Head (m)

L G :

Length of inlet cone pipe (mm)

L J :

Groove length (mm)

L JX :

Groove length in inlet cone pipe (mm)

L JZ :

Groove length in inlet extension (mm)

m :

Number of objectives

n :

Number of Pareto solutions

n r :

Rated speed (r/min)

N i :

Mesh number of the ith mesh case

N J :

Groove number

Q :

Flow rate (L/s)

r i +1, i :

Grid refinement factor

R 2 :

Coefficient of determination

S G :

Position of inlet cone pipe (mm)

S J :

Distance between groove head and impeller center (mm)

v ij :

Weighted normalized decision matrix

V j +/V j − :

Positive ideal/negative ideal solutions

V * :

Specific gravity of axial velocity

W j :

Entropy weight

Z 1 :

Number of impeller blade

Z 2 :

Number of guide vane

z ij :

Normalizing decision matrix

α G :

Diffusion angle of inlet cone pipe

a J :

Groove width (mm)

β i :

Regression coefficient

η :

Efficiency

φ :

Efficiency change rate

δ :

Tip clearance depth (mm)

GCI:

Grid convergence index

GFCT:

Groove flow control technique

LINMAP:

Linear programming technique for multidimensional analysis of preference

NPSHR:

Net positive suction head required

NSGA-II:

Non-dominated sorting genetic algorithm-II

RMS:

Root mean square

SST:

Shear-stress transport k–ω model

TOPSIS:

Technique for order preference by similarity to an ideal solution

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant No. 51809081), the Natural Science Foundation of Jiangsu Province (Grant No. BK20201315), the Open Research Subject of Key Laboratory of Fluid and Power Machinery (**hua University), Ministry of Education (Grant No. LTDL2021-002), the Fundamental Research Funds for the Central Universities (Grant No. B200202096), the Fundamental Research Funds for the Central Universities (Grant No. B200202096), the China Postdoctoral Science Foundation (Grant No. 2019M661707), and the Jiangsu Planned Projects for Postdoctoral Research Funds (Grant No. 2019K095).

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Authors and Affiliations

  1. College of Water Conservancy and Hydropower Engineering, Hohai University, Nan**g, 210098, China

    **ghong Li

  2. College of Agricultural Science and Engineering, Hohai University, Nan**g, 210098, China

    Rui Zhang, Hui Xu & Jiangang Feng

  3. Key Laboratory of Fluid and Power Machinery (**hua University), Ministry of Education, Chengdu, 610039, China

    Rui Zhang

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Correspondence to Rui Zhang.

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Li, J., Zhang, R., Xu, H. et al. Two-stage design optimization of groove flow control technique to improve energy performance of an axial-flow pump. J Braz. Soc. Mech. Sci. Eng. 44, 381 (2022). https://doi.org/10.1007/s40430-022-03684-8

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