Research Papers: Fundamental Issues and Canonical Flows

Three-Objective Optimization of a Centrifugal Pump to Reduce Flow Recirculation and Cavitation

[+] Author and Article Information
Hyeon-Seok Shim

Department of Mechanical Engineering,
Inha University,
100 Inha-Ro, Nam-Gu,
Incheon 22212, South Korea
e-mail: shs_8341@inha.edu

Kwang-Yong Kim

Fellow ASME
Department of Mechanical Engineering,
Inha University,
100 Inha-Ro, Nam-Gu,
Incheon 22212, South Korea
e-mail: kykim@inha.ac.kr

Young-Seok Choi

Thermal & Fluid System R&BD Group,
Korea Institute of Industrial Technology,
89 Yangdaegiro-gil, Seobuk-gu,
Cheonan-si 331-822, South Korea
e-mail: yschoi@kitech.re.kr

1Corresponding author.

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received October 19, 2017; final manuscript received February 18, 2018; published online April 19, 2018. Assoc. Editor: Matevz Dular.

J. Fluids Eng 140(9), 091202 (Apr 19, 2018) (14 pages) Paper No: FE-17-1674; doi: 10.1115/1.4039511 History: Received October 19, 2017; Revised February 18, 2018

This work presents a three-objective design optimization of a centrifugal pump impeller to reduce flow recirculation and cavitation using three-dimensional (3D) Reynolds-averaged Navier–Stokes equations. A cavitation model was used to simulate the multiphase cavitating flow inside the centrifugal pump. The numerical results were validated by comparing them with experimental data for the total head coefficient and critical cavitation number. To achieve the optimization goals, blockage at 50% of the design flow rate, hydraulic efficiency at the design flow rate, and critical cavitation number for a head-drop of 3% at 125% of the design flow rate were selected as the objective functions. Based on the results of the elementary effect (EE) method, the design variables selected were the axial length of the blade, the control point for the meridional profile of the shroud, the inlet radius of the blade hub, and the incidence angle of tip of the blade. Kriging models were constructed to approximate the objective functions in the design space using the objective function values calculated at the design points selected by Latin hypercube sampling (LHS). Pareto-optimal solutions were obtained using a multi-objective genetic algorithm (MOGA). Six representative Pareto-optimal designs (POD) were analyzed to evaluate the optimization results. The PODs showed large improvements in the objective functions compared to the baseline design. Thus, both the hydraulic performance and the reliability of the centrifugal pump were improved by the optimization.

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Grahic Jump Location
Fig. 1

Schematic of the centrifugal pump model and geometric parameters: (a) meridional view defining CPs, CPh, ZE, and r1h, (b) definitions of θ1 and θ2, and (c) β distributions along normalized meridional location defining kh and ks

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Fig. 2

Computational domain

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Fig. 3

Example of grid system

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Fig. 5

Schematic of suction recirculating flow and definition of critical radius (rc)

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Fig. 4

Multi-objective optimization procedure

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Fig. 12

Performance curves at noncavitating condition: (a) η and (b) Ψ

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Fig. 13

Suction performance curves at cavitating condition

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Fig. 6

Validation of numerical results using experimental data [29] for total head coefficient (Ψ) and critical cavitation number for head-drop of 3% (σ3)

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Fig. 7

Plots of S versus μ* of objective functions for geometric parameters: (a) FB, (b) Fη, and (c) Fσ

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Fig. 8

Pareto-optimal solutions and representative PODs in 3D functional space

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Fig. 9

Projected Pareto-optimal solutions and representative PODs in two-dimensional functional space: (a) FB-Fη, (b) Fη-Fσ, and (c) FB-Fσ

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Fig. 10

Geometric configurations of baseline design and POD A, E, and F: (a) meridional view and (b) β distributions along normalized meridional location

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Fig. 11

Relationships between normalized design variables and objective functions for Pareto-optimal solutions: (a) FB, (b) Fη, and (c) Fσ

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Fig. 14

Normalized pitchwise averaged velocity distributions at z/D0s = 0: (a) axial velocity and (b) circumferential velocity

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Fig. 15

Distributions of normalized critical radius upstream of impeller at Φ/ΦD = 0.5

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Fig. 16

Normalized relative radial and circumferential velocity contours at exit of impeller at Φ/ΦD = 1.0: (a) baseline design, (b) POD A, (c) POD E, and (d) POD F

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Fig. 17

Progressions of cavitation with decreasing cavitation number at Φ/ΦD = 1.25 (The iso-surfaces represent a vapor volume fraction of 0.1.)




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