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Research Papers: Fundamental Issues and Canonical Flows

Extending Classical Friction Loss Modeling to Predict the Viscous Performance of Pumping Devices

[+] Author and Article Information
Abhay Patil

Department of Mechanical Engineering,
Texas A&M University,
College Station, TX 77843
e-mail: abhyapatil@tamu.edu

Wenjie Yin

Department of Mechanical Engineering,
Texas A&M University,
College Station, TX 77843
e-mail: yinwenjie@tamu.edu

Rahul Agarwal

Department of Mechanical Engineering,
Texas A&M University,
College Station, TX 77843
e-mail: r9.agarwal24@gmail.com

Adolfo Delgado

Department of Mechanical Engineering,
Texas A&M University,
College Station, TX 77843
e-mail: adelgado@tamu.edu

Gerald Morrison

Department of Mechanical Engineering,
Texas A&M University,
College Station, TX 77843
e-mail: gmorrison@tamu.edu

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received October 28, 2018; final manuscript received March 6, 2019; published online April 15, 2019. Assoc. Editor: Wayne Strasser.

J. Fluids Eng 141(10), 101202 (Apr 15, 2019) (11 pages) Paper No: FE-18-1717; doi: 10.1115/1.4043162 History: Received October 28, 2018; Revised March 06, 2019

The affinity law modified for viscosity effects is further extended to include the power input and efficiency. The power input and efficiency data generated using computational fluid dynamics (CFD) are utilized to represent dimensionless power coefficient and efficiency for the pump under consideration. The goal of modifying the affinity laws for power input is achieved by developing a new relationship where the power coefficient is modified by multiplying it by rotational Reynolds number raised to a power Π*RewPat. This new relationship is then represented as a function of a modified flow coefficient ф*RewMo. All the data collapse onto a single curve for varying values of the exponents Morrison number (Mo) and Patil number (Pat). Pat is further characterized as a function of flow regime and specific speed. The method also holds true for efficiency prediction, however, with different values of Mo and Pat. The proposed method is validated by using data collected from published literature.

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References

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Gülich, J. F. , 2003, “ Effect of Reynolds Number and Surface Roughness on the Efficiency of Centrifugal Pump,” ASME J. Fluids Eng., 125(4), pp. 670–679. [CrossRef]
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Wen-Guang, L. , 2013, “ Model of Flow in the Side Chambers of an Industrial Centrifugal Pump for Delivering Viscous Oil,” ASME J. Fluids Eng., 135, p. 051201. [CrossRef]
El-Naggar, M. A. , 2013, “ A One-Dimensional Flow Analysis for the Prediction of Centrifugal Pump Performance Characteristics,” Int. J. Rotating Mach., 2013, p. 1. [CrossRef]
Hydraulic Institute, 2010, “ Effects of Liquid Viscosity on Rotodynamic (Centrifugal and Vertical) Pump Performance,” Hydraulic Institute, Parsippany, NJ, Standard No. ANSI/HI 9.6.7-2010.
Morrison, G. , Yin, W. , Agarwal, R. , and Patil, A. , 2018, “ Development of Modified Affinity Law for Centrifugal Pump to Predict the Effect of Viscosity,” ASME J. Energy Resour. Technol., 140(9), p. 092005. [CrossRef]
Patil, A. , and Morrison, G. , 2018, “ Affinity Law Modified to Predict the Pump Performance for Different Viscosities Using the Morrison Number,” ASME J. Fluids Eng., 141(2), p. 021203. [CrossRef]
Le Fur , Brigitte, M. , and Cerru, F. , 2015, “ High Viscosity Test of a Crude Oil Pump,” 44th Turbomachinery and 31st Pump Symposium, Houston, TX.
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Figures

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Fig. 1

(a) Typical electrical submersible pump configuration and (b) hexahedral structural gridding scheme utilized for the pump stage under consideration [14]

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Fig. 2

Pump performance curve comparison based on specific speed [11]

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Fig. 3

Power input coefficient for different fluid viscosities at 3600 rpm

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Fig. 4

Power input coefficient as a function of new ϕ.Rew−Mo with varying values of the Morrison number

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Fig. 5

Application of proposed correlation to develop the affinity laws to predict the input power for different fluid viscosities

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Fig. 6

Variation in the Morrison number and Patil number as a function of Rew

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Fig. 7

(a) Power coefficient versus flow coefficient using data from Ref. [16] and (b) plot using proposed correlation to include the effect of viscosity

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Fig. 8

Variation in the Morrison number and Patil number as a function of Rew for the data collected from Ref. [16] (Ns: 2400)

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Fig. 9

Prediction of power input using obtained values of Mo and Pat and comparison with the ANSI/HI 9.6.7 (2010) data extracted from Le Fur et al. [16]

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Fig. 10

(a) Power coefficient versus flow coefficient for data collected from Ref. [17] and (b) affinity law modified to include the effect of viscosity (Ns: 2622)

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Fig. 11

(a) Power coefficient versus flow coefficient for data collected from Ref. [3] and (b) affinity law modified to include the effect of viscosity (Ns: 3200)

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Fig. 12

Patil number as a function of Rew and Ns

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Fig. 13

Prediction of power input and comparison with the measured data extracted from Ref. [3]

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Fig. 14

Head coefficient and efficiency of mixed flow pump based on CFD analysis [13]

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Fig. 15

Application of proposed correlation to develop the affinity laws to predict the input power for different fluid viscosities

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Fig. 16

Variation in Mo and Pat as a function of Rew

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Fig. 17

The affinity laws modified to predict the efficiency for different fluid viscosities applied to pump data (Ns: 1163) from Ref. [17]

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Fig. 18

The affinity laws modified to predict the efficiency for different fluid viscosities applied to data (Ns: 2622) from Ref.[17]

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Fig. 19

The affinity laws modified to predict the efficiency for different fluid viscosities applied to pump data (Ns: 2400) from Ref. [16]

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Fig. 20

Variation in the Pateff as a function of Rew and Ns

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