Research Papers: Flows in Complex Systems

Turbulent Drag Reduction by Biopolymers in Large Scale Pipes

[+] Author and Article Information
Marina Campolo

Department of Chemistry,
Physics, and Environment,
University of Udine,
Udine 33100, Italy
e-mail: marina.campolo@uniud.it

Mattia Simeoni

Department of Electrical,
Management, and Mechanical Engineering,
University of Udine,
Udine 33100, Italy
e-mail: mattia.simeoni@uniud.it

Romano Lapasin

Department of Engineering and Architecture,
University of Trieste,
Trieste 34128, Italy
e-mail: romano.lapasin@di3.units.it

Alfredo Soldati

Department of Electrical, Management, and
Mechanical Engineering,
University of Udine;
Centro Internazionale di Scienze
Meccaniche (CISM),
Udine 33100, Italy
e-mail: alfredo.soldati@uniud.it

1Corresponding author.

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received May 8, 2014; final manuscript received October 9, 2014; published online December 3, 2014. Assoc. Editor: Frank C. Visser.

J. Fluids Eng 137(4), 041102 (Apr 01, 2015) (11 pages) Paper No: FE-14-1245; doi: 10.1115/1.4028799 History: Received May 08, 2014; Revised October 09, 2014; Online December 03, 2014

In this work, we describe drag reduction experiments performed in a large diameter pipe (i.d. 100 mm) using a semirigid biopolymer Xanthan Gum (XG). The objective is to build a self-consistent data base which can be used for validation purposes. To aim this, we ran a series of tests measuring friction factor at different XG concentrations (0.01, 0.05, 0.075, 0.1, and 0.2% w/w XG) and at different values of Reynolds number (from 758 to 297,000). For each concentration, we obtain also the rheological characterization of the test fluid. Our data is in excellent agreement with data collected in a different industrial scale test rig. The data is used to validate design equations available from the literature. Our data compare well with data gathered in small scale rigs and scaled up using empirically based design equations and with data collected for pipes having other than round cross section. Our data confirm the validity of a design equation inferred from direct numerical simulation (DNS) which was recently proposed to predict the friction factor. We show that scaling procedures based on this last equation can assist the design of piping systems in which polymer drag reduction can be exploited in a cost effective way.

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Fig. 1

Experimental flow loop: pipe diameter is 100 mm and loop length is 350 D overall. Measuring section (dashed rectangle) is 140 D long.

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Fig. 2

Results of rheological characterization: (a) shear stress, τ, versus shear rate, γ· measured in rheometer for various XG concentrations (symbols) and reference curve for water (solid line); shear stress is in the range (0.02–20 Pa), dashed lines indicate range of variation of shear stress at pipe wall, τw in the hydraulic loop and (b) viscosimetric data for various XG concentrations together with the Carreau–Yasuda fits (dotted lines) [25]. Data for XG 0.2% from Ref. [11] are shown by a thick solid line. Data for XG 0.2% from Ref. [17] are shown by a thin solid line.

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Fig. 3

Specific pressure drop versus bulk velocity for tap water (open symbol) and aqueous XG solutions (solid symbols). Error bars represent data variability over three independent tests. Solid line is value of specific pressure drop calculated using friction factor given by Eq. (3). Solid symbols represent different values of XG concentration. The arrow indicates increasing XG concentration.

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Fig. 4

Comparison against data from Ref. [11]: friction factor, f, versus generalized Reynolds number, ReMR; curve for tap water (solid line), MDR asymptote (dotted line), data for different XG solutions (solid symbols), and data from Ref. [11] (open triangles)

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Fig. 5

Comparison against Bewersdorff and Singh (BS) [13] data: 0.01% XG (diamonds), 0.10% XG (circles); present data (solid symbols), original BS data (gray symbols) (D1 = 3.146 mm, D2 = 5.186 mm, D3 = 6.067 mm), BS data rescaled to D0 = 100 mm (open symbols)

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Fig. 6

Effect of pipe cross section: drag reduction data measured for annular [18] (JP) and rectangular [16] (ENP) section at different XG concentrations (diamonds, 0.01% XG; squares, 0.05% XG and triangles 0.075% XG); our data (solid symbols), original JP/ENP data (gray symbols) (DH1 = 46 mm, DH2 = 49.2 mm), JP/ENP data rescaled to DH0 = 100 mm (open symbols)

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Fig. 7

Percent drag reduction for aqueous solutions at different XG concentrations as a function of friction Reynolds number, Reτ. Symbols represent values of XG concentration; arrow indicates increasing XG concentration; black line represents MDR according to Ref. [31].

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Fig. 8

Comparison between experimental data and predictive correlation by Ref. [10]: solid symbols identify data for different XG solutions; dotted lines identify Housiadas and Beris (HB2013) correlation prediction; curve for tap water (solid line) and MDR asymptote (dotted line) are shown for reference

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Fig. 9

Scale up and scale down of friction factor predicted by Ref. [10] correlation: curve for tap water (solid line), MDR asymptote (dotted line), data for different pipe diameters (solid symbols)

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Fig. 10

(a) Percent drag reduction for aqueous solutions at different XG concentrations as a function of bulk velocity in the pipe, U. Symbols represent values of XG concentration. Arrow indicates increasing XG concentration. (b) Variation of maximum %DR, %DRmax, as a function of XG concentration, C: the increment in %DR is less than linear with C. (c) Variation of threshold velocity for drag reduction, Ut, as a function of polymer concentration, C, and linear fit (dashed line).

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Fig. 11

Percent net cost savings expected from use of XG as DRA: dashed lines on the background represent isocontours of %DR (starting from zero, step 2%; isocontour labels are in gray); continuous lines (in color online) represent isocontours of %S (starting from zero, step 2%); subfigures correspond to different pipeline scenario: (a) α = 5 × 10–2 s2/m2, (b) α = 1 × 10–1 s2/m2, (c) α = 2.5 × 10–1 s2/m2, and (d) α = 5 × 10–1 s2/m2

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Fig. 12

Results of rheological data processing: (a) variation of zero-shear viscosity versus concentration; present data (solid circle); ESC (1999) data [11] (open circle); JEP (2010) data [17] (open triangle); power law fitting of data in the dilute and semidilute concentration range (solid line, present data; dashed line, JEP data); (b) variation of viscosity versus concentration: symbols identify different values of shear rate, γ·, solid lines are power law fit, η=KH'·Cn'', for concentration values larger than 0.01%; and (c) value of fitting parameters KH' and n'' as a function of shear rate, γ·.




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