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Research Papers: Fundamental Issues and Canonical Flows

Losses Due to Conduit Components: An Optimization Strategy and Its Application

[+] Author and Article Information
Bastian Schmandt

Institute of Thermo-Fluid Dynamics,
Hamburg University of Technology,
Hamburg 21073, Germany
e-mail: bastian.schmandt@tuhh.de

Heinz Herwig

Institute of Thermo-Fluid Dynamics,
Hamburg University of Technology,
Hamburg 21073, Germany
e-mail: h.herwig@tuhh.de

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received December 11, 2014; final manuscript received September 8, 2015; published online October 14, 2015. Assoc. Editor: Moran Wang.

J. Fluids Eng 138(3), 031204 (Oct 14, 2015) (8 pages) Paper No: FE-14-1746; doi: 10.1115/1.4031607 History: Received December 11, 2014; Revised September 08, 2015

In the present study, we introduce a method which we call the glass box optimization (GBO) method as a strategy how to reduce flow losses whenever numerical data based on computational fluid dynamics (CFD)-results are available. Based on local values of the velocity and entropy generation fields, a systematic analysis of the loss mechanisms involved is used in order to develop control mechanisms for the reduction of losses due to a conduit component. Furthermore, it is shown how the losses are distributed between a component itself and the adjacent flow field. Since often a large amount of the losses occurs outside of the actual component, it is discussed under which circumstances an optimized component leads to improved efficiency of an entire fluid flow network. The method is exemplified for turbulent flow through a 90 deg bend.

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Figures

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Fig. 1

Geometry of the standard 90 deg bend including upstream and downstream channels

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Fig. 2

Determination of the overall entropy generation rate due to a conduit component. ΔS˙u: additional entropy generation upstream of the component. S˙c: entropy generation in the component. ΔS˙d: additional entropy generation downstream of the component.

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Fig. 3

Distribution of the entropy generation S˙′ along the 90 deg bend centerline for a square cross section; dark: losses inside the bend, light: additional losses in the upstream and downstream tangents up to Lu and Ld

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Fig. 4

Geometry of the 90 deg bend; CS0Dh…CS3Dh: cross sections to show profiles of entropy generation and velocity indexed with the downstream distance from the bend's outlet; xm: centerline coordinate

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Fig. 5

Velocity and entropy generation rate profiles in five cross sections, see Fig. 4. Symmetry plane at z/Dh = 0.

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Fig. 6

Geometry modification function illustrated for x̃p=0.7, a = 0.6, and various values of T

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Fig. 9

Optimization progress

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Fig. 10

Parameters of all individuals during the optimization (six geometry parameters and the result K)

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Fig. 7

Cross section averaged entropy generation S˙′/S˙0′ along the 90 deg bend centerline; dashed line: Reference case with Kref = 0.33; solid line: optimal case with Kopt = 0.27

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Fig. 8

Velocity and entropy generation rate improvement profiles S˙‴−S˙ref‴ in five cross sections, cf. Fig. 4. Left column: velocity component in the direction of the axial coordinate, solid line: profile in the midplane for developed flow; middle column: local entropy generation reduction compared to the reference case; right column: different view angle to show the effect close to the upper/lower wall of the bend.

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Fig. 11

The head loss coefficient of the initial case: comparison of results from different sources; ⊙: initial case from this study with Kref = 0.33, *: numerical results from Ref. [2], – –: experiments from Miller [13], +: experiments from Ito [14], ○: experiments from Hofmann [15], ––: correlation from Idelchick [16]

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