Research Papers

Improved Modeling Capabilities of the Airflow Within Turbine Case Cooling Systems Using Smart Porous Media

[+] Author and Article Information
Yanling Li

Department of Aeronautical and
Automotive Engineering,
Loughborough University,
Loughborough LE11 3TU, UK
e-mail: y.li3@lboro.ac.uk

A. Duncan Walker

Department of Aeronautical and
Automotive Engineering,
Loughborough University,
Loughborough LE11 3TU, UK
e-mail: a.d.walker@lboro.ac.uk

John Irving

Rolls-Royce plc,
P.O. Box 31, Moor Lane,
Derby DE24 8BJ, UK
e-mail: John.Irving2@Rolls-Royce.com

1Corresponding author.

Manuscript received May 15, 2018; final manuscript received October 26, 2018; published online November 22, 2018. Assoc. Editor: Riccardo Da Soghe.

J. Eng. Gas Turbines Power 141(5), 051003 (Nov 22, 2018) (12 pages) Paper No: GTP-18-1212; doi: 10.1115/1.4041933 History: Received May 15, 2018; Revised October 26, 2018

Impingement cooling is commonly employed in gas turbines to control the turbine tip clearance. During the design phase, computational fluid dynamics (CFD) is an effective way of evaluating such systems but for most turbine case cooling (TCC) systems resolving the small scale and large number of cooling holes is impractical at the preliminary design phase. This paper presents an alternative approach for predicting aerodynamic performance of TCC systems using a “smart” porous media (PM) to replace regions of cooling holes. Numerically CFD defined correlations have been developed, which account for geometry and local flow field, to define the PM loss coefficient. These are coded as a user-defined function allowing the loss to vary, within the calculation, as a function of the predicted flow and hence produce a spatial variation of mass flow matching that of the cooling holes. The methodology has been tested on various geometrical configurations representative of current TCC systems and compared to full cooling hole models. The method was shown to achieve good overall agreement while significantly reducing both the mesh count and the computational time to a practical level.

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Fig. 1

Typical TCC system: (a) side view and (b) end view

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Fig. 2

Manifold flow distribution [5]

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Fig. 3

Porous media representation of impingement cooling holes: (a) perforated plate (typical of impingement cooling holes) and (b) PM

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Fig. 4

An example of the computational domain used for the sensitivity study

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Fig. 5

Effect of flow angle and area ratio on mass flow distribution: aAR2=5,bAR2=4,cAR2=3,(d)AR2=2, eAR2=2,ΔT=0−400K, and (f)AR2=1

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Fig. 6

Normalized velocity magnitude contour plots with θ=68deg (showing 6 out of 20 IH): (a)AR2=5, (b)AR2=4, (c)AR2=3, (d)AR2=2, and (e)AR2=1

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Fig. 7

Computational domains with T-shaped inlet (t/d = 0.58, s/d = 8.33): (a) IH case and (b) PM case

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Fig. 8

Computational domains with T-shaped inlet (t/d = 0.58, s/d = 8.33): (a) without curvature and (b) with curvature

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Fig. 9

Mass flow distributions for cases with T-shaped inlet: (a)AR2=4, (b)AR2=3, (c)AR2=2, (d)AR2=2,ΔT=0−400K, and (e)AR2=1

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Fig. 10

Normalized velocity magnitude contour pots (left: IH case; right: PM case, showing first 7 out of 20 IH): (a)AR2=4, (b)AR2=3, (c)AR2=2, and (d)AR2=1

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Fig. 11

Effect of t/d on normalized mass flow distribution: (a)AR2=2 and (b)AR2=1

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Fig. 12

Normalized velocity magnitude contour plots with AR2 = 2 (showing 7 out of 20 IH)

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Fig. 13

Mass flow distribution for cases with t/d ratio of 0.35: (a)AR2=2 and (b)AR2=1

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Fig. 14

Mass flow distribution with T-shaped inlet: (a) effect of curvature, IH, AR2 = 2 and (b) comparison with PM, δ = 0.01

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Fig. 15

Normalized velocity magnitude contour plots for T-shaped inlet cases (IH): (a) no curvature, AR2 = 2, (b) high curvature (δ = 0.02), AR2 = 2, (c) low curvature (δ = 0.01), AR2 = 2, and (d) low curvature (δ = 0.01), AR2 = 1

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Fig. 16

Normalized velocity magnitude contour plots for T-shaped inlet cases (PM): (a) high curvature (δ = 0.02), AR2 = 2 and (b) low curvature (δ = 0.01), AR2 = 2



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