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Research Papers

Future Use of Large Eddy Simulation in Aero‐engines

[+] Author and Article Information
James C. Tyacke

Whittle Laboratory,
Department of Engineering,
University of Cambridge,
Cambridge CB3 0DY, UK
e-mail: jct53@cam.ac.uk

Paul G. Tucker

Whittle Laboratory,
Department of Engineering,
University of Cambridge,
Cambridge CB3 0DY, UK

We note Chapman's outer layer estimates are optimistic.

1Corresponding author.

Contributed by the International Gas Turbine Institute (IGTI) of ASME for publication in the JOURNAL OF TURBOMACHINERY. Manuscript received November 28, 2014; final manuscript received December 9, 2014; published online January 28, 2015. Editor: Kenneth C. Hall.

J. Turbomach 137(8), 081005 (Aug 01, 2015) (16 pages) Paper No: TURBO-14-1308; doi: 10.1115/1.4029363 History: Received November 28, 2014; Revised December 09, 2014; Online January 28, 2015

Computational fluid dynamics (CFD) has become a critical tool in the design of aero-engines. Increasing demand for higher efficiency, performance, and reduced emissions of noise and pollutants has focused attention on secondary flows, small scale internal flows, and flow interactions. In conjunction with low order correlations and experimental data, RANS (Reynolds-averaged Navier–Stokes) modeling has been used effectively for some time, particularly at high Reynolds numbers and at design conditions. However, the range of flows throughout an engine is vast, with most, in reality being inherently unsteady. There are many cases where RANS can perform poorly, particularly in zones characterized by strong streamline curvature, separation, transition, relaminarization, and heat transfer. The reliable use of RANS has also been limited by its strong dependence on turbulence model choice and related ad-hoc corrections. For complex flows, large-eddy simulation (LES) methods provide reliable solutions, largely independent of turbulence model choice, and at a relatively low cost for particular flows. LES can now be used to provide in depth knowledge of flow physics, for example, in areas such as transition and real wall roughness effects. This can be used to inform RANS and lower order modeling (LOM). For some flows, LES can now even be used for design. Existing literature is used to show the potential of LES for a range of flows in different zones of the engine. Based on flow taxonomy, best practices including RANS/LES zonalization, meshing requirements, and turbulent inflow conditions are introduced, leading to the proposal of a tentative expert system for industrial use. In this way, LES becomes a well controlled tool, suitable for design use and reduces the burden on the end user. The problem sizes tackled however have lagged behind potential computing power, hence future LES use at scale requires substantial progress in several key areas. Current and future solver technologies are thus examined and the potential current and future use of LES is considered.

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References

Figures

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Fig. 1

LES hierarchy indicating importance of each element

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Fig. 2

Number of LES publications in turbomachinery compared to other disciplines and available computational power

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Fig. 3

Potential inflow options for idealized turbulence

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Fig. 4

Merging of wake, BL, and freestream turbulence for a LPT simulation (vorticity magnitude contours)

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Fig. 5

Inputs to flow field in a real propulsive jet nozzle: (a) potential inputs for real jet nozzles and (b) idealized and realistic inputs

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Fig. 6

Jet LES with outer sponge zone indicated

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Fig. 7

Example of physics coupling for a turbine passage

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Fig. 8

Grid topology influence on T–S wave decay. % error in amplitude at ca. 50% domain length.

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Fig. 9

Use of numerical schemes in literature

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Fig. 10

Numerical influence on HDT

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Fig. 11

(a) Tiling and (b) parareal methods to avoid communication and increase parallelism

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Fig. 12

HDT for various SGS models

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Fig. 13

Grid point requirements for LES and hybrid RANS–(N)LES compared with typical grid densities used in existing literature for (a) turbomachinery and (b) airframe

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Fig. 14

Use of a model energy spectrum to improve grid resolution for a turbine blade

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Fig. 15

Summary of validation levels contrasting turbomachinery and airframe zones (%)

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Fig. 16

Numerous choices related to eddy-resolving methods

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Fig. 17

Frequency of modeling methods used for turbomachinery contrasted with those for airframes

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Fig. 18

Example engine-level RANS/LES zonalization

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Fig. 19

Example component-level RANS/LES zonalization based on flow features

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Fig. 20

Example class A flows: (a) ribbed ducts, (b) CBTEs, and (c) predominantly free-shear flows

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Fig. 21

Example class B LPT flow and potential inflow requirements

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Fig. 22

Example class C flows: (a) HPT and (b) labyrinth/rim-seals

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Fig. 23

Examples of an expert system for (a) internal cooling passages and (b) an LPT blade passage

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Fig. 24

Example of an expert system menu for different engine zones

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Fig. 25

Interdisciplinary interactions required

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Fig. 26

Potential uses of eddy-resolving simulations

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