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Research Papers: Gas Turbines: Combustion, Fuels, and Emissions

Unsteady Computational Fluid Dynamics Investigation of Effusion Cooling Process in a Lean Burn Aero-Engine Combustor

[+] Author and Article Information
L. Mazzei

Department of Industrial Engineering,
University of Florence,
via S. Marta 3,
Florence 50139, Italy
e-mail: lorenzo.mazzei@htc.de.unifi.it

A. Picchi

Department of Industrial Engineering,
University of Florence,
via S. Marta 3,
Florence 50139, Italy
e-mail: alessio.picchi@htc.de.unifi.it

A. Andreini

Department of Industrial Engineering,
University of Florence,
via S. Marta 3,
Florence 50139, Italy
e-mail: antonio.andreini@htc.de.unifi.it

B. Facchini

Department of Industrial Engineering,
University of Florence,
via S. Marta 3,
Florence 50139, Italy
e-mail: bruno.facchini@htc.de.unifi.it

I. Vitale

Combustors Product Engineering,
GE Avio S.r.l.,
via Primo Maggio 56,
Rivalta di Torino (TO) 10040, Italy
e-mail: ignazio.vitale@avioaero.com

1Corresponding author.

Contributed by the Combustion and Fuels Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received June 18, 2016; final manuscript received June 22, 2016; published online August 16, 2016. Editor: David Wisler.

J. Eng. Gas Turbines Power 139(1), 011502 (Aug 16, 2016) (11 pages) Paper No: GTP-16-1230; doi: 10.1115/1.4034192 History: Received June 18, 2016; Revised June 22, 2016

This work describes the main findings of a computational fluid dynamics (CFD) analysis intended to accurately investigate the flow field and wall heat transfer as a result of the mutual interaction between a swirling flow generated by a lean burn injection system and a slot–effusion liner cooling system. In order to overcome some limitations of Reynolds-averaged Navier–Stokes (RANS) approach, the simulations were performed with shear stress transport (SST)–scale-adaptive simulation (SAS), a hybrid RANS–large eddy simulation (LES) model. Moreover, the significant computational effort due to the presence of more than 600 effusion holes was limited exploiting two different modeling strategies: a homogeneous model based on the application of uniform boundary conditions on both aspiration and injection sides, and another solution that provides a coolant injection through point mass sources within a single cell. CFD findings were compared to experimental results coming from an investigation carried out on a three-sector linear rig. The comparison pointed out that advanced modeling strategies, i.e., based on discrete mass sources, are able to reproduce the effects of mainstream–coolant interactions on convective heat loads. By validating the approach through a benchmark against time-averaged quantities, the transient data acquired were examined in order to better understand the unsteady behavior of the thermal load through a statistical analysis, providing useful information with a design perspective.

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References

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Figures

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Fig. 1

Swirling flows at nonreactive conditions: measurements by PIV and predictions by RANS calculations [10]

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Fig. 2

Classical flow pattern of an array of swirlers (top) and dome-attached swirling flow (bottom) [11]

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Fig. 3

Experimental apparatus [7]

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Fig. 4

Detail of the PIV measurements planes location

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Fig. 5

Computational domain

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Fig. 6

Example of computational mesh for the AHM case

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Fig. 7

Conceptual representation of effusion hole modeling through SAFE approach

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Fig. 8

Comparison between experimental data and film cooling models: time-averaged flow field on the center plane

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Fig. 9

Comparison between experimental data and SAFE approach: time-averaged adiabatic effectiveness distributions

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Fig. 10

Comparison among experimental data, AHM, and SAFE approaches: time-averaged adiabatic effectiveness profiles

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Fig. 11

Comparison between experimental data and SAFE approach: Nusselt number distributions

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Fig. 12

Laterally averaged Nusselt number augmentation

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Fig. 13

Instantaneous ηaw and Nu/Nu0 distributions, probe positions reported with white dots

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Fig. 14

PDFs of Nusselt number augmentation factor

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Fig. 15

PDFs of adiabatic effectiveness

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