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

Analysis of Azimuthal Thermo-acoustic Modes in Annular Gas Turbine Combustion Chambers

[+] Author and Article Information
Mirko R. Bothien

Baden 5400, Switzerland
e-mail: mirko.bothien@power.alstom.com

Nicolas Noiray, Bruno Schuermans

Baden 5400, Switzerland

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 July 29, 2014; final manuscript received September 23, 2014; published online December 9, 2014. Editor: David Wisler.

J. Eng. Gas Turbines Power 137(6), 061505 (Jun 01, 2015) (8 pages) Paper No: GTP-14-1447; doi: 10.1115/1.4028718 History: Received July 29, 2014; Revised September 23, 2014; Online December 09, 2014

Modern gas turbine combustors operating in lean-premixed mode are prone to thermo-acoustic instabilities. In annular combustion chambers, usually azimuthal acoustic modes are the critical ones interacting with the flame. In case of constructive interference, high amplitude oscillations might result. In this paper, the azimuthal acoustic field of a full-scale engine is investigated in detail. The analyses are based on measurements in a full-scale gas turbine, analytical models to derive the system dynamics, as well as simulations performed with an in-house 3d nonlinear network model. It is shown that the network model is able to reproduce the behavior observed in the engine. Spectra, linear growth rates, as well as the statistics of the system's dynamics can be predicted. A previously introduced algorithm is used to extract linear growth rates from engine and model time domain data. The method's accuracy is confirmed by comparison of the routine's results to analytically determined growth rates from the network model. The network model is also used to derive a burner staging configuration, resulting in the decrease of linear growth rate and thus an increase of engine operation regime; model predictions are verified by full-scale engine measurements. A thorough investigation of the azimuthal modes statistics is performed. Additionally, the network model is used to show that an unfavorable flame temperature distribution with an amplitude of merely 1% of the mean flame temperature can change the azimuthal mode from dominantly rotating to dominantly standing. This is predicted by the network model that only takes into account flame fluctuations in axial direction.

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Fig. 1

Ta3 network diagram to model the engine thermo-acoustics

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Fig. 2

Simulated spectra (bottom) and corresponding system eigenvalues (top) for different ΔT/T¯

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Fig. 3

Averaged modal amplitudes from Ta3 simulations at various ΔT/T¯

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Fig. 4

Joint probability densities obtained from simulations at various ΔT/T¯ (see also Figs. 3 and 4)

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Fig. 5

Averaged spectra baseline configuration. Top: Ta3 simulations; bottom: full-scale engine measurements.

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Fig. 6

Eigenvalues baseline configuration (growth rate in % of first azimuthal mode's frequency). Top: calculated from Ta3 (○) and system identification applied to Ta3 with arctangent (+) and cubic (×) nonlinearity; bottom: nonlinear system identification applied to engine data.

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Fig. 7

Joint probability density functions of modal amplitudes a and b (cf. to Eq. (5)) extracted from Ta3 simulations (top) and engine measurements (bottom). Baseline configuration.

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Fig. 8

Spatially averaged spectra: baseline and optimized configuration. Top: Ta3 simulations; bottom: full-scale engine measurements. Black: baseline, red: optimized configuration.

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Fig. 9

Eigenvalues baseline and optimized configuration (growth rate in % of first azimuthal mode's frequency). Top: calculated from Ta3 (baseline: black ○, optimized: red ) and system identification applied to Ta3 with arctangent saturation (baseline: black × , optimized: red *); bottom: identified eigenvalues of dominant mode for engine measurement.

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Fig. 10

Joint probability density functions of modal amplitudes a and b (cf. to Eq. (5)) extracted from Ta3 simulations (top) and engine measurements (bottom). Optimized configuration.




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