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

Dynamical Properties of Combustion Instability in a Laboratory-Scale Gas-Turbine Model Combustor

[+] Author and Article Information
Hiroshi Gotoda

Department of Mechanical Engineering,
Tokyo University of Science,
6-3-1 Niijuku,
Katsushika, Tokyo 125-8585, Japan
e-mail: gotoda@rs.tus.ac.jp

Kenta Hayashi, Ryosuke Tsujimoto, Shohei Domen

Department of Mechanical Engineering,
Ritsumeikan University,
1-1-1 Nojihigashi,
Kusatsu, Shiga 525-8577, Japan

Shigeru Tachibana

Aeronautical Technology Directorate,
Japan Aerospace Exploration Agency,
7-44-1 Jindaiji-Higashi,
Chofu, Tokyo 182-8522, Japan

Contributed by the Combustion and Fuels Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received June 30, 2016; final manuscript received August 11, 2016; published online November 8, 2016. Editor: David Wisler.

J. Eng. Gas Turbines Power 139(4), 041509 (Nov 08, 2016) (6 pages) Paper No: GTP-16-1294; doi: 10.1115/1.4034700 History: Received June 30, 2016; Revised August 11, 2016

We present an experimental study on the nonlinear dynamics of combustion instability in a lean premixed gas-turbine model combustor with a swirl-stabilized turbulent flame. Intermittent combustion oscillations switching irregularly back and forth between burst and pseudo-periodic oscillations exhibit the deterministic nature of chaos. This is clearly demonstrated by considering two nonlinear forecasting methods: an extended version (Gotoda et al., 2015, “Nonlinear Forecasting of the Generalized Kuramoto-Sivashinsky Equation,” Int. J. Bifurcation Chaos, 25, p. 1530015) of the Sugihara and May algorithm (Sugihara and May, 1990, “Nonlinear Forecasting as a Way of Distinguishing Chaos From Measurement Error in Time Series,” Nature, 344, pp. 734–741) as a local predictor, and a generalized radial basis function (GRBF) network as a global predictor (Gotoda et al., 2012, “Characterization of Complexities in Combustion Instability in a Lean Premixed Gas-Turbine Model Combustor,” Chaos, 22, p. 043128; Gotoda et al., 2016 (unpublished)). The former enables us to extract the short-term predictability and long-term unpredictability of chaos, while the latter can produce surrogate data to test for determinism by a free-running approach. The permutation entropy based on a symbolic sequence approach is estimated for the surrogate data to test for determinism and is also used as an online detector to prevent lean blowout.

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References

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Figures

Grahic Jump Location
Fig. 1

Temporal evolution of OH* chemiluminescence intensity fluctuations I′ for different equivalence ratios ϕ

Grahic Jump Location
Fig. 2

Power spectra of OH* chemiluminescence intensity fluctuations I′ for different equivalence ratios ϕ

Grahic Jump Location
Fig. 3

Variations in permutation entropy hp for the original and surrogate time-series data of intermittent combustion oscillations. Note that 20 sets of surrogate time-series data are obtained by the radial basis function network.

Grahic Jump Location
Fig. 4

Variations in correlation coefficient C obtained by the local predictor for the intermittent combustion oscillations as a function of prediction time tP. Variations in C for increments of ΔI′(=I′(ti+1)−I′(ti)) are also shown as a function of tP.

Grahic Jump Location
Fig. 5

Variations in permutation entropy hp of OH* chemiluminescence fluctuations I′ as a function of equivalence ratio ϕ

Grahic Jump Location
Fig. 6

Temporal variations in volume flow rate of secondary fuel Qsecondary and equivalence ratio ϕ with decreasing volume flow rate of main fuel Qmain

Grahic Jump Location
Fig. 7

Temporal variations in volume flow rate of secondary fuel Qsecondary and equivalence ratio ϕ with decreasing and increasing volume flow rate of main fuel Qmain

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