Nonlinear determinism in degenerated combustion instability in a gas-turbine model combustor

2018 ◽  
Vol 510 ◽  
pp. 345-354 ◽  
Author(s):  
Hiroaki Kobayashi ◽  
Hiroshi Gotoda ◽  
Shigeru Tachibana
Keyword(s):  
Gas Turbine ◽  
Combustion Instability ◽  
Turbine Model

scholarly journals Characterization of complexities in combustion instability in a lean premixed gas-turbine model combustor

2012 ◽  
Vol 22 (4) ◽  
pp. 043128 ◽  
Author(s):  
Hiroshi Gotoda ◽  
Masahito Amano ◽  
Takaya Miyano ◽  
Takuya Ikawa ◽  
Koshiro Maki ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Combustion Instability ◽  
Lean Premixed ◽  
Turbine Model

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

2016 ◽  
Vol 139 (4) ◽  
Author(s):  
Hiroshi Gotoda ◽  
Kenta Hayashi ◽  
Ryosuke Tsujimoto ◽  
Shohei Domen ◽  
Shigeru Tachibana
Keyword(s):  
Gas Turbine ◽  
Combustion Instability ◽  
Turbulent Flame ◽  
Surrogate Data ◽  
Permutation Entropy ◽  
Lean Blowout ◽  
Lean Premixed ◽  
Free Running ◽  
Nonlinear Forecasting ◽  
Turbine Model

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in Combustion Instability in a Lean Premixed Gas-Turbine Model Combustor,” Chaos, 22, p. 043128; Gotoda et al., 2016 (unpublished)).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.

scholarly journals Laser Diagnostic Study of the Mechanism of a Periodic Combustion Instability in a Gas Turbine Model Combustor

2005 ◽  
Vol 75 (1-4) ◽  
pp. 275-292 ◽  
Author(s):  
P. Weigand ◽  
W. Meier ◽  
X. R. Duan ◽  
R. Giezendanner-Thoben ◽  
U. Meier
Keyword(s):  
Gas Turbine ◽  
Combustion Instability ◽  
Diagnostic Study ◽  
Laser Diagnostic ◽  
Turbine Model

Investigation into the cause of high multi-mode combustion instability of H2/CO/CH4 syngas in a partially premixed gas turbine model combustor

2015 ◽  
Vol 35 (3) ◽  
pp. 3263-3271 ◽  
Author(s):  
Min Chul Lee ◽  
Jisu Yoon ◽  
Seongpil Joo ◽  
Jeongjin Kim ◽  
Jeongjae Hwang ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Combustion Instability ◽  
Partially Premixed ◽  
Multi Mode ◽  
Turbine Model

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

2016 ◽  
Author(s):  
Hiroshi Gotoda ◽  
Kenta Hayashi ◽  
Ryosuke Tsujimoto ◽  
Shohei Domen ◽  
Shigeru Tachibana
Keyword(s):  
Gas Turbine ◽  
Radial Basis Function Network ◽  
Combustion Instability ◽  
Turbulent Flame ◽  
Surrogate Data ◽  
Permutation Entropy ◽  
Lean Blowout ◽  
Free Running ◽  
Turbine Model

We present an experimental study on 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 a burst and pseudo-periodic oscillations exhibit the deterministic nature of chaos. This is clearly demonstrated by considering two nonlinear forecasting methods: the extended version [1] of the Sugihara & May algorithm [2] as a local predictor, and the generalized radial basis function network as a global predictor [3], [4]. The former enables us to extract the short-term predictability and long-term unpredictability nature of chaos, while the latter can produce surrogate data to test for determinism as a free running approach. Permutation entropy is estimated by a symbolic sequence approach for the surrogate data to test for determinism and is also used as an online detector to prevent lean blowout.

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