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

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in Combustion Instability in a Lean Premixed Gas-Turbine Model Combustor,” Chaos, 22, p. 043128; Gotoda et al., 2016 (unpublished)).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

Sign in / Sign up

Export Citation Format

Share Document