Dynamical Properties of Combustion Instability in a Laboratory-Scale Gas-Turbine Model Combustor
Dynamical Properties of Combustion Instability in a Laboratory-Scale Gas-Turbine Model Combustor
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实验室规模燃气轮机模型燃烧室燃烧不稳定性的动力学特性
DOI：
10.1115/1.4034700
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Shohei Domen and Shigeru Tachibana
中科院分区：
文献类型：
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作者：
Hiroshi Gotoda;Kenta Hayashi;Ryosuke Tsujimoto;Shohei Domen and Shigeru Tachibana
本文对带有旋流稳定湍流火焰的贫油预混燃气轮机模型燃烧室中燃烧不稳定性的非线性动力学进行了实验研究。间歇燃烧振荡在爆发振荡和伪周期振荡之间不规则地来回切换，表现出混沌的确定性。通过考虑两种非线性预测方法清楚地证明了这一点：扩展版本（Gotoda等人，2015，“Nonlinear Forecasting of the Generalized Kuramoto-Sivashinsky Equation，”Int. J. Bifurcation Chaos，25，p. 1530015）的Sugihara和May算法（Sugihara和May，1990，“Nonlinear Forecasting as a Way of Distinguishing Chaos From Measurement Error in Time Series，”Nature，344，pp. 734-741）作为局部预测器，以及广义径向基函数（GRBF）网络作为全局预测器（Gotoda等人，2012，“Characterization of Complexities in Combustion Instability in a Lean Premixed Gas-Turbine Model Combustor”，Chaos，22，p.043128; Gotoda等人，2016年（未出版）。前者使我们能够提取混沌的短期可预测性和长期不可预测性，而后者可以通过自由运行的方法产生替代数据来测试确定性。基于符号序列方法的置换熵估计的替代数据的确定性测试，也被用来作为在线检测器，以防止贫油井喷。
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in Combustion Instability in a Lean Premixed Gas-Turbine Model Combustor,” Chaos,22, p. 043128; Gotoda et al., 2016 (unpublished)).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.