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

Gotoda, Hiroshi; Amano, Masahito; Miyano, Takeshi; Ikawa, Takuya; Maki, Koshiro; Tachibana, Shigeru

doi:10.1063/1.4766589

Cited by 110 publications

(60 citation statements)

References 54 publications

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“…For example, Gotoda et al 14 have observed variations of minimum permutation entropy 41 as a function of equivalence ratio È to detect the degree of complexity of pressure time series at the onset of combustion instability. They have also observed that the dynamic behavior in combustion instability near lean blowout exhibits a self-affine structure, which is ascribed to fractional Brownian motion and undergoes chaos by the onset of combustion oscillations with slow amplitude modulation.…”

Section: Instability Prediction By D-markov and âD-markov Entropy Ratesmentioning

confidence: 99%

“…time series, which varies linearly with data length. The minimum permutation entropy is obtained by (multiple times with varying order) usage of a fast window-based algorithm 43 on pressure data that are downsampled to 5 kHz with an embedding delay of 1 ms similar to Gotoda et al 14 The time complexity for computation of both Hurst exponent 9 and ÂD-Markov entropy rate (see Instability prediction by D-Markov and ÂD-Markov entropy rates subsection) vary linearly with data length and have very small slopes. The mean of the Hurst exponent of order 2 is estimated here with a range of time scales ranging from 15 ms to 30 ms (i.e.…”

Section: Instability Prediction By D-markov and âD-markov Entropy Ratesmentioning

confidence: 99%

“…However, a major drawback is the bifurcation in the combustor behavior at the frequency break-point; therefore, it may not be used as a tool of early warning, thus motivating the need for a fast predictive tool that is generic. Both analytical and experimental investigations on the onset of thermo-acoustic instabilities have been reported by several researchers, for example, Lieuwen, 12 Gotoda et al, 13,14 and Murugesan and Sujith. 15 Some of these recent studies have shown the possibility of encountering low-dimensional chaotic oscillations in combustors by employing several methods of nonlinear time series analysis.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic data-driven prediction of instability in a swirl-stabilized combustor

Sarkar

Chakravarthy

Ramanan

et al. 2016

International Journal of Spray and Combustion Dynamics

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Combustion instability poses a negative impact on the performance and structural durability of both land-based and aircraft gas turbine engines, and early detection of combustion instabilities is of paramount importance not only for performance monitoring and fault diagnosis, but also for initiating efficient decision and control of such engines. Combustion instability is, in general, characterized by self-sustained growth of large-amplitude pressure tones that are caused by a positive feedback arising from complex coupling of localized hydrodynamic perturbations, heat energy release, and acoustics of the combustor. This paper proposes a fast dynamic data-driven method for detecting early onsets of thermo-acoustic instabilities, where the underlying algorithms are built upon the concepts of symbolic time series analysis (STSA) via generalization of D-Markov machine construction. The proposed method captures the spatiotemporal co-dependence among time series from heterogeneous sensors (e.g. pressure and chemiluminescence) to generate an information-theoretic precursor, which is uniformly applicable across multiple operating regimes of the combustion process. The proposed method is experimentally validated on the time-series data, generated from a laboratory-scale swirl-stabilized combustor, while inducing thermo-acoustic instabilities for various protocols (e.g. increasing Reynolds number (Re) at a constant fuel flow rate and reducing equivalence ratio at a constant air flow rate) at varying air-fuel premixing levels. The underlying algorithms are developed based on D-Markov entropy rates, and the resulting instability precursor measure is rigorously compared with the state-of-the-art techniques in terms of its performance of instability prediction, computational complexity, and robustness to sensor noise.

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Section: Instability Prediction By D-markov and âD-markov Entropy Ratesmentioning

confidence: 99%

Section: Instability Prediction By D-markov and âD-markov Entropy Ratesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic data-driven prediction of instability in a swirl-stabilized combustor

Sarkar

Chakravarthy

Ramanan

et al. 2016

International Journal of Spray and Combustion Dynamics

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“…The dynamics of a combustor close to blowout is a topic of intense current research. For example, Gotoda and coworkers [17] [18] 224…”

Section: B Reduction Of Modeling Complexity Near Lean Blowoutmentioning

confidence: 99%

“…Kabiraj et al [15] and Kabiraj and Sujith [16] investigated dynamics of a ducted laminar premixed flame and using tools of dynamical systems analysis demonstrated that the system undergoes a number of bifurcations involving quasiperiodic and intermittent behaviors leading to blowout. Gotoda and co-workers [17] used a number of analytical tools to examine the dynamics of a model gas turbine combustor and showed that close to lean blowout the dynamics of the combustor exhibits a self-affine structure indicating fractional Brownian motion but changes to chaotic oscillations showing oscillations with slow amplitude modulation as the equivalence ratio increases. Gotoda et al [18] showed that the relatively regular pressure oscillations undergo transition to intermittent chaotic oscillations as the equivalence ratio is decreased and the system approaches LBO.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Data-Driven Prediction of Lean Blowout in a Swirl-Stabilized Combustor

Sarkar

Ray

Mukhopadhyay

et al. 2015

International Journal of Spray and Combustion Dynamics

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This paper addresses dynamic data-driven prediction of lean blowout (LBO) phenomena in confined combustion processes, which are prevalent in many physical applications (e.g., landbased and aircraft gas-turbine engines). The underlying concept is built upon pattern classification and is validated for LBO prediction with time series of chemiluminescence sensor data from a laboratory-scale swirl-stabilized dump combustor. The proposed method of LBO prediction makes use of the theory of symbolic dynamics, where (finite-length) time series data are partitioned to produce symbol strings that, in turn, generate a special class of probabilistic finite state automata (PFSA). These PFSA, called D-Markov machines, have a deterministic algebraic structure and their states are represented by symbol blocks of length D or less, where D is a positive integer. The D-Markov machines are constructed in two steps: (i) state splitting, i.e., the states are split based on their information contents, and (ii) state merging, i.e., two or more states (of possibly different lengths) are merged together to form a new state without any significant loss of the embedded information. The modeling complexity (e.g., number of states) of a D-Markov machine model is observed to be drastically reduced as the combustor approaches LBO. An anomaly measure, based on Kullback-Leibler divergence, is constructed to predict the proximity of LBO. The problem of LBO prediction is posed in a pattern classification setting and the underlying algorithms have been tested on experimental data at different extents of fuel-air premixing and fuel/air ratio. It is shown that, over a wide range of fuel-air premixing, D-Markov machines with D > 1 perform better as predictors of LBO than those with D = 1.Keywords: Data-driven Dynamics, Lean Blowout, Gas Turbine Combustor, Symbolic Dynamics, Probabilistic Finite State AutomataInternational journal of spray and combustion dynamics · Volume . 7 · Number . 3 . 2015 -pages 209 -242 209 *Corresponding author email: svs5464@psu.edu; axr2@psu.edu; achintya.mukho@gmail.com; sen.swarnendu@gmail.com INTRODUCTIONUltra-lean combustion is commonly used for reduction of oxides of nitrogen (NOx) and is susceptible to thermo-acoustic instabilities and lean blowout (LBO). It is well known that occurrence of LBO could be detrimental for operations of both land-based and aircraft gas turbine engines. For example, LBO in land-based gas turbines may lead to engine shutdown and subsequent re-ignition involves loss of productivity; similarly, LBO in aircraft gas turbines may cause loss of engine thrust especially if the fuel flow is suddenly reduced during a throttling operation, or if air flow reduction takes place at a much slower rate due to the moment of inertia of the compressor. In essence, a sudden decrease in the equivalence ratio may lead to LBO in gas turbine engines, which could have serious consequences. This phenomenon calls for a real-time prediction of LBO and adoption of appropriate measures to mitigate it. It is noted...

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Introduction

Sujith

Pawar

2021

Springer Series in Synergetics

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

Cited by 110 publications

References 54 publications

Dynamic data-driven prediction of instability in a swirl-stabilized combustor

Dynamic data-driven prediction of instability in a swirl-stabilized combustor

Dynamic Data-Driven Prediction of Lean Blowout in a Swirl-Stabilized Combustor

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