Acoustic Based Rapid Blowout Mitigation in a Swirl Stabilized Combustor

Prakash, Shashvat; Nair, Satish; Muruganandam, T. M.; Neumeier, Yedidia; Lieuwen, Tim; Seitzman, Jerry M.; Zinn, B. T.

doi:10.1115/gt2005-68589

Cited by 13 publications

(11 citation statements)

References 9 publications

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“…Figure 6(c) shows the performance of the proposed LBO measure for different flow rates at a (nearly) non-premixed condition. [23] and Mukhopadhyay et al [12] to perform equivalently or sometime superior to other time series based online LBO prediction tools [6], [7], [8], [9], [10]. This subsection makes a comparison of the predictive performance of D-Markov machines for D > 1 with that for D = 1, which was reported earlier for LBO prediction [12].…”

Section: ) Lbo Prediction For Non-premixed Flamementioning

confidence: 99%

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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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Section: ) Lbo Prediction For Non-premixed Flamementioning

confidence: 99%

“…Lieuwen, Seitzman and coworkers [6], [7], [8], [9] used time series data from acoustic and optical sensors for early detection and control of LBO in laboratory-scale gas turbine combustors. Nair and Lieuwen [8] identified blowout parameters using various (e.g., spectral, statistical, wavelet-based and threshold-based) techniques.…”

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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“…Furthermore the LBO limit can be extended to lower equivalence ratios through the injection of pilot fuel/air [5], addition of hydrogen [6][7][8] or an increase of preheat temperature [2,9], whereas the influence of pressure has been found to be small [7]. During operation, the proximity to LBO is currently determined on the basis of empirical quantities, which are mostly based on the intensity of low-frequency combustion oscillations [10][11][12][13] and require careful calibration before operation.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of lean blowout of a swirl-stabilized flame in a gas turbine model combustor

Stöhr

Boxx

Carter

et al. 2011

Proceedings of the Combustion Institute

223

119

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Lean blowout (LBO) of a partially premixed swirl flame is studied using chemiluminescence imaging and simultaneous stereo-PIV and OH-PLIF measurements at repetition rates up to 5 kHz. The flame, which is operated with methane and air in a gas turbine model combustor at atmospheric pressure, features a pronounced precessing vortex core (PVC) at the inner shear layer. In the first part of the study, the stabilization mechanism of the flame close to LBO is investigated. The fields of velocity and OH show that near LBO there are essentially two regions where reaction takes place, namely the helical zone along the PVC and the flame root around the lower stagnation point. The zone along the PVC is favorable to the flame due to low strain rates in the vortex center and accelerated mixing of burned and fresh gas. The flame root, which is located close to the nozzle exit, is characterized by an opposed flow of hot burned gas and relatively fuel-rich fresh gas. Due to the presence of high strain rates, the flame root is inherently unstable near LBO, featuring frequent extinction and re-ignition. The blowout process, discussed in the second part of the study, starts when the extinction of the flame root persists over a critical length of time. Subsequently, the reaction in the helical zone can no longer be sustained and the flame finally blows out. The results highlight the crucial role of the flame root, and suggest that well-aimed modifications of flow field or mixture fraction in this region might shift the LBO limit to leaner conditions.

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“…For wavelet-based analysis, they used Mexican Hat wavelet and a customized wavelet that matches with the time series data of OH * chemiluminescence. Prakash et al [19] devised control strategies based on optical and acoustic sensor data to mitigate lean blow-out. They used redirection of fuel to a pilot flame as the control action.…”

Section: Introductionmentioning

confidence: 99%

“…However, their study was focused more towards understanding of the basic interaction between the hydrodynamics and thermoacoustics of gas turbine combustors rather than developing strategies for prediction of blow-out. Lieuwen and coworkers [16,17,18,19] used time series data from acoustic and optical sensors for early detection and control of lean blowout in gasand liquid-fuelled combustors. They used several techniques for detecting imminent blow-out.…”

Section: Introductionmentioning

confidence: 99%

A Cross-Wavelet Transform Aided Rule Based Approach for Early Prediction of Lean Blow-out in Swirl-Stabilized Dump Combustor

Dey

Chaudhari

Mukhopadhyay

et al. 2015

International Journal of Spray and Combustion Dynamics

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Lean or ultralean combustion is one of the popular strategies to achieve very low emission levels. However, it is extremely susceptible to lean blow-out (LBO). The present work explores a Crosswavelet transform (XWT) aided rule based scheme for early prediction of lean blowout. XWT can be considered as an advancement of wavelet analysis which gives correlation between two waveforms in time-frequency space. In the present scheme a swirl-stabilized dump combustor is used as a laboratory-scale model of a generic gas turbine combustor with LPG as fuel. Various time series data of CH chemiluminescence signal are recorded for different flame conditions by varying equivalence ratio, flow rate and level of air-fuel premixing. Some features are extracted from the cross-wavelet spectrum of the recorded waveforms and a reference wave. The extracted features are observed to classify the flame condition into three major classes: near LBO, moderate and healthy. Moreover, a Rough Set based technique is also applied on the extracted features to generate a rule base so that it can be fed to a real time controller or expert system to take necessary control action to prevent LBO. Results show that the proposed methodology performs with an acceptable degree of accuracy.

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Acoustic Based Rapid Blowout Mitigation in a Swirl Stabilized Combustor

Cited by 13 publications

References 9 publications

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

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

Dynamics of lean blowout of a swirl-stabilized flame in a gas turbine model combustor

A Cross-Wavelet Transform Aided Rule Based Approach for Early Prediction of Lean Blow-out in Swirl-Stabilized Dump Combustor

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