Characterizing premixed laminar flame–acoustics nonlinear interaction

Zhang, Zhiguo; Guan, Di; Zheng, Youqu; Li, Guoneng

doi:10.1016/j.enconman.2015.03.095

Cited by 21 publications

(6 citation statements)

References 38 publications

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“…Thus it is important to gain insight on the onset of thermoacoustic instability [12,13,2] and to develop mitigation/-control approaches. Over last few decades, thermoacoustic instability has been intensively studied [14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Transient growth of acoustical energy associated with mitigating thermoacoustic oscillations

Zhao

Yang

et al. 2016

Applied Energy

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Section: Introductionmentioning

confidence: 99%

Transient growth of acoustical energy associated with mitigating thermoacoustic oscillations

Zhao

Yang

et al. 2016

Applied Energy

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“…We use a phase-shifter i.e. a control scheme to eliminate the limit cycle thermoacoustic oscillations [46,47]. And then a loud enough white noise signal V(x) to the loudspeaker is added to the thermoacoustic system.…”

Section: Measurement Of the Transfer Function Of A Standing-wave Thermentioning

confidence: 99%

Theoretical and experimental investigation of thermoacoustics transfer function

et al. 2015

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“…18,20 Selimefendigil et al [21][22][23] used the method to identify nonlinear low-order models for a cylinder in pulsating crossflow. Zhang et al 24 used Hammerstein-Wiener models to deduce nonlinear loworder models from a G-equation solver.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty encountered when modelling self-excited thermoacoustic oscillations with artificial neural networks

Jaensch

Polifke

2017

International Journal of Spray and Combustion Dynamics

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Artificial neural networks are a popular nonlinear model structure and are known to be able to describe complex nonlinear phenomena. This article investigates the capability of artificial neural networks to serve as a basis for deducing nonlinear low-order models of the dynamics of a laminar flame from a Computational Fluid Dynamics (CFD) simulation. The methodology can be interpreted as an extension of the CFD/system identification approach: a CFD simulation of the flame is perturbed with a broadband, high-amplitude signal and the resulting fluctuations of the global heat release rate and of the reference velocity are recorded. Thereafter, an artificial neural network is identified based on the time series collected. Five data sets that differ in amplitude distribution and length were generated for the present study. Based on each of these data sets, a parameter study was conducted by varying the structure of the artificial neural network. A general fit-value criterion is applied and the 10 artificial neural networks with the highest fit values are selected. Comparing of these 10 artificial neural networks allows to obtain information on the uncertainty encountered. It is found that the methodology allows to capture the forced response of the flame reasonably well. The validation against the forced response, however, depends strongly on the forcing signal used. Therefore, an additional validation criterion is investigated. The artificial neural networks are coupled with a thermoacoustic network model. This allows to model selfexcited thermoacoustic oscillations. If the training time series are sufficiently long, this coupled model allows to predict the trend of the root mean square values of fluctuations of the global heat release rate. However, the prediction of the maximal value of the fluctuation amplitude is poor. Another drawback found is that even if very long-time series are available, the behaviour of artificial neural networks cannot be guaranteed. It is concluded that more sophisticated nonlinear low-order models are necessary.

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Characterizing premixed laminar flame–acoustics nonlinear interaction

Cited by 21 publications

References 38 publications

Transient growth of acoustical energy associated with mitigating thermoacoustic oscillations

Transient growth of acoustical energy associated with mitigating thermoacoustic oscillations

Theoretical and experimental investigation of thermoacoustics transfer function

Uncertainty encountered when modelling self-excited thermoacoustic oscillations with artificial neural networks

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