A Zero-Mach Solver and Reduced Order Acoustic Representations for Modeling and Control of Combustion Instabilities

Moeck, Jonas P.; Scharfenberg, C.; Paschereit, Christian Oliver; Klein, Rupert

doi:10.1007/978-3-642-11735-0_19

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…A promising technique is hybrid CFD/low-order models. [12][13][14][15][16] These models have the advantage that they can account for the complex interaction between heat source, flow and acoustics. Additionally, compared to a fully compressible simulation of the whole thermoacoustic configuration, the computational effort can be significantly reduced.…”

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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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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Reconstruction of the flame nonlinear response using deep learning algorithms

Nan

Yang

et al. 2023

Physics of Fluids

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This paper demonstrates the ability of the neural network trained on frequency-sweeping signals with different amplitudes to reconstruct the flame nonlinear response. The neural network architecture consists of a decreasing sequence increasing dimension (DSID) model and a sequence model; the latter one uses the Long short-term memory (LSTM) and encoder of Transformer, respectively. Results show that the neural network trained using the proposed sweeping method with limited training data can reconstruct realistic signals over the envisaged range of frequencies and amplitudes. The nonlinear flame responses obtained by the neural network are further embedded into the closed-loop thermoacoustic feedback to quantify the reconstruction performance of sequence signals. It is demonstrated that the neural network can accurately capture the evolution of the limit cycle. This paper has also compared the effect of different types and sizes of datasets on trained neural networks model; results show that the models trained with our proposed datasets perform better. For small-size datasets, LSTM performs significantly better than encoder of Transformer. Encoder of Transformer is more suitable for large-size datasets.

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A Zero-Mach Solver and Reduced Order Acoustic Representations for Modeling and Control of Combustion Instabilities

Cited by 2 publications

References 25 publications

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

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

Reconstruction of the flame nonlinear response using deep learning algorithms

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