Distributed transfer function approach for one-dimensional thermoacoustic combustor models

Yoon, Myung-Gon

doi:10.1080/13647830.2022.2156930

Cited by 2 publications

(1 citation statement)

References 54 publications

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“…Several other models can predict the global FDF, but to the best of the authors' knowledge, attempts with an accurate capture of the spatial dynamics have been limited. Some examples of local FDF calculations include [36] and [37], with the latter relying on performing a sweep of CFD simulations for each frequency of interest. However, the accurate prediction of local FDFs is crucial for modeling acoustically noncompact flames and correct entropy waves analysis [26,27].…”

Section: Flame Describing Functionmentioning

confidence: 99%

Spatially Resolved Modeling of the Nonlinear Dynamics of a Laminar Premixed Flame With a Multilayer Perceptron—Convolution Autoencoder Network

Rywik,

Zimmermann,

Eder

et al. 2024

Journal of Engineering for Gas Turbines and Power

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This work presents a multilayer perceptron-convolutional autoencoder (MLP-CAE) neural network, which accurately predicts the two-dimensional flame dynamics of an acoustically excited premixed laminar flame. The architecture maps the acoustic perturbation time series to a heat release rate field, capturing flame lengths and shapes. This extends previous neural network models, which predicted only the field-integrated value. The MLP-CAE comprises two sub-models: an MLP and a CAE. The idea behind the CAE network is to find a lower dimensional latent space of the heat release rate field. The MLP is responsible for modeling the flame dynamics by transforming the acoustic forcing signal into this latent space, enabling the decoder to produce the flow field distributions. To train the MLP-CAE, computational fluid dynamics (CFD) flame simulations with a broadband acoustic forcing were used. Its normalized amplitude was set to 0.5 and 1.0, ensuring a nonlinear flame response. The network was found to accurately predict the perturbed flame shapes. Additionally, it conserved the correct frequency response as verified by the global and local flame describing functions. The MLP-CAE provides a building block towards a potential shift away from a '0D' flame analysis with the acoustic compactness assumption. Combined with an acoustic network, the generated flame fields could provide more physical insight in the thermoacoustic dynamics. Those capabilities do not come at an additional significant computational cost, as even the previous nonspatial flame models had to train on the CFD data, which included field distributions.

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Section: Flame Describing Functionmentioning

confidence: 99%

Spatially Resolved Modeling of the Nonlinear Dynamics of a Laminar Premixed Flame With a Multilayer Perceptron—Convolution Autoencoder Network

Rywik,

Zimmermann,

Eder

et al. 2024

Journal of Engineering for Gas Turbines and Power

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Spatially Resolved Modeling of the Nonlinear Dynamics of a Laminar Premixed Flame With a Multilayer Perceptron - Convolution Autoencoder Network

Rywik,

Zimmermann,

Eder

et al. 2023

Volume 3A: Combustion, Fuels, and Emissions

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This work presents a multilayer perceptron-convolutional autoencoder (MLP-CAE) neural network model, which accurately predicts the two-dimensional flame field dynamics of an acoustically excited premixed laminar flame. The obtained architecture maps the acoustic perturbation time series to a spatially distributed heat release rate field, capturing the flame lengths and shapes. This extends to previous neural network models, which predicted only the field-integrated value of the heat release rate. The MLP-CAE comprises two sub-models: a fully connected MLP and a CAE. The key idea behind the CAE network is to find a lower dimensional latent space representation of the heat release rate field. The MLP is responsible for modeling the flame dynamics by transforming the acoustic forcing signal into this latent space, enabling the decoder to produce the flow field distributions. To train the MLP-CAE, computational fluid dynamics (CFD) flame simulations with a broadband acoustic forcing were used. Its normalized amplitude was set to 0.5 and 1.0, resulting in a nonlinear flame response. The network was found to accurately predict the perturbed flame shapes — both under broadband and harmonic forcing. Additionally, it conserved the correct frequency response characteristics as verified by the global and local flame describing functions. The MLP-CAE provides a building block towards a potential shift away from a purely ‘0D’ analysis with the assumption of acoustic compactness of the flame. When combined with an acoustic network, the generated flame fields could provide more physical insight in the thermoacoustic dynamics of combustion chambers. Those capabilities do not come at an additional significant computational cost, as even the previous nonspatial flame models had to train on the CFD data, which readily included field distributions.

show abstract

Distributed transfer function approach for one-dimensional thermoacoustic combustor models

Cited by 2 publications

References 54 publications

Spatially Resolved Modeling of the Nonlinear Dynamics of a Laminar Premixed Flame With a Multilayer Perceptron—Convolution Autoencoder Network

Spatially Resolved Modeling of the Nonlinear Dynamics of a Laminar Premixed Flame With a Multilayer Perceptron—Convolution Autoencoder Network

Spatially Resolved Modeling of the Nonlinear Dynamics of a Laminar Premixed Flame With a Multilayer Perceptron - Convolution Autoencoder Network

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