Deep Neural Network-Based Generation of Planar CH Distribution through Flame Chemiluminescence in Premixed Turbulent Flame

Han, Lei; Gao, Qiang; Dayuan, Zhang; Feng, Zhanyu; Sun, Zhiwei; Li, Bo; Li, Zhongshan

doi:10.1016/j.egyai.2022.100221

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Relevant research aimed at the cross-disciplinary area of energy and artificial intelligence has obtained many meaningful results, for example, reconstructing PLIF images using chemiluminescence images [17], and improving the resolution ratio of spatiotemporal evolution based on the frame interpolation method and an accurate prediction of the combustion state [18]. Aiming to investigate local flame structure features, aside from using traditional methods such as flame geometric and intensity features, researchers have conducted extensive work on the intelligent processing of flame images and pattern recognition of combustion field images, especially the combination of big data, machine learning, and artificial intelligence [19].…”

Section: Introductionmentioning

confidence: 99%

A Neural Network-Based Flame Structure Feature Extraction Method for the Lean Blowout Recognition

Yan,

Cao,

Peng

et al. 2024

Aerospace

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A flame’s structural feature is a crucial parameter required to comprehensively understand the interaction between turbulence and flames. The generation and evolution processes of the structure feature have rarely been investigated in lean blowout (LBO) flame instability states. Hence, to understand the precursor features of the LBO flame, this work employed high-speed OH-PLIF measurements to acquire time-series LBO flame images and developed a novel feature extraction method based on a deep neural network to quantify the LBO features in real time. Meanwhile, we proposed a deep neural network segmentation method based on a tri-map called the Fire-MatteFormer, and conducted a statistical analysis on flame surface features, primarily holes. The statistical analysis results determined the relationship between the life cycle of holes (from generation to disappearance) and their area, perimeter, and total number. The trained Fire-MatteFormer model was found to represent a viable method for determining flame features in the detection of incipient LBO instability conditions. Overall, the model shows significant promise in ascertaining local flame structure features.

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Section: Introductionmentioning

confidence: 99%

A Neural Network-Based Flame Structure Feature Extraction Method for the Lean Blowout Recognition

Yan,

Cao,

Peng

et al. 2024

Aerospace

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“…Thanks to neural networks, CFD super-resolution is becoming more efficient and computationally affordable [9]. In combustion, they enable the prediction of an evolving 3D flame front recreated from experimental images [10] and infer turbulent flame front structures from line-of-sight chemiluminescence without the need for lasers [11]. Finally, another branch of thermoacoustics application focuses on identifying precursors for instabilities [12][13][14].…”

Section: Introductionmentioning

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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Reconstructing temperature fields from OH distribution and soot volume fraction in turbulent flames using an artificial neural network

Nie,

Zhang,

Dong

et al. 2024

Combustion and Flame

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Deep Neural Network-Based Generation of Planar CH Distribution through Flame Chemiluminescence in Premixed Turbulent Flame

Cited by 4 publications

References 26 publications

A Neural Network-Based Flame Structure Feature Extraction Method for the Lean Blowout Recognition

A Neural Network-Based Flame Structure Feature Extraction Method for the Lean Blowout Recognition

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

Reconstructing temperature fields from OH distribution and soot volume fraction in turbulent flames using an artificial neural network

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