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

Abstract

Abstract This work presents a multilayer perceptron-convolutional auto-encoder (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 into 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 submodels: 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 toward 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 into the thermoacoustic dynamics. Those capabilities do not come at an additional significant computational cost, as even previous nonspatial flame models had to train on the CFD data, which included field distributions.