Latent space-based machine learning prediction of coupled flame-flow fields in a hydrogen-enriched syngas combustor

In this study, we propose an innovative latent space-based machine learning model to predict coupled flame-flow fields across various combustion states. This model consists of two main components: a local convolutional autoencoder (LCAE) with an encoder to establish a latent space and a decoder to restore predicted features to their original physical dimensions, and a long short-term memory (LSTM) model to forecast the spatiotemporal behavior of features in this latent space. The model is trained and validated using flame-flow images simultaneously measured in a gas turbine model combustor, utilizing synchronized stereoscopic particle image velocimetry (S-PIV) and planar laser induced fluorescence of the hydroxyl radical (OH-PLIF). Our model effectively extracts large-scale structures from the coupled flame-flow measurements, even in the presence of aleatoric noise, and demonstrates satisfactory prediction and generalization capabilities, as verified through out-of-sample testing. By establishing a common latent space for the coupled flame-flow fields, we reveal inherent similarities between flame and flow fields in a low-dimensional feature space, suggesting the potential for holistic understanding and modeling of their behavior in such a space. Additionally, our model predicts rapidly and reliably, requiring only 403 ms for out-of-sample predictions, with no error accumulation across 499 predictions. This paves the way for early warning of impending undesirable combustion states in future applications.