Dynamical Mode Recognition of Coupled Flame Oscillators by Supervised and Unsupervised Learning Approaches

TL;DR

This paper presents data-driven supervised and unsupervised learning methods using variational autoencoders for recognizing dynamical modes in coupled flame oscillators, aiding understanding of combustion instability.

Contribution

It introduces a VAE-based framework combined with Wasserstein-distance classifier and GMM-DTWC for mode recognition in flame oscillators, addressing unlabeled and labeled data scenarios.

Findings

VAE effectively reduces dimensionality of flame oscillation data.

Proposed classifiers outperform conventional methods in mode recognition.

Framework shows potential for complex combustion system analysis.

Abstract

Combustion instability in gas turbines and rocket engines, as one of the most challenging problems in combustion research, arises from the complex interactions among flames, which are also influenced by chemical reactions, heat and mass transfer, and acoustics. Identifying and understanding combustion instability is essential to ensure the safe and reliable operation of many combustion systems, where exploring and classifying the dynamical behaviors of complex flame systems is a core take. To facilitate fundamental studies, the present work concerns dynamical mode recognition of coupled flame oscillators made of flickering buoyant diffusion flames, which have gained increasing attention in recent years but are not sufficiently understood. The time series data of flame oscillators are generated by fully validated reacting flow simulations. Due to limitations of expertise-based models, a data-driven approach is adopted. In this study, a nonlinear dimensional reduction model of variational autoencoder (VAE) is used to project the simulation data onto a 2-dimensional latent space. Based on the phase trajectories in latent space, both supervised and unsupervised classifiers are proposed for datasets with well known labeling and without, respectively. For labeled datasets, we establish the Wasserstein-distance-based classifier (WDC) for mode recognition; for unlabeled datasets, we develop a novel unsupervised classifier (GMM-DTWC) combining dynamic time warping (DTW) and Gaussian mixture model (GMM). Through comparing with conventional approaches for dimensionality reduction and classification, the proposed supervised and unsupervised VAE-based approaches exhibit a prominent performance for distinguishing dynamical modes, implying their potential extension to dynamical mode recognition of complex combustion problems.