Dynamical Mode Recognition of Coupled Flame Oscillators by Supervised and Unsupervised Learning Approaches
arxiv(2024)
摘要
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.
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