Dynamical mode recognition of coupled flame oscillators by supervised and unsupervised learning approaches

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Knowledge-Based Systems Pub Date : 2024-10-28 DOI:10.1016/j.knosys.2024.112683
Weiming Xu , Tao Yang , Peng Zhang
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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 influenced by chemical reactions, heat and mass transfer, and acoustics. Identifying and understanding combustion instability is essential for ensuring the safe and reliable operation of many combustion systems, where exploring and classifying the dynamical behaviors of complex flame systems is a core task. To facilitate fundamental studies, the present work concerned 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 were generated through fully validated reacting flow simulations. Due to the limitations of expertise-based models, a data-driven approach was adopted. In this study, a nonlinear dimensional reduction model of variational autoencoder (VAE) was used to project the high dimensional data onto a 2-dimensional latent space. Based on phase trajectories in the latent space, both supervised and unsupervised classifiers were proposed for datasets with and without well-known labeling, respectively. For labeled datasets, we established the Wasserstein-distance-based classifier (WDC) for mode recognition; for unlabeled datasets, we developed a novel unsupervised classifier (GMM-DTW) 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 across seven assessment metrics for distinguishing dynamical modes, implying their potential extension to dynamical mode recognition in complex combustion problems.
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通过监督和非监督学习方法识别耦合火焰振荡器的动态模式
燃气轮机和火箭发动机中的燃烧不稳定性是燃烧研究中最具挑战性的问题之一,它源于受化学反应、热量和质量传递以及声学影响的火焰之间复杂的相互作用。识别和理解燃烧不稳定性对于确保许多燃烧系统的安全可靠运行至关重要,而探索复杂火焰系统的动力学行为并对其进行分类是一项核心任务。为了促进基础研究,本研究涉及由闪烁浮力扩散火焰构成的耦合火焰振荡器的动力学模式识别,近年来,这种火焰振荡器受到越来越多的关注,但人们对它的理解还不够充分。火焰振荡器的时间序列数据是通过充分验证的反应流模拟生成的。由于基于专业知识的模型存在局限性,因此采用了数据驱动的方法。本研究采用变异自动编码器(VAE)的非线性降维模型,将高维数据投射到二维潜空间。根据潜空间中的相位轨迹,分别针对有知名标签和无知名标签的数据集提出了有监督和无监督分类器。对于有标记的数据集,我们建立了基于 Wasserstein-distance 的分类器(WDC)用于模式识别;对于无标记的数据集,我们开发了一种结合动态时间扭曲(DTW)和高斯混合模型(GMM)的新型无监督分类器(GMM-DTW)。通过与传统的降维和分类方法比较,所提出的基于 VAE 的有监督和无监督方法在区分动态模式的七个评估指标方面表现突出,这意味着它们有可能扩展到复杂燃烧问题中的动态模式识别。
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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