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- Title
Detection of Precursors of Thermoacoustic Instability in a Swirled Combustor Using Chaotic Analysis and Deep Learning Models.
- Authors
Xu, Boqi; Wang, Zhiyu; Zhou, Hongwu; Cao, Wei; Zhong, Zhan; Huang, Weidong; Nie, Wansheng
- Abstract
This paper investigates the role of chaotic analysis and deep learning models in combustion instability predictions. To detect the precursors of impending thermoacoustic instability (TAI) in a swirled combustor with various fuel injection strategies, a data-driven framework is proposed in this study. Based on chaotic analysis, a recurrence matrix derived from combustion system is used in deep learning models, which are able to detect precursors of TAI. More specifically, the ResNet-18 network model is trained to predict the proximity of unstable operation conditions when the combustion system is still stable. The proposed framework achieved state-of-the-art 91.06% accuracy in prediction performance. The framework has potential for practical applications to avoid an unstable operation domain in active combustion control systems and, thus, can offer on-line information on the margin of the combustion instability.
- Subjects
DEEP learning; COMBUSTION
- Publication
Aerospace (MDPI Publishing), 2024, Vol 11, Issue 6, p455
- ISSN
2226-4310
- Publication type
Article
- DOI
10.3390/aerospace11060455