Detection of Precursors of Thermoacoustic Instability in a Swirled Combustor Using Chaotic Analysis and Deep Learning Models

Xu, Boqi; Wang, Zhiyu; Zhou, Hongwu; Cao, Wei; Zhong, Zhan; Huang, Weidong; Nie, Wansheng

doi:10.3390/aerospace11060455

Open AccessArticle

Detection of Precursors of Thermoacoustic Instability in a Swirled Combustor Using Chaotic Analysis and Deep Learning Models

by

Boqi Xu

¹,

Zhiyu Wang

¹,

Hongwu Zhou

²,

Wei Cao

¹,

Zhan Zhong

^1,*,

Weidong Huang

^1,* and

Wansheng Nie

¹

Department of Aerospace Science and Technology, Space Engineering University, Beijing 101416, China

²

Wuhan Institute of Marine Electric Propulsion, Wuhan 430064, China

^*

Authors to whom correspondence should be addressed.

Aerospace 2024, 11(6), 455; https://doi.org/10.3390/aerospace11060455

Submission received: 23 April 2024 / Revised: 13 May 2024 / Accepted: 31 May 2024 / Published: 5 June 2024

(This article belongs to the Special Issue Advanced Flow Diagnostic Tools)

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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.

Keywords: combustion instability; thermoacoustic instability; chaotic analysis; deep learning; instability precursors

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MDPI and ACS Style

Xu, B.; Wang, Z.; Zhou, H.; Cao, W.; Zhong, Z.; Huang, W.; Nie, W. Detection of Precursors of Thermoacoustic Instability in a Swirled Combustor Using Chaotic Analysis and Deep Learning Models. Aerospace 2024, 11, 455. https://doi.org/10.3390/aerospace11060455

AMA Style

Xu B, Wang Z, Zhou H, Cao W, Zhong Z, Huang W, Nie W. Detection of Precursors of Thermoacoustic Instability in a Swirled Combustor Using Chaotic Analysis and Deep Learning Models. Aerospace. 2024; 11(6):455. https://doi.org/10.3390/aerospace11060455

Chicago/Turabian Style

Xu, Boqi, Zhiyu Wang, Hongwu Zhou, Wei Cao, Zhan Zhong, Weidong Huang, and Wansheng Nie. 2024. "Detection of Precursors of Thermoacoustic Instability in a Swirled Combustor Using Chaotic Analysis and Deep Learning Models" Aerospace 11, no. 6: 455. https://doi.org/10.3390/aerospace11060455

APA Style

Xu, B., Wang, Z., Zhou, H., Cao, W., Zhong, Z., Huang, W., & Nie, W. (2024). Detection of Precursors of Thermoacoustic Instability in a Swirled Combustor Using Chaotic Analysis and Deep Learning Models. Aerospace, 11(6), 455. https://doi.org/10.3390/aerospace11060455

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Detection of Precursors of Thermoacoustic Instability in a Swirled Combustor Using Chaotic Analysis and Deep Learning Models

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