利用混沌分析和深度学习模型检测漩涡式燃烧器中的热声不稳定性前兆

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Boqi Xu, Zhiyu Wang, Hongwu Zhou, Wei Cao, Zhan Zhong, Weidong Huang, Wansheng Nie
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引用次数: 0

摘要

本文研究了混沌分析和深度学习模型在燃烧不稳定性预测中的作用。为了检测采用不同燃料喷射策略的漩涡燃烧器中即将发生的热声不稳定性(TAI)的前兆,本研究提出了一个数据驱动框架。在混沌分析的基础上,将燃烧系统衍生的递归矩阵用于深度学习模型,该模型能够检测 TAI 的前兆。更具体地说,ResNet-18 网络模型经过训练,可以在燃烧系统仍然稳定时预测不稳定运行条件的临近程度。所提出的框架在预测性能方面达到了最先进的 91.06% 的准确率。该框架具有实际应用的潜力,可避免主动燃烧控制系统出现不稳定运行域,从而提供有关燃烧不稳定裕度的在线信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of Precursors of Thermoacoustic Instability in a Swirled Combustor Using Chaotic Analysis and Deep Learning Models
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.
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
期刊介绍: ACS Applied Bio Materials is an interdisciplinary journal publishing original research covering all aspects of biomaterials and biointerfaces including and beyond the traditional biosensing, biomedical and therapeutic applications. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important bio applications. The journal is specifically interested in work that addresses the relationship between structure and function and assesses the stability and degradation of materials under relevant environmental and biological conditions.
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