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递归定量分析在旋流稳定突扩燃烧室贫油熄火早期检测中的应用

Application of recurrence quantification analysis for early detection of lean blowout in a swirl-stabilized dump combustor.

作者信息

De Somnath, Bhattacharya Arijit, Mondal Sirshendu, Mukhopadhyay Achintya, Sen Swarnendu

机构信息

Department of Mechanical Engineering, Jadavpur University, Kolkata 700032, India.

Department of Mechanical Engineering, National Institute of Technology Durgapur, Durgapur 713209, India.

出版信息

Chaos. 2020 Apr;30(4):043115. doi: 10.1063/1.5131231.

DOI:10.1063/1.5131231
PMID:32357653
Abstract

Lean blowout (LBO) is a serious issue in modern gas turbine engines that operate in a lean (premixed) mode to follow the stringent emission norms. When an engine operates with a lean fuel-air mixture, the flame becomes unstable and is at times carried out of the combustion chamber by the unburnt flow. Thus, the sudden loss of the flame, known as lean blowout, leads to fatal accidents in aircrafts and loss of production in power plants. Therefore, an in-depth analysis of lean blowout is necessary as the phenomenon involves complex interactions between flow dynamics and chemical kinetics. For understanding the complex dynamics of this phenomenon, recurrence analysis can be a very useful method. In the current study, we observe a transition to LBO as the global fuel-air ratio is reduced from stoichiometric condition and perform recurrence quantification analysis (RQA) with the CH chemiluminescence data obtained experimentally. The extent of fuel-air mixing is varied with an objective of developing some robust early predictors of LBO that would work over a wide range of premixing. We find some RQA measures, such as determinism, laminarity, and trapping time, which show distinctive signature toward LBO and thereby can be used as early predictors of LBO for both premixed and partially premixed flames. Our analysis shows that the computational time for laminarity and trapping time is relatively less. However, computational time for those measures depends upon the dynamics of the combustor, size of the data taken, and choice of recurrence threshold.

摘要

贫油熄火(LBO)是现代燃气轮机发动机中的一个严重问题,这些发动机以贫油(预混)模式运行以符合严格的排放规范。当发动机使用贫油燃料空气混合物运行时,火焰会变得不稳定,有时会被未燃烧的气流带出燃烧室。因此,火焰的突然熄灭,即贫油熄火,会导致飞机发生致命事故以及发电厂停产。所以,由于该现象涉及流动动力学和化学动力学之间的复杂相互作用,对贫油熄火进行深入分析是必要的。为了理解这一现象的复杂动力学,递归分析可能是一种非常有用的方法。在当前的研究中,我们观察到随着全局燃料空气比从化学计量比条件降低时向贫油熄火的转变,并使用实验获得的CH化学发光数据进行递归定量分析(RQA)。改变燃料空气混合程度的目的是开发一些强大的贫油熄火早期预测指标,这些指标将在广泛的预混范围内起作用。我们发现一些RQA指标,如确定性、层流性和捕获时间,它们对贫油熄火表现出独特的特征,因此可作为预混火焰和部分预混火焰贫油熄火的早期预测指标。我们的分析表明,层流性和捕获时间的计算时间相对较短。然而,这些指标的计算时间取决于燃烧室的动力学、所取数据的大小以及递归阈值的选择。

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