用机器学习分析燃烧数据，精准识别燃气轮机燃烧状态。
Combustion Condition Identification using a Decision Tree based Machine Learning Algorithm Applied to a Model Can Combustor with High Shear Swirl Injector
- 基于决策树算法分析声压与火焰图像数据。
- 在不同当量比下准确区分稳定与不稳定燃烧状态。
- 适合关注燃烧监测与控制的工程研究人员。
燃烧是燃气轮机的核心过程，高效空气-燃料混合对性能至关重要。高剪切旋流喷嘴可改善燃油雾化与混合，直接影响燃烧效率与排放。然而，在特定条件下，燃烧室可能产生热声不稳定性。本研究针对采用甲烷燃料的单缸燃烧室中反向旋转高剪切旋流喷嘴，利用基于决策树的机器学习算法，通过分析声压信号与高速火焰成像数据，对燃烧状态进行分类。在雷诺数恒定、当量比变化的工况下，燃烧室呈现稳定与不稳定两种状态。通过时序分析提取特征，训练的监督学习模型实现了对燃烧状态的精准分类，验证了在研究参数范围内的有效预测能力。
原文摘要 · Abstract (English)
Combustion is the primary process in gas turbine engines, where there is a need for efficient air-fuel mixing to enhance performance. High-shear swirl injectors are commonly used to improve fuel atomization and mixing, which are key factors in determining combustion efficiency and emissions. However, under certain conditions, combustors can experience thermoacoustic instability. In this study, a decision tree-based machine learning algorithm is used to classify combustion conditions by analyzing acoustic pressure and high-speed flame imaging from a counter-rotating high-shear swirl injector of a single can combustor fueled by methane. With a constant Reynolds number and varying equivalence ratios, the combustor exhibits both stable and unstable states. Characteristic features are extracted from the data using time series analysis, providing insight into combustion dynamics. The trained supervised machine learning model accurately classifies stable and unstable operations, demonstrating effective prediction of combustion conditions within the studied parameter range.
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