Combustion Condition Identification using a Decision Tree based Machine Learning Algorithm Applied to a Model Can Combustor with High Shear Swirl Injector

TL;DR

This paper presents a decision tree machine learning approach to classify stable and unstable combustion conditions in a model gas turbine combustor using acoustic and flame imaging data, aiding in real-time combustion monitoring.

Contribution

It introduces a novel application of decision tree algorithms for classifying combustion stability based on acoustic and flame imaging data in a high-shear swirl injector system.

Findings

Accurately classifies stable and unstable combustion states.

Effective prediction of combustion conditions within the studied parameters.

Demonstrates potential for real-time combustion monitoring.

Abstract

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.