Early Detection of Thermoacoustic Instabilities in a Cryogenic Rocket Thrust Chamber using Combustion Noise Features and Machine Learning

TL;DR

This paper introduces a machine learning-based approach using recurrence analysis to predict thermoacoustic instabilities in cryogenic rocket chambers, providing early warnings crucial for active control.

Contribution

It presents a novel data-driven method combining recurrence quantification and support vector machines for early detection of combustion instabilities in rocket engines.

Findings

Successfully predicts instabilities hundreds of milliseconds in advance

Achieves reliable detection on experimental rocket chamber data

Outperforms existing early warning indicators

Abstract

Combustion instabilities are particularly problematic for rocket thrust chambers because of their high energy release rates and their operation close to the structural limits. In the last decades, progress has been made in predicting high amplitude combustion instabilities but still, no reliable prediction ability is given. Reliable early warning signals are the main requirement for active combustion control systems. In this paper, we present a data-driven method for the early detection of thermoacoustic instabilities. Recurrence quantification analysis is used to calculate characteristic combustion features from short-length time series of dynamic pressure sensor data. Features like the recurrence rate are used to train support vector machines to detect the onset of an instability a few hundred milliseconds in advance. The performance of the proposed method is investigated on experimental data from a representative LOX/H$_2$ research thrust chamber. In most cases, the method is able to timely predict two types of thermoacoustic instabilities on test data not used for training. The results are compared with state-of-the-art early warning indicators.