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

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.High-amplitude pressure oscillations known as combustion instability are an issue for all chemical propulsion systems. Combustion instabilities are particularly problematic for rocket thrust chambers because of their high energy release rates. Especially thermoacoustic oscillations are a major hazard, but difficult to predict. An important question is whether features of combustion noise can be used to construct reliable early warning signals for representative rocket thrust chambers. Among other things, instability precursors are needed for active combustion control systems. In this study, we use recurrence quantification analysis and well-known machine learning models to construct an early warning signal that should be able to predict high-amplitude instabilities 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. We describe the test case in detail and also evaluate other measures like the Hurst exponent.

Machine Learning Methods for the Design and Operation of Liquid Rocket Engines -- Research Activities at the DLR Institute of Space Propulsion

“…Waxenegger-Wilfing et al [15] study the combination of combustion noise features with support vector machines. First, recurrence quantification analysis is used to calculate characteristic combustion features from short-length time series of dynamic pressure sensor data.…”