English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Combustion instabilities are particularly problematic for rocket thrustchambers because of their high energy release rates and their operation closeto the structural limits. In the last decades, progress has been made inpredicting high amplitude combustion instabilities but still, no reliableprediction ability is given. Reliable early warning signals are the mainrequirement for active combustion control systems. In this paper, we present adata-driven method for the early detection of thermoacoustic instabilities.Recurrence quantification analysis is used to calculate characteristiccombustion features from short-length time series of dynamic pressure sensordata. Features like the recurrence rate are used to train support vectormachines to detect the onset of an instability a few hundred milliseconds inadvance. The performance of the proposed method is investigated on experimentaldata from a representative LOX/H$_2$ research thrust chamber. In most cases,the method is able to timely predict two types of thermoacoustic instabilitieson test data not used for training. The results are compared withstate-of-the-art early warning indicators.

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