Machine Learning in Thermoacoustics

Thermoacoustic oscillations are a perpetual problem in aircraft and rocket engines. Unacceptably large oscillations often appear during full-scale engine tests, despite being absent during part-scale tests, leading to costly re-designs. The aim of this project is to combine experiments and machine learning so that a computer can build up a data-driven model of a rocket engine and use it to design out thermoacoustic oscillations.