Engineering

Aerospace engineering

Computer science

Acoustics

Thermoacoustics

Physics

Organic chemistry

Instability

Combustion

Chemistry

Mechanics

Physical Review Applied

Early detection of thermoacoustic instabilities is of interest to both applied physicists and engineers, to avoid resonance leading to self-destruction of gas-based engines and turbines. This study shows how a combination of complex-network physics and machine learning can be used to detect a precursor of thermoacoustic instabilities, which can help to prevent the onset of a potentially destructive combustion-driven instability.

Early Detection of Thermoacoustic Combustion Instability Using a Methodology Combining Complex Networks and Machine Learning