From abstraction to design: Interpretable tree-based machine learning for stable thermoacoustic system layout

Abstract

Thermoacoustic instabilities are a long-standing problem. Both academia and industry researched intensively this problem, but even today, no guidelines (or design rules) exist to layout a stable combustor. It is often unclear how the system parameters, individually, and in combination, contribute to the thermoacoustic stability. Knowing these relationships would provide useful insight into the problem domain and accelerate the design process.