Machine Learning for Thermoacoustics

Abstract

This chapter demonstrates three promising ways to combine machine learning with physics-based modelling in order to model, forecast, and avoid thermoacoustic instability. The first method assimilates experimental data into candidate physics-based models and is demonstrated on a Rijke tube. This uses Bayesian inference to select the most likely model. This turns qualitatively-accurate models into quantitatively-accurate models that can extrapolate, which can be combined powerfully with automated design. The second method assimilates experimental data into level set numerical simulations of a premixed bunsen flame and a bluff-body stabilized flame. This uses either an Ensemble Kalman filter, which requires no prior simulation but is slow, or a Bayesian Neural Network Ensemble, which is fast but requires prior simulation. This method deduces the simulations’ parameters that best reproduce the data and quantifies their uncertainties. The third method recognises precursors of thermoacoustic instability from pressure measurements. It is demonstrated on a turbulent bunsen flame, an industrial fuel spray nozzle, and full scale aeroplane engines. With this method, Bayesian Neural Network Ensembles determine how far each system is from instability. The trained BayNNEs out-perform physics-based methods on a given system. This method will be useful for practical avoidance of thermoacoustic instability.