We present a two-step optimization (TSO) framework, which uses the pressure data of an
unstable combustion process to estimate the complex-valued flame transfer function (FTF) …

In this present study, gene expression programing (GEP) has been used for training a model
for the subgrid scale (SGS) scalar dissipation rate (SDR) for a large range of filter widths …

We use Bayesian inference, accelerated by adjoint methods, to construct a quantitatively
accurate model of the thermoacoustic behaviour of a conical flame in a duct. We first perform …

Lean premixed combustion is the state-of-the-art technology to achieve ultra low NOx
emissions in stationary gas turbines. However, lean premixed flames are susceptible to …

We use approximate Bayesian inference, accelerated by adjoint methods, to construct a
quantitatively accurate model of the thermoacoustic behaviour of a turbulent conical flame in …

We assimilate experimental data from non-reacting flow in the SCARLET (SCaled Acoustic
Rig for Low Emission Technologies) test rig using physics-based Bayesian inference. We …

Thermoacoustic oscillations arise because of the interaction between acoustic waves inside
a duct or a combustion chamber, and heat release rate oscillations at the flame or heater …

The stability of thermoacoustic systems is often regulated by the time delay between
acoustic perturbations and corresponding heat release fluctuations. An accurate estimate of …

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 …

The flame transfer function (FTF) of an industrial lean-burn fuel injector has been computed
using large eddy simulation (LES) and compared to experimental measurements using the …