Autoignition risk in initially non-premixed flowing systems, such as premixing ducts, must be assessed to help the
development of low-NOx systems and hydrogen combustors. Such situations may involve randomly fluctuating
inlet conditions that are challenging to model in conventional mixture-fraction-based approaches. A modelling
strategy is presented here featuring a joint CFD and surrogate modelling for fast and accurate prediction of the
stochastic autoignition behaviour in an experiment with continuous hydrogen flow in a hot air turbulent co-flow.
The variability of three input parameters, i.e., inlet fuel and air temperatures and average wall temperature, is
first sampled via a space-filling design. For each sampled set of conditions, the CFD modelling of the flame is
performed via the Incompletely Stirred Reactor Network (ISRN) approach, which solves the reacting flow governing
equations in post-processing on top of an LES of the inert hydrogen plume. An accurate surrogate model,
namely a Gaussian Process, is then trained on the ISRN simulations of the burner, and the final quantification of
the variability of autoignition locations is achieved by querying the surrogate model via Monte Carlo sampling of
the random input quantities. The results are in agreement with the observed statistics of the autoignition locations.
The methodology adopted in this work can be used effectively to quantify the impact of fluctuations and assist the
design of practical combustion systems.EU, Grant Agreement No 952181, project "COEC
