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Stochastic Low-order Modelling of Hydrogen Autoignition in a Turbulent Non-premixed Flow

Abstract

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 Computational Fluid Dynamics (CFD)-based surrogate modelling strategy is presented here for fast and accurate predictions of the stochastic autoignition behaviour of a 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 a Large Eddy Simulation (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. © 2022 The Authors. Published by Elsevier Inc. on behalf of The Combustion Institute.

Funding source: The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under the CoEC project, grant agreement No 952181, and from Siemens Energy.
Related subjects: Safety
Countries: Belgium ; United Kingdom
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2022-07-12
2022-10-06
http://instance.metastore.ingenta.com/content/journal3789
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