Local combustion regime identification using machine learning

Abstract

A new combustion regime identification methodology using the neural networks as supervised classifiers is proposed and validated. As a first proof of concept, a binary classifier is trained with labelled thermochemical states obtained as solutions of prototypical one-dimensional models representing premixed and nonpremixed regimes. The trained classifier is then used to associate the regime to any given thermochemical state originating from a multi-dimensional reacting flow simulation that shares similar operating conditions with the training problems. The classification requires local information only, i.e. no gradients are required, and operates on reduced-dimension thermochemical states, in order to cope with experimental data as well. The validity of the approach is assessed by employing a two-dimensional laminar edge flame data as a canonical configuration exhibiting multi-regime combustion behaviour. The method is readily extendable to additional classes to identify criticality phenomena, such as local extinction and re-ignition. It is anticipated that the proposed classifier tool will be useful in the development of turbulent multi-regime combustion closure models in large scale simulations.

Keywords:

• Gradient-free regime classification
• neural networks
• multi-regime reacting flows
• tribrachial flames

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by King Abdullah University of Science and Technology (KAUST) OSR-2019-CCF-1975-35 Subaward Agreement.