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.
|Original language||English (US)|
|Number of pages||17|
|Journal||Combustion Theory and Modelling|
|State||Published - Oct 24 2021|
Bibliographical noteKAUST Repository Item: Exported on 2021-10-27
Acknowledged KAUST grant number(s): OSR-2019-CCF-1975-35
Acknowledgements: This work was supported by King Abdullah University of Science and Technology (KAUST) OSR-2019-CCF-1975-35 Subaward Agreement.
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Physics and Astronomy(all)
- Modeling and Simulation
- Chemical Engineering(all)
- Fuel Technology