Local combustion regime identification using machine learning

Embargo End Date
2022-10-24

Type
Article

Authors
Malpica Galassi, Riccardo
Ciottoli, Pietro P.
Valorani, Mauro
Im, Hong G.

KAUST Department
Clean Combustion Research Center
Computational Reacting Flow Laboratory (CRFL)
Mechanical Engineering Program
Physical Science and Engineering (PSE) Division

KAUST Grant Number
OSR-2019-CCF-1975-35

Online Publication Date
2021-10-24

Print Publication Date
2022-01-02

Date
2021-10-24

Submitted Date
2020-10-07

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.

Citation
Malpica Galassi, R., Ciottoli, P. P., Valorani, M., & Im, H. G. (2021). Local combustion regime identification using machine learning. Combustion Theory and Modelling, 1–17. doi:10.1080/13647830.2021.1991595

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

Publisher
Informa UK Limited

Journal
Combustion Theory and Modelling

DOI
10.1080/13647830.2021.1991595

Additional Links
https://www.tandfonline.com/doi/full/10.1080/13647830.2021.1991595https://iris.uniroma1.it/bitstream/11573/1621281/1/Galassi_Preprint_Local-combustion_2021.pdf

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