KAUST DepartmentPhysical Science and Engineering (PSE) Division
Mechanical Engineering Program
Clean Combustion Research Center
KAUST Grant NumberOSR-2019-CCF-1975-35
Embargo End Date2022-10-24
Permanent link to this recordhttp://hdl.handle.net/10754/672962
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AbstractA 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.
CitationMalpica 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
SponsorsThis work was supported by King Abdullah University of Science and Technology (KAUST) OSR-2019-CCF-1975-35 Subaward Agreement.
PublisherInforma UK Limited
JournalCombustion Theory and Modelling