Castellanos, Luisa
[UCL]
S. M. Freitas, Rodolfo
[Universite Libre de Bruxelles]
Parente, Alessandro
[Universite Libre de Bruxelles]
Contino, Francesco
[UCL]
The modeling of chemical kinetics holds many challenges, as well as a necessity for more efficient modeling techniques, together with dimensionality reduction techniques. This work studies the application of time-lag auto-encoders for the analysis of combustion chemistry kinetics. Such a technique allows a better reconstruction of the thermochemical temporal advancement in relation to traditional reduction techniques (principal component analysis) while applying a potential denoising operation. Moreover, the reduced manifolds or latencies are provided with physical meaning, which further analysis gives insight into key chemical reactions and interactions between chemical species, allowing for a deeper understanding of the chemical mechanism itself.
Bibliographic reference |
Castellanos, Luisa ; S. M. Freitas, Rodolfo ; Parente, Alessandro ; Contino, Francesco. Deep learning dynamical latencies for the analysis and reduction of combustion chemistry kinetics. In: Physics of Fluids, Vol. 35, no.10, p. 13 (2023) |
Permanent URL |
http://hdl.handle.net/2078/284099 |