Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2410.09901 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866929540577624064 |
|---|---|
| author | Fang, Shen Zhang, Siyi Li, Zeyu Fu, Qingfei Zhou, Chong-Wen Hana, Wang Yang, Lijun |
| author_facet | Fang, Shen Zhang, Siyi Li, Zeyu Fu, Qingfei Zhou, Chong-Wen Hana, Wang Yang, Lijun |
| contents | Reduction of detailed chemical reaction mechanisms is one of the key methods for mitigating the computational cost of reactive flow simulations. Exploitation of species and elementary reaction sparsity ensures the compactness of the reduced mechanisms. In this work, we propose a novel sparse statistical learning approach for chemical reaction mechanism reduction. Specifically, the reduced mechanism is learned to explicitly reproduce the dynamical evolution of detailed chemical kinetics, while constraining on the sparsity of the reduced reactions at the same time. Compact reduced mechanisms are be achieved as the collection of species that participate in the identified important reactions. We validate our approach by reducing oxidation mechanisms for $n$-heptane (194 species) and 1,3-butadiene (581 species). The results demonstrate that the reduced mechanisms show accurate predictions for the ignition delay times, laminar flame speeds, species mole fraction profiles and turbulence-chemistry interactions across a wide range of operating conditions. Comparative analysis with directed relation graph (DRG)-based methods and the state-of-the-art (SOTA) methods reveals that our sparse learning approach produces reduced mechanisms with fewer species while maintaining the same error limits. The advantages are particularly evident for detailed mechanisms with a larger number of species and reactions. The sparse learning strategy shows significant potential in achieving more substantial reductions in complex chemical reaction mechanisms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_09901 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A data-driven sparse learning approach to reduce chemical reaction mechanisms Fang, Shen Zhang, Siyi Li, Zeyu Fu, Qingfei Zhou, Chong-Wen Hana, Wang Yang, Lijun Chemical Physics Reduction of detailed chemical reaction mechanisms is one of the key methods for mitigating the computational cost of reactive flow simulations. Exploitation of species and elementary reaction sparsity ensures the compactness of the reduced mechanisms. In this work, we propose a novel sparse statistical learning approach for chemical reaction mechanism reduction. Specifically, the reduced mechanism is learned to explicitly reproduce the dynamical evolution of detailed chemical kinetics, while constraining on the sparsity of the reduced reactions at the same time. Compact reduced mechanisms are be achieved as the collection of species that participate in the identified important reactions. We validate our approach by reducing oxidation mechanisms for $n$-heptane (194 species) and 1,3-butadiene (581 species). The results demonstrate that the reduced mechanisms show accurate predictions for the ignition delay times, laminar flame speeds, species mole fraction profiles and turbulence-chemistry interactions across a wide range of operating conditions. Comparative analysis with directed relation graph (DRG)-based methods and the state-of-the-art (SOTA) methods reveals that our sparse learning approach produces reduced mechanisms with fewer species while maintaining the same error limits. The advantages are particularly evident for detailed mechanisms with a larger number of species and reactions. The sparse learning strategy shows significant potential in achieving more substantial reductions in complex chemical reaction mechanisms. |
| title | A data-driven sparse learning approach to reduce chemical reaction mechanisms |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2410.09901 |