Scalability strategies for automated reaction mechanism generation
Detailed modeling of complex chemical processes, like pollutant formation during combustion events, remains challenging and often intractable due to tedious and error-prone manual mechanism generation strategies. Automated mechanism generation methods seek to solve these problems but are held back b...
| Main Authors: | , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Elsevier BV
2020
|
| Online Access: | https://hdl.handle.net/1721.1/124333 |
| _version_ | 1826213663062622208 |
|---|---|
| author | Jocher, Agnes Vandewiele, Nick Han, Kehang Liu, Mengjie Gao, Connie Wu Gillis, Ryan J. Green Jr, William H |
| author2 | Massachusetts Institute of Technology. Department of Chemical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Chemical Engineering Jocher, Agnes Vandewiele, Nick Han, Kehang Liu, Mengjie Gao, Connie Wu Gillis, Ryan J. Green Jr, William H |
| author_sort | Jocher, Agnes |
| collection | MIT |
| description | Detailed modeling of complex chemical processes, like pollutant formation during combustion events, remains challenging and often intractable due to tedious and error-prone manual mechanism generation strategies. Automated mechanism generation methods seek to solve these problems but are held back by prohibitive computational costs associated with generating larger reaction mechanisms. Consequently, automated mechanism generation software such as the Reaction Mechanism Generator (RMG) must find novel ways to explore reaction spaces and thus understand the complex systems that have resisted other analysis techniques. In this contribution, we propose three scalability strategies — code optimization, algorithm heuristics, and parallel computing — that are shown to considerably improve RMG's performance as measured by mechanism generation time for three representative simulations (oxidation, pyrolysis, and combustion). The improvements create new opportunities for the detailed modeling of diverse real-world processes.Keywords: Chemical kinetics; Mechanism generation; Scalability; Parallel computing |
| first_indexed | 2024-09-23T15:52:50Z |
| format | Article |
| id | mit-1721.1/124333 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T15:52:50Z |
| publishDate | 2020 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1243332022-10-02T04:48:46Z Scalability strategies for automated reaction mechanism generation Jocher, Agnes Vandewiele, Nick Han, Kehang Liu, Mengjie Gao, Connie Wu Gillis, Ryan J. Green Jr, William H Massachusetts Institute of Technology. Department of Chemical Engineering Detailed modeling of complex chemical processes, like pollutant formation during combustion events, remains challenging and often intractable due to tedious and error-prone manual mechanism generation strategies. Automated mechanism generation methods seek to solve these problems but are held back by prohibitive computational costs associated with generating larger reaction mechanisms. Consequently, automated mechanism generation software such as the Reaction Mechanism Generator (RMG) must find novel ways to explore reaction spaces and thus understand the complex systems that have resisted other analysis techniques. In this contribution, we propose three scalability strategies — code optimization, algorithm heuristics, and parallel computing — that are shown to considerably improve RMG's performance as measured by mechanism generation time for three representative simulations (oxidation, pyrolysis, and combustion). The improvements create new opportunities for the detailed modeling of diverse real-world processes.Keywords: Chemical kinetics; Mechanism generation; Scalability; Parallel computing 2020-03-25T18:18:26Z 2020-03-25T18:18:26Z 2019-12 2019-09 Article http://purl.org/eprint/type/JournalArticle 0098-1354 https://hdl.handle.net/1721.1/124333 Agnes, Jocher et al. "Scalability strategies for automated reaction mechanism generation." Computers & Chemical Engineering 131 (December 2019): 106578 © 2019 Elsevier Ltd 10.1016/j.compchemeng.2019.106578 Computers & Chemical Engineering Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/vnd.openxmlformats-officedocument.wordprocessingml.document application/pdf Elsevier BV William H. Green |
| spellingShingle | Jocher, Agnes Vandewiele, Nick Han, Kehang Liu, Mengjie Gao, Connie Wu Gillis, Ryan J. Green Jr, William H Scalability strategies for automated reaction mechanism generation |
| title | Scalability strategies for automated reaction mechanism generation |
| title_full | Scalability strategies for automated reaction mechanism generation |
| title_fullStr | Scalability strategies for automated reaction mechanism generation |
| title_full_unstemmed | Scalability strategies for automated reaction mechanism generation |
| title_short | Scalability strategies for automated reaction mechanism generation |
| title_sort | scalability strategies for automated reaction mechanism generation |
| url | https://hdl.handle.net/1721.1/124333 |
| work_keys_str_mv | AT jocheragnes scalabilitystrategiesforautomatedreactionmechanismgeneration AT vandewielenick scalabilitystrategiesforautomatedreactionmechanismgeneration AT hankehang scalabilitystrategiesforautomatedreactionmechanismgeneration AT liumengjie scalabilitystrategiesforautomatedreactionmechanismgeneration AT gaoconniewu scalabilitystrategiesforautomatedreactionmechanismgeneration AT gillisryanj scalabilitystrategiesforautomatedreactionmechanismgeneration AT greenjrwilliamh scalabilitystrategiesforautomatedreactionmechanismgeneration |