Uncertainty analysis of correlated parameters in automated reaction mechanism generation
Uncertainty analysis is a useful tool for inspecting and improving detailed kinetic mechanisms because it can identify the greatest sources of model output error. Owing to the very nonlinear relationship between kinetic and thermodynamic parameters and computed concentrations, model predictions can...
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| Format: | Article |
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Wiley
2020
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| Online Access: | https://hdl.handle.net/1721.1/124020 |
| _version_ | 1811095691760500736 |
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| author | Gao, Connie Wu Liu, Mengjie Green Jr, William H |
| author2 | Massachusetts Institute of Technology. Department of Chemical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Chemical Engineering Gao, Connie Wu Liu, Mengjie Green Jr, William H |
| author_sort | Gao, Connie Wu |
| collection | MIT |
| description | Uncertainty analysis is a useful tool for inspecting and improving detailed kinetic mechanisms because it can identify the greatest sources of model output error. Owing to the very nonlinear relationship between kinetic and thermodynamic parameters and computed concentrations, model predictions can be extremely sensitive to uncertainties in some parameters while uncertainties in other parameters can be irrelevant. Error propagation becomes even more convoluted in automatically generated kinetic models, where input uncertainties are correlated through kinetic rate rules and thermodynamic group values.
Local and global uncertainty analyses were implemented and used to analyze error propagation in Reaction Mechanism Generator (RMG), an open‐source software for generating kinetic models. A framework for automatically assigning parameter uncertainties to estimated thermodynamics and kinetics was created, enabling tracking of correlated uncertainties. Local first‐order uncertainty propagation was implemented using sensitivities computed natively within RMG. Global uncertainty analysis was implemented using adaptive Smolyak pseudospectral approximations as implemented in the MIT Uncertainty Quantification Library to efficiently compute and construct polynomial chaos expansions to approximate the dependence of outputs on a subset of uncertain inputs. Cantera was used as a backend for simulating the reactor system in the global analysis. Analyses were performed for a phenyldodecane pyrolysis model. Local and global methods demonstrated similar trends; however, many uncertainties were significantly overestimated by the local analysis. Both local and global analyses show that correlated uncertainties based on kinetic rate rules and thermochemical groups drastically reduce a model's degrees of freedom and have a large impact on the determination of the most influential input parameters. These results highlight the necessity of incorporating uncertainty analysis in the mechanism generation workflow. Keywords: automatic reaction mechanism generation; chemical kinetics; polynomial chaos expansion; sensitivity analysis; uncertainty analysis |
| first_indexed | 2024-09-23T16:24:40Z |
| format | Article |
| id | mit-1721.1/124020 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T16:24:40Z |
| publishDate | 2020 |
| publisher | Wiley |
| record_format | dspace |
| spelling | mit-1721.1/1240202022-09-29T19:48:10Z Uncertainty analysis of correlated parameters in automated reaction mechanism generation Gao, Connie Wu Liu, Mengjie Green Jr, William H Massachusetts Institute of Technology. Department of Chemical Engineering Uncertainty analysis is a useful tool for inspecting and improving detailed kinetic mechanisms because it can identify the greatest sources of model output error. Owing to the very nonlinear relationship between kinetic and thermodynamic parameters and computed concentrations, model predictions can be extremely sensitive to uncertainties in some parameters while uncertainties in other parameters can be irrelevant. Error propagation becomes even more convoluted in automatically generated kinetic models, where input uncertainties are correlated through kinetic rate rules and thermodynamic group values. Local and global uncertainty analyses were implemented and used to analyze error propagation in Reaction Mechanism Generator (RMG), an open‐source software for generating kinetic models. A framework for automatically assigning parameter uncertainties to estimated thermodynamics and kinetics was created, enabling tracking of correlated uncertainties. Local first‐order uncertainty propagation was implemented using sensitivities computed natively within RMG. Global uncertainty analysis was implemented using adaptive Smolyak pseudospectral approximations as implemented in the MIT Uncertainty Quantification Library to efficiently compute and construct polynomial chaos expansions to approximate the dependence of outputs on a subset of uncertain inputs. Cantera was used as a backend for simulating the reactor system in the global analysis. Analyses were performed for a phenyldodecane pyrolysis model. Local and global methods demonstrated similar trends; however, many uncertainties were significantly overestimated by the local analysis. Both local and global analyses show that correlated uncertainties based on kinetic rate rules and thermochemical groups drastically reduce a model's degrees of freedom and have a large impact on the determination of the most influential input parameters. These results highlight the necessity of incorporating uncertainty analysis in the mechanism generation workflow. Keywords: automatic reaction mechanism generation; chemical kinetics; polynomial chaos expansion; sensitivity analysis; uncertainty analysis United States. Department of Energy. Office of Basic Energy Sciences (Award DE‐SC0014901) 2020-03-06T18:20:04Z 2020-03-06T18:20:04Z 2020-02 2019-12 Article http://purl.org/eprint/type/JournalArticle 0538-8066 1097-4601 https://hdl.handle.net/1721.1/124020 Gao, Connie Wu et al. "Uncertainty analysis of correlated parameters in automated reaction mechanism generation." International Journal of Chemical Kinetics 52, 4 (February 2020): 266-282 © 2020 Wiley http://dx.doi.org/10.1002/kin.21348 International Journal of Chemical Kinetics Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Wiley Prof. Green |
| spellingShingle | Gao, Connie Wu Liu, Mengjie Green Jr, William H Uncertainty analysis of correlated parameters in automated reaction mechanism generation |
| title | Uncertainty analysis of correlated parameters in automated reaction mechanism generation |
| title_full | Uncertainty analysis of correlated parameters in automated reaction mechanism generation |
| title_fullStr | Uncertainty analysis of correlated parameters in automated reaction mechanism generation |
| title_full_unstemmed | Uncertainty analysis of correlated parameters in automated reaction mechanism generation |
| title_short | Uncertainty analysis of correlated parameters in automated reaction mechanism generation |
| title_sort | uncertainty analysis of correlated parameters in automated reaction mechanism generation |
| url | https://hdl.handle.net/1721.1/124020 |
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