Local sensitivity informed anisotropic sparse grid method for uncertainty quantification of chemical kinetics
The determination of rate coefficient parameters in detailed chemical kinetic mechanisms through experiments often suffers from avoidable aleatory uncertainty, while the use of reduced mechanisms, based on various reduction methods, introduces epistemic uncertainty. Both sources of uncertainty pose...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2023-07-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0153726 |
| _version_ | 1797755899167637504 |
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| author | Linying Li Bin Zhang Hong Liu |
| author_facet | Linying Li Bin Zhang Hong Liu |
| author_sort | Linying Li |
| collection | DOAJ |
| description | The determination of rate coefficient parameters in detailed chemical kinetic mechanisms through experiments often suffers from avoidable aleatory uncertainty, while the use of reduced mechanisms, based on various reduction methods, introduces epistemic uncertainty. Both sources of uncertainty pose significant challenges for modeling and numerical simulation, highlighting the need for calibrating these uncertain parameters to achieve robust chemical mechanisms in the next generation. However, the high-dimensional parameter space of chemical kinetic mechanisms remains a significant obstacle in uncertainty computing. The Anisotropic Sparse Grid (ASG) technique has been successful in dealing with high-dimensional uncertainty problems, but it lacks prior knowledge of anisotropy. To address this issue, we propose the Local Sensitivity-Informed Anisotropic Sparse Grid (LSIASG) method, which utilizes local sensitivity as prior information for the ASG method, thereby accelerating the entire uncertainty quantification process. We test the LSIASG method on a theoretical model, a detailed hydrogen kinetic mechanism, and a methane mechanism and demonstrate that it can capture high-dimensional uncertainty characteristics, including expectation and deviation. |
| first_indexed | 2024-03-12T17:53:47Z |
| format | Article |
| id | doaj.art-a9b3690c6a234af0ac39e022b6e1c384 |
| institution | Directory Open Access Journal |
| issn | 2158-3226 |
| language | English |
| last_indexed | 2024-03-12T17:53:47Z |
| publishDate | 2023-07-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj.art-a9b3690c6a234af0ac39e022b6e1c3842023-08-02T20:06:09ZengAIP Publishing LLCAIP Advances2158-32262023-07-01137075323075323-910.1063/5.0153726Local sensitivity informed anisotropic sparse grid method for uncertainty quantification of chemical kineticsLinying Li0Bin Zhang1Hong Liu2School of Aeronautics and Astronautics, Shanghai Jiao Tong University, 800 Dong Chuan Road, Shanghai 200240, ChinaSchool of Aeronautics and Astronautics, Shanghai Jiao Tong University, 800 Dong Chuan Road, Shanghai 200240, ChinaSchool of Aeronautics and Astronautics, Shanghai Jiao Tong University, 800 Dong Chuan Road, Shanghai 200240, ChinaThe determination of rate coefficient parameters in detailed chemical kinetic mechanisms through experiments often suffers from avoidable aleatory uncertainty, while the use of reduced mechanisms, based on various reduction methods, introduces epistemic uncertainty. Both sources of uncertainty pose significant challenges for modeling and numerical simulation, highlighting the need for calibrating these uncertain parameters to achieve robust chemical mechanisms in the next generation. However, the high-dimensional parameter space of chemical kinetic mechanisms remains a significant obstacle in uncertainty computing. The Anisotropic Sparse Grid (ASG) technique has been successful in dealing with high-dimensional uncertainty problems, but it lacks prior knowledge of anisotropy. To address this issue, we propose the Local Sensitivity-Informed Anisotropic Sparse Grid (LSIASG) method, which utilizes local sensitivity as prior information for the ASG method, thereby accelerating the entire uncertainty quantification process. We test the LSIASG method on a theoretical model, a detailed hydrogen kinetic mechanism, and a methane mechanism and demonstrate that it can capture high-dimensional uncertainty characteristics, including expectation and deviation.http://dx.doi.org/10.1063/5.0153726 |
| spellingShingle | Linying Li Bin Zhang Hong Liu Local sensitivity informed anisotropic sparse grid method for uncertainty quantification of chemical kinetics AIP Advances |
| title | Local sensitivity informed anisotropic sparse grid method for uncertainty quantification of chemical kinetics |
| title_full | Local sensitivity informed anisotropic sparse grid method for uncertainty quantification of chemical kinetics |
| title_fullStr | Local sensitivity informed anisotropic sparse grid method for uncertainty quantification of chemical kinetics |
| title_full_unstemmed | Local sensitivity informed anisotropic sparse grid method for uncertainty quantification of chemical kinetics |
| title_short | Local sensitivity informed anisotropic sparse grid method for uncertainty quantification of chemical kinetics |
| title_sort | local sensitivity informed anisotropic sparse grid method for uncertainty quantification of chemical kinetics |
| url | http://dx.doi.org/10.1063/5.0153726 |
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