Optimization of methane simplified chemical kinetic mechanism based on uncertainty quantitation analysis by sparse polynomial chaos expansions. (1st May 2023)
- Record Type:
- Journal Article
- Title:
- Optimization of methane simplified chemical kinetic mechanism based on uncertainty quantitation analysis by sparse polynomial chaos expansions. (1st May 2023)
- Main Title:
- Optimization of methane simplified chemical kinetic mechanism based on uncertainty quantitation analysis by sparse polynomial chaos expansions
- Authors:
- Lian, Zifan
Zhang, Jiwei
Zhao, Feiyang
Yu, Wenbin - Abstract:
- Highlights: New SPCE scheme was proposed to facilitate the intelligent selection of reaction rates based on minimized uncertainties. NSGA-II multi-objective optimization was introduced to achieve self-adaptive tuning of Arrhenius pre-exponential factor in simplified mechanism. Good performance by the optimized surrogate models were highlighted against existing experimental results, especially for fuel-lean combustion. Abstract: A robust and compact simplified chemical kinetics mechanism for methane oxidation is newly developed based on the Directed Relation Graph with Error Propagation method (DRGEP) and the Full Species Sensitivity Analysis method (FSSA). It is constructed by adaptive self-tuning of reaction rates with a non-dominated sorting genetic algorithm II (NSGA-II) approach to satisfy ignition behaviors under varied engine conditions. Its high fidelity is achieved by uncertainty quantitative (UQ) analysis to retain the uncertainties caused by updated reaction rates. In UQ analysis, the sparse polynomial chaos expansions (SPCE) model based on the Least Angle regression (LAR) method is applied to the adaptive selection of significant polynomial bases, and the accuracy of the SPCE model is tested using a statistical methodology (e.g. leave-one-out cross-validation). Consequently, compared with the full PCE approximations, the UQ analysis scheme reduces the computational cost by more than one order of magnitude and effectively alleviates the "dimension disaster" dilemmaHighlights: New SPCE scheme was proposed to facilitate the intelligent selection of reaction rates based on minimized uncertainties. NSGA-II multi-objective optimization was introduced to achieve self-adaptive tuning of Arrhenius pre-exponential factor in simplified mechanism. Good performance by the optimized surrogate models were highlighted against existing experimental results, especially for fuel-lean combustion. Abstract: A robust and compact simplified chemical kinetics mechanism for methane oxidation is newly developed based on the Directed Relation Graph with Error Propagation method (DRGEP) and the Full Species Sensitivity Analysis method (FSSA). It is constructed by adaptive self-tuning of reaction rates with a non-dominated sorting genetic algorithm II (NSGA-II) approach to satisfy ignition behaviors under varied engine conditions. Its high fidelity is achieved by uncertainty quantitative (UQ) analysis to retain the uncertainties caused by updated reaction rates. In UQ analysis, the sparse polynomial chaos expansions (SPCE) model based on the Least Angle regression (LAR) method is applied to the adaptive selection of significant polynomial bases, and the accuracy of the SPCE model is tested using a statistical methodology (e.g. leave-one-out cross-validation). Consequently, compared with the full PCE approximations, the UQ analysis scheme reduces the computational cost by more than one order of magnitude and effectively alleviates the "dimension disaster" dilemma of the classical PCE scheme. Therefore, the application of the adaptive SPCE scheme allows more reactions to be involved in quantifying the uncertainty propagation. Finally, the aforementioned new simplified methane mechanism is widely verified under engine-relevant conditions, and good auto-ignition performance is highlighted, especially in fuel-lean combustion. … (more)
- Is Part Of:
- Fuel. Volume 339(2023)
- Journal:
- Fuel
- Issue:
- Volume 339(2023)
- Issue Display:
- Volume 339, Issue 2023 (2023)
- Year:
- 2023
- Volume:
- 339
- Issue:
- 2023
- Issue Sort Value:
- 2023-0339-2023-0000
- Page Start:
- Page End:
- Publication Date:
- 2023-05-01
- Subjects:
- Simplified mechanism -- Chemistry kinetic mechanism -- Sparse polynomial chaos expansions -- Uncertainty quantitation analysis
Fuel -- Periodicals
Coal -- Periodicals
Coal
Fuel
Periodicals
662.6 - Journal URLs:
- http://www.sciencedirect.com/science/journal/latest/00162361 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.fuel.2023.127393 ↗
- Languages:
- English
- ISSNs:
- 0016-2361
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4048.000000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 25712.xml