Chemical kinetic model reduction based on species‐targeted local sensitivity analysis

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Autori principali: You Wu, Shengqiang Lin, Chung K. Law, Bin Yang
Natura: Artículo Open Access
Pubblicazione: Wiley 2024
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author You Wu
Shengqiang Lin
Chung K. Law
Bin Yang
author_facet You Wu
Shengqiang Lin
Chung K. Law
Bin Yang
You Wu
Shengqiang Lin
Chung K. Law
Bin Yang
collection Wiley Open Access
contents Chemical kinetic model reduction based on species‐targeted local sensitivity analysis You Wu Shengqiang Lin Chung K. Law Bin Yang International Journal of Chemical Kinetics AbstractReduction of large combustion mechanisms is usually conducted based on the detection and elimination of redundant species and reactions. Reaction elimination methods are mostly based on sensitivity analysis, which can provide insight into the kinetic system, while species elimination methods are more efficient. In this work, the species‐targeted local sensitivity analysis (STLSA) method is proposed to evaluate the importance of species and eliminate non‐crucial species and their related reactions to simplify kinetic models. This paper comprehensively evaluates the effectiveness of STLSA across various combustion scenarios, including high and low‐temperature ignition and laminar flame speed, using diverse mechanisms like USC Mech II, JetSurf 1.0, POLIMI_TOT_1412, NUIGMech1.1 and so on. Comparisons with graph‐based methods, such as DRG and DRGEP, highlight STLSA's superior efficiency and accuracy. Moreover, STLSA is compared to species‐targeted global sensitivity analysis (STGSA), demonstrating significant computation cost savings and comparable model reduction capabilities. The study concludes that STLSA is a robust and versatile tool for mechanism reduction, offering substantial improvements in computational efficiency while maintaining high accuracy in predicting key combustion properties. 10.1002/kin.21721 http://onlinelibrary.wiley.com/termsAndConditions#vor
doi_str_mv 10.1002/kin.21721
format Artículo Open Access
id wiley_oa_10_1002_kin_21721
institution Wiley Open Access
license_str_mv http://onlinelibrary.wiley.com/termsAndConditions#vor
publishDate 2024
publisher Wiley
record_format wiley_oa
spellingShingle Chemical kinetic model reduction based on species‐targeted local sensitivity analysis
You Wu
Shengqiang Lin
Chung K. Law
Bin Yang
International Journal of Chemical Kinetics
Chemical kinetic model reduction based on species‐targeted local sensitivity analysis You Wu Shengqiang Lin Chung K. Law Bin Yang International Journal of Chemical Kinetics AbstractReduction of large combustion mechanisms is usually conducted based on the detection and elimination of redundant species and reactions. Reaction elimination methods are mostly based on sensitivity analysis, which can provide insight into the kinetic system, while species elimination methods are more efficient. In this work, the species‐targeted local sensitivity analysis (STLSA) method is proposed to evaluate the importance of species and eliminate non‐crucial species and their related reactions to simplify kinetic models. This paper comprehensively evaluates the effectiveness of STLSA across various combustion scenarios, including high and low‐temperature ignition and laminar flame speed, using diverse mechanisms like USC Mech II, JetSurf 1.0, POLIMI_TOT_1412, NUIGMech1.1 and so on. Comparisons with graph‐based methods, such as DRG and DRGEP, highlight STLSA's superior efficiency and accuracy. Moreover, STLSA is compared to species‐targeted global sensitivity analysis (STGSA), demonstrating significant computation cost savings and comparable model reduction capabilities. The study concludes that STLSA is a robust and versatile tool for mechanism reduction, offering substantial improvements in computational efficiency while maintaining high accuracy in predicting key combustion properties. 10.1002/kin.21721 http://onlinelibrary.wiley.com/termsAndConditions#vor
title Chemical kinetic model reduction based on species‐targeted local sensitivity analysis
topic International Journal of Chemical Kinetics
url https://onlinelibrary.wiley.com/doi/10.1002/kin.21721