JOURNAL ARTICLE

Laminar flame speed measurement and combustion mechanism optimization of methanol–air mixtures.

  • Published In: International Journal of Chemical Kinetics, 2024, v. 56, n. 7. P. 406 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: WANG, LEI; Zhang, Zixing; Zhong, Zheng 3 of 3

Abstract

Laminar flame speeds of methanol/air mixtures at 338–398 K are measured by the heat flux method, extending the range of equivalence ratio up to 2.1. And a new optimized methanol mechanism with 94 reactions is proposed by using the particle swarm algorithm, adjusting 20 Arrhenius pre‐exponential factors in their uncertainty domains. The optimized model is compared with eight methanol combustion mechanisms and experimental data published in recent years, covering a wide range of initial temperatures (298–1537 K), pressures (0.04–50 atm) and equivalence ratios (0.5–2.1). The results show that the optimized mechanism not only improves the accuracy of ignition delay time with rapid compression machine at low temperature but also moderately improve the description of laminar flame speed in lean and stoichiometric conditions. Meanwhile, the optimized model significantly enhances the prediction accuracy of CH3 and CH2O radical, and perfectly captures the evolution trend of HCO radical in laminar flat flame. Overall, the optimized mechanism provides the best overall description of the currently available measurements, leading to more accurate and comprehensive prediction of ignition delay time, laminar flame speed and species concentration. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Chemical Kinetics. 2024/07, Vol. 56, Issue 7, p406
  • Document Type:Article
  • Subject Area:Engineering
  • Publication Date:2024
  • ISSN:0538-8066
  • DOI:10.1002/kin.21717
  • Accession Number:177398091
  • Copyright Statement:Copyright of International Journal of Chemical Kinetics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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