Honors College Thesis
 

An Improved Chemical Kinetic Model Reduction in pyMARS for Liquid Propellants

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https://ir.library.oregonstate.edu/concern/honors_college_theses/3t945z65q

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  • The aerospace industry uses chemical kinetic models when designing propulsion systems to acquire specific details about the rate of chemical reactions, heat release, and combustion stability. These models, however, are too large and stiff to be useful as they can take weeks to run computations on. The pyMARS python package was developed based on previous research with established reduction algorithms to accurately reduce the size of these models to only the essential species for a given tolerance error. This work adds the additional functionality to use laminar flame speed data to quantify error between the original and reduced models, building upon the existing use of autoignition delay for error calculation. The efficacy of this method is proven by comparing the resulting models that are reduced by pyMARS using the original autoigniton benchmarks vs the new laminar flame speed functionality. Findings show that the additional use of laminar flame speed data helps retain important species in reduced models for a given error tolerance that provide more accurate flame information compared to autoignition modeling. Therefore, pyMARS users have better reduced models for their research.
  • Key Words: Chemical kinetics, Model reduction, laminar flame speed, Python, aerospace
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  • pyMARS
  • Cailin Moore Fork of pyMARS
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  • This material is based upon work supported by the National Science Foundation under Grant No. 1931592.
  • Funded in part by Deloach Scholarship.
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