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<p>A global reaction model for ammonia combustion, optimized using genetic algorithm to replicate flame speeds of a reference detailed mechanism, is proposed in this study. Despite the optimization focusing solely on flame speed, the model closely aligns with the reference mechanism’s temperature and species profiles. The rate parameters are expressed as polynomials of the equivalence ratio to extend applicability across diverse conditions. The model is evaluated under various combustion conditions, including counter-flow flames, swirl burners, and micro-flow reactors, achieving lower RMSRE than the reference model for extinction strain rate (0.2658 vs. 0.5621) and NH<sub>3</sub> mole fraction (38.5582 vs. 95.3796), but higher for NO concentration (23.4627 vs. 5.0473). Furthermore, a nitric oxide emission model is developed by fitting a two-term Gaussian equation to the net NO production rate. While the model performs reliably across a range of equivalence ratios, it exhibits limitations under fuel-rich conditions, suggesting the need for further refinement.</p>

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Optimizing global reaction models for ammonia combustion using genetic algorithms

  • 期刊论文

  • 发表日期：

    2025年9月3日

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  • Youngjin Jun,
  • Seong-kyun Im

摘要

A global reaction model for ammonia combustion, optimized using genetic algorithm to replicate flame speeds of a reference detailed mechanism, is proposed in this study. Despite the optimization focusing solely on flame speed, the model closely aligns with the reference mechanism’s temperature and species profiles. The rate parameters are expressed as polynomials of the equivalence ratio to extend applicability across diverse conditions. The model is evaluated under various combustion conditions, including counter-flow flames, swirl burners, and micro-flow reactors, achieving lower RMSRE than the reference model for extinction strain rate (0.2658 vs. 0.5621) and NH3 mole fraction (38.5582 vs. 95.3796), but higher for NO concentration (23.4627 vs. 5.0473). Furthermore, a nitric oxide emission model is developed by fitting a two-term Gaussian equation to the net NO production rate. While the model performs reliably across a range of equivalence ratios, it exhibits limitations under fuel-rich conditions, suggesting the need for further refinement.