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<p>This study presents a reduced ionic chemical kinetics mechanism for predicting combustion phasing in methane-fueled homogeneous charge compression ignition (HCCI) engines. Starting from the detailed GRI-Mech 3.0 mechanism, a reduced scheme with 22 species and 48 reactions was developed. Ionic reactions were then added, forming a comprehensive mechanism with 27 species and 54 reactions. The mechanism was integrated into a multi-zone combustion model and validated against experimental data from a CFR engine under four operating conditions. Results show that the mechanism accurately predicts the start of combustion (SOC), with a maximum error below 0.09%. In-cylinder pressure and temperature profiles closely match experimental data. The model also captures the behavior of key radicals and ions, including H₃O⁺, OH⁻, and O₂⁻. Exhaust emissions such as CO and CO₂ are predicted with relative errors under 12%, while UHC predictions show moderate discrepancies. Notably, electrons are fully consumed during combustion, while some ions remain in the exhaust. The proposed mechanism offers a reliable and computationally efficient tool for combustion diagnostics and control, with potential applications in low-temperature and fuel-flexible engine technologies.</p>

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Advanced prediction of combustion phasing in methane homogeneous engines via in-cylinder ion current profiling and a novel ionic kinetics framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月24日

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  • Elaheh Neshat,
  • Amin Motevali Imami,
  • Damon Honnery

摘要

This study presents a reduced ionic chemical kinetics mechanism for predicting combustion phasing in methane-fueled homogeneous charge compression ignition (HCCI) engines. Starting from the detailed GRI-Mech 3.0 mechanism, a reduced scheme with 22 species and 48 reactions was developed. Ionic reactions were then added, forming a comprehensive mechanism with 27 species and 54 reactions. The mechanism was integrated into a multi-zone combustion model and validated against experimental data from a CFR engine under four operating conditions. Results show that the mechanism accurately predicts the start of combustion (SOC), with a maximum error below 0.09%. In-cylinder pressure and temperature profiles closely match experimental data. The model also captures the behavior of key radicals and ions, including H₃O⁺, OH⁻, and O₂⁻. Exhaust emissions such as CO and CO₂ are predicted with relative errors under 12%, while UHC predictions show moderate discrepancies. Notably, electrons are fully consumed during combustion, while some ions remain in the exhaust. The proposed mechanism offers a reliable and computationally efficient tool for combustion diagnostics and control, with potential applications in low-temperature and fuel-flexible engine technologies.