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Development and Optimization of Chemical Kinetic Mechanisms for Ethanol–Gasoline Blends Using Genetic Algorithms

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  • Filipe Cota

    (Graduate Program in Mechanical Engineering, Universidade Federal de Minas Gerais (PPGMEC-UFMG), Belo Horizonte CEP 31270-901, MG, Brazil
    Mobility Technology Center (CTM-UFMG), Department of Mechanical Engineering, Federal University of Minas Gerais, Antônio Carlos Avenue 6627, Belo Horizonte CEP 31270-901, MG, Brazil)

  • Clarissa Martins

    (Graduate Program in Mechanical Engineering, Universidade Federal de Minas Gerais (PPGMEC-UFMG), Belo Horizonte CEP 31270-901, MG, Brazil
    Mobility Technology Center (CTM-UFMG), Department of Mechanical Engineering, Federal University of Minas Gerais, Antônio Carlos Avenue 6627, Belo Horizonte CEP 31270-901, MG, Brazil)

  • Raphael Braga

    (Graduate Program in Mechanical Engineering, Universidade Federal de Minas Gerais (PPGMEC-UFMG), Belo Horizonte CEP 31270-901, MG, Brazil
    Mobility Technology Center (CTM-UFMG), Department of Mechanical Engineering, Federal University of Minas Gerais, Antônio Carlos Avenue 6627, Belo Horizonte CEP 31270-901, MG, Brazil)

  • José Baeta

    (Graduate Program in Mechanical Engineering, Universidade Federal de Minas Gerais (PPGMEC-UFMG), Belo Horizonte CEP 31270-901, MG, Brazil
    Mobility Technology Center (CTM-UFMG), Department of Mechanical Engineering, Federal University of Minas Gerais, Antônio Carlos Avenue 6627, Belo Horizonte CEP 31270-901, MG, Brazil)

Abstract

Reduced chemical kinetic mechanisms are essential for enabling the use of complex fuels in 3D CFD combustion simulations. This study presents the development and optimization of a compact mechanism capable of accurately modeling ethanol–gasoline blends, including Brazilian Type-C gasoline (27% ethanol by volume) and up to pure ethanol (E100). An initial mechanism was constructed using the Directed Relation Graph with Error Propagation (DRGEP) method applied to detailed mechanisms selected for each surrogate component. The resulting mechanism was then refined through three global iterations of a genetic algorithm targeting ignition delay time (IDT) and laminar flame speed (LFS) performance. Five candidate versions (Mec1 to Mec5), each containing 179 species and 771 reactions, were generated. Mec4 was identified as the optimal configuration based on quantitative error analysis across all tested conditions and blend ratios. The final mechanism offers a balance between predictive accuracy and computational feasibility, making it well-suited for high-fidelity simulations in complex geometries involving multi-component ethanol–gasoline fuels.

Suggested Citation

  • Filipe Cota & Clarissa Martins & Raphael Braga & José Baeta, 2025. "Development and Optimization of Chemical Kinetic Mechanisms for Ethanol–Gasoline Blends Using Genetic Algorithms," Energies, MDPI, vol. 18(16), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:16:p:4444-:d:1729183
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    References listed on IDEAS

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    1. Zhennan Zhu & Kun Liang & Xinwen Chen & Zhongwei Meng & Wenbin He & Hao Song, 2020. "Laminar Flame Characteristics of Premixed Methanol–Water–Air Mixture," Energies, MDPI, vol. 13(24), pages 1-13, December.
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    4. Glyniadakis, Sofia & Balestieri, José Antônio Perrella, 2023. "Brazilian light vehicle fleet decarbonization scenarios for 2050," Energy Policy, Elsevier, vol. 181(C).
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