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Investigation on the applicability for reaction rates adjustment of the optimized biodiesel skeletal mechanism

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  • Liu, Teng
  • E, Jiaqiang
  • Yang, W.M.
  • Deng, Yuangwang
  • An, H.
  • Zhang, Zhiqing
  • Pham, Minhhieu

Abstract

In order to reduce the blindness in developing a new skeletal mechanism, a chaos genetic algorithm (CGA) is proposed for optimizing the progress of reaction rates adjustment against traditional manual operator. By this way, an optimized mechanism has been generated from a skeletal mechanism including 112 species and 498 reactions, which was reduced from a detailed mechanism consisting of 3299 species and 10806 reactions. CGA is composed by important reactions determination, fitness function building, and chaos genetic compilation. To test the applicability of the optimized biodiesel skeletal mechanism, it has been validated by 0-D ignition delay prediction and 3-D engine simulation and experiment data. Results indicate that the ignition delay error of the optimized mechanism is smaller than the skeletal mechanism by comparing with the detailed mechanism. The optimized mechanism can simulate MD and MD9D ignition behavior over a wide range of common conditions. The 3-D engine prediction results also indicated that the optimized mechanism can better present the in-cylinder combustion characteristics.

Suggested Citation

  • Liu, Teng & E, Jiaqiang & Yang, W.M. & Deng, Yuangwang & An, H. & Zhang, Zhiqing & Pham, Minhhieu, 2018. "Investigation on the applicability for reaction rates adjustment of the optimized biodiesel skeletal mechanism," Energy, Elsevier, vol. 150(C), pages 1031-1038.
    • Handle: RePEc:eee:energy:v:150:y:2018:i:c:p:1031-1038
    DOI: 10.1016/j.energy.2018.03.026
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    Cited by:

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    2. Sultana, Ayesha & Alam, Md. Mehebub & Ghosh, Sujoy Kumar & Middya, Tapas Ranjan & Mandal, Dipankar, 2019. "Energy harvesting and self-powered microphone application on multifunctional inorganic-organic hybrid nanogenerator," Energy, Elsevier, vol. 166(C), pages 963-971.
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    4. Li, Yuqiang & Chen, Yong & Wu, Gang & Liu, Jiangwei, 2018. "Experimental evaluation of water-containing isopropanol-n-butanol-ethanol and gasoline blend as a fuel candidate in spark-ignition engine," Applied Energy, Elsevier, vol. 219(C), pages 42-52.

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