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1D diesel engine cycle modeling integrated with MOPSO optimization for improved NOx control and pressure boost

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  • Taghavifar, Hadi
  • Mazari, Farhad

Abstract

In current investigation, one dimensional gas dynamic and chemical species prediction are simulated according to diesel engine with air cooler and catalyst. The results of engine cycle modeling are then verified with experimental data and the robust validation is obtained. The key parameters of air/fuel ratio, compression ratio, swirl number, coolant temperature, and air temperature of the heat exchanger are taken as input parameters for NOx reduction and pressure increase by multi-objective particle swarm optimization (MOPSO). According to merit function, NOx reduction is weighted two times the pressure increase, as a result out of 80 generated design ID78 is chosen as the best objective. The best design is characterized with high air temperature, swirl number, and compression ratio. It is found that swirl number is a dominant factor for NOx reduction and compression ratio for pressure increase. The best objective case is capable of 4.8% NOx reduction and 5.9% increase of pressure, however the best objective results in unfavorable torque decrease since the NOx reduction is biased. The best solutions also are benefited with 1.6% less wallheat loss due to lower diesel fuel mass injection.

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  • Taghavifar, Hadi & Mazari, Farhad, 2022. "1D diesel engine cycle modeling integrated with MOPSO optimization for improved NOx control and pressure boost," Energy, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:energy:v:247:y:2022:i:c:s0360544222004200
    DOI: 10.1016/j.energy.2022.123517
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    Cited by:

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    2. Han, Xiaojuan & Li, Jiarong & Zhang, Zhewen, 2023. "Dynamic game optimization control for shared energy storage in multiple application scenarios considering energy storage economy," Applied Energy, Elsevier, vol. 350(C).
    3. Wang, Huaiyu & Ji, Changwei & Yang, Jinxin & Wang, Shuofeng & Ge, Yunshan, 2022. "Towards a comprehensive optimization of the intake characteristics for side ported Wankel rotary engines by coupling machine learning with genetic algorithm," Energy, Elsevier, vol. 261(PB).

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