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Application of genetic algorithm in exergy and sustainability: A case of aero-gas turbine engine at cruise phase

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  • Aygun, Hakan
  • Turan, Onder

Abstract

As sustainability, increasing of fuel and energy efficiency has become a greater concern in aircraft design and fleet operation for two decades. To meet this need, a new methodology is being developed here that proposes the use of optimization for indexing the sustainability throughout flight phases of the aircraft and its power system. In this study, off-design modeling of several exergetic parameters for the turbofan engine is conducted by using genetic algorithm at cruise phase. In this context, exergetic sustainability parameters such as exergy efficiency, wasted exergy ratio, exergy destruction factor, environmental effect factor and exergetic sustainability index are calculated for the aircraft engine. After this process, linear modeling of exergetic indexes, depending on Mach (0.7–0.9) and altitude (9–11 km) ranges is performed. The results highlight that the linear modeling of exergetic parameters with the genetic algorithm (GA) enhances accuracy of the model compared to the least square method (LSM). Modeling of exergy efficiency for the turbofan is achieved with R = 0.9974 by LSM whereas it is obtained with R = 0.9999 by GA at altitude of 11 km. Finally, modeling of exergetic metrics for turbofan engine by using parametric flight data could enable so as to determine optimum cruise flight conditions in terms of both engine performance and environmental sustainability.

Suggested Citation

  • Aygun, Hakan & Turan, Onder, 2022. "Application of genetic algorithm in exergy and sustainability: A case of aero-gas turbine engine at cruise phase," Energy, Elsevier, vol. 238(PA).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pa:s0360544221018922
    DOI: 10.1016/j.energy.2021.121644
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    References listed on IDEAS

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    2. Zhou Ruhan & Nurulafiqah Nadzirah Binti Mansor & Hazlee Azil Illias, 2023. "Identification of Inrush Current Using a GSA-BP Network," Energies, MDPI, vol. 16(5), pages 1-22, February.
    3. Balli, Ozgur, 2022. "Thermodynamic, thermoenvironmental and thermoeconomic analyses of piston-prop engines (PPEs) for landing and take-off (LTO) flight phases," Energy, Elsevier, vol. 250(C).
    4. Dang, Chaolei & Cheng, Kunlin & Fan, Junhao & Wang, Yilin & Qin, Jiang & Liu, Guodong, 2023. "Performance analysis of fuel vapor turbine and closed-Brayton-cycle combined power generation system for hypersonic vehicles," Energy, Elsevier, vol. 266(C).
    5. Wen, Jie & Wan, Chenxi & Xu, Guoqiang & Zhuang, Laihe & Dong, Bensi & Chen, Junjie, 2024. "Optimization of thermal management system architecture in hydrogen engine employing improved genetic algorithm," Energy, Elsevier, vol. 297(C).
    6. Noorollahi, Younes & Golshanfard, Aminabbas & Hashemi-Dezaki, Hamed, 2022. "A scenario-based approach for optimal operation of energy hub under different schemes and structures," Energy, Elsevier, vol. 251(C).
    7. Yin, Linfei & Cai, Zhenjian, 2024. "Multimodal multi-objective hierarchical distributed consensus method for multimodal multi-objective economic dispatch of hierarchical distributed power systems," Energy, Elsevier, vol. 295(C).
    8. Oruc, Ridvan & Baklacioglu, Tolga, 2022. "Modeling of aircraft performance parameters with metaheuristic methods to achieve specific excess power contours using energy maneuverability method," Energy, Elsevier, vol. 259(C).
    9. Cai, Changpeng & Wang, Yong & Fang, Juan & Chen, Haoying & Zheng, Qiangang & Zhang, Haibo, 2023. "Multiple aspects to flight mission performances improvement of commercial turbofan engine via variable geometry adjustment," Energy, Elsevier, vol. 263(PA).
    10. Laihe Zhuang & Guoqiang Xu & Bensi Dong & Qihang Liu & Mengchen Li & Jie Wen, 2022. "Exergetic Effects of Cooled Cooling Air Technology on the Turbofan Engine during a Typical Mission," Energies, MDPI, vol. 15(14), pages 1-25, July.

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