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Energetic and exergetic efficiency modeling of a cargo aircraft by a topology improving neuro-evolution algorithm

Author

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  • Baklacioglu, Tolga
  • Aydin, Hakan
  • Turan, Onder

Abstract

An aircraft is a complex system that requires methodologies for an efficient thermodynamic design process. So, it is important to gain a deeper understanding of energy and exergy use throughout an aircraft. The aim of this study is to propose a topology improving NE (neuro-evolution) algorithm modeling for assessing energy and exergy efficiency of a cargo aircraft for the phases of a flight. In this regard, energy and exergy data of the aircraft achieved from several engine runs at different power settings have been utilized to derive the ANN (artificial neural network) models optimized by a GA (genetic algorithm). NE of feed-forward networks trained by a BP (backpropagation) algorithm with momentum has assured the accomplishment of optimum initial network weights as well as the improvement of the network topology. The linear correlation coefficients very close to unity obtained for the derived ANN models have proved the tight fitting of the real data and the estimated values of the efficiencies provided by the models. Finally, compared to the trial-and-error case, evolving the networks by GAs has enhanced the accuracy of the modeling simply further as the reduction in the MSE (mean squared errors) for the energy and exergy efficiencies indicates.

Suggested Citation

  • Baklacioglu, Tolga & Aydin, Hakan & Turan, Onder, 2016. "Energetic and exergetic efficiency modeling of a cargo aircraft by a topology improving neuro-evolution algorithm," Energy, Elsevier, vol. 103(C), pages 630-645.
    • Handle: RePEc:eee:energy:v:103:y:2016:i:c:p:630-645
    DOI: 10.1016/j.energy.2016.03.018
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    Cited by:

    1. 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).
    2. Can, Özer & Baklacioglu, Tolga & Özturk, Erkan & Turan, Onder, 2022. "Artificial neural networks modeling of combustion parameters for a diesel engine fueled with biodiesel fuel," Energy, Elsevier, vol. 247(C).
    3. 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).
    4. Laubscher, Ryno, 2019. "Time-series forecasting of coal-fired power plant reheater metal temperatures using encoder-decoder recurrent neural networks," Energy, Elsevier, vol. 189(C).
    5. Parsa, Navid & Bahmani-Firouzi, Bahman & Niknam, Taher, 2021. "A social-economic-technical framework for reinforcing the automated distribution systems considering optimal switching and plug-in hybrid electric vehicles," Energy, Elsevier, vol. 220(C).

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