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Modeling of energy maneuverability based specific excess power contours for commercial aircraft using metaheuristic methods

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  • Oruc, Ridvan
  • Baklacioglu, Tolga

Abstract

Specific excess power (Ps) contours that can be obtained using the energy method; these are important contours that show the performance limits of the aircraft, allow the performance comparison of different aircraft, and help determine the trajectory corresponding to the minimum time to climb without the need for any mathematical operation. Due to the difficulties associated with obtaining Ps contours, there are not many studies on this subject. In order to overcome these difficulties, within the scope of this study, an estimation model was created that will easily predict the Ps contour depending only on flight altitude and Mach number. The data required for modeling are Ps contours obtained in a different study for B737-800 aircraft in 4 different flights. Although it is new, the cuckoo search algorithm (CSA) method, which has proven its success in many optimization problems, has been used for modeling and highly accurate results have been obtained. A different metaheuristic method, particle swarm optimization (PSO), was used to measure the accuracy of the model created. These models constitute the first attempt in the current literature; furthermore the datasets used include real Flight Data Recorder (FDR) values.

Suggested Citation

  • Oruc, Ridvan & Baklacioglu, Tolga, 2023. "Modeling of energy maneuverability based specific excess power contours for commercial aircraft using metaheuristic methods," Energy, Elsevier, vol. 269(C).
  • Handle: RePEc:eee:energy:v:269:y:2023:i:c:s036054422300213x
    DOI: 10.1016/j.energy.2023.126819
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    References listed on IDEAS

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    1. Perrigot, Antoine & Perier-Muzet, Maxime & Ortega, Pascal & Stitou, Driss, 2020. "Technical economic analysis of PV-driven electricity and cold cogeneration systems using particle swarm optimization algorithm," Energy, Elsevier, vol. 211(C).
    2. Basu, M. & Chowdhury, A., 2013. "Cuckoo search algorithm for economic dispatch," Energy, Elsevier, vol. 60(C), pages 99-108.
    3. Meng, Xuejiao & Chang, Jianxia & Wang, Xuebin & Wang, Yimin, 2019. "Multi-objective hydropower station operation using an improved cuckoo search algorithm," Energy, Elsevier, vol. 168(C), pages 425-439.
    4. 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).
    5. Siddhartha, & Sharma, Naveen & Varun,, 2012. "A particle swarm optimization algorithm for optimization of thermal performance of a smooth flat plate solar air heater," Energy, Elsevier, vol. 38(1), pages 406-413.
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