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Cruise range modeling of different flight strategies for transport aircraft using genetic algorithms and particle swarm optimization

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  • Oruc, Ridvan
  • Baklacioglu, Tolga

Abstract

Cruise flight profile accounts for the highest percentage of fuel consumption on long-haul flights, which makes the optimization of cruise range crucial for a net-zero, sustainable, secure, and affordable energy future. Therefore, flying at altitudes close to the optimum cruise altitude will significantly reduce fuel consumption and fuel-related emissions; It will also increase cruise range. In this context, accurate range calculations are critical to evaluate and understand the environmental and economic impacts of all aircraft. This paper proposes two novel non-conventional models of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) to estimate the cruise range of jet powered transport aircraft as a first attempt in the literature. Using both methods, non-linear models which provide range formulations dependent on velocity, aircraft weight and cruise flight altitude were developed for the cruise flight strategies including constant altitude-constant lift coefficient and constant altitude-constant Mach number. The accuracies of both derived models were analyzed and it was seen that both models are capable of providing satisfactory cruise range prediction results.

Suggested Citation

  • Oruc, Ridvan & Baklacioglu, Tolga, 2024. "Cruise range modeling of different flight strategies for transport aircraft using genetic algorithms and particle swarm optimization," Energy, Elsevier, vol. 294(C).
    • Handle: RePEc:eee:energy:v:294:y:2024:i:c:s0360544224006893
    DOI: 10.1016/j.energy.2024.130917
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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. 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).
    3. Chu, Chao-Hsien & Premkumar, G. & Chou, Hsinghua, 2000. "Digital data networks design using genetic algorithms," European Journal of Operational Research, Elsevier, vol. 127(1), pages 140-158, November.
    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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