IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v269y2023ics036054422300213x.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422300213X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.126819?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Basu, M. & Chowdhury, A., 2013. "Cuckoo search algorithm for economic dispatch," Energy, Elsevier, vol. 60(C), pages 99-108.
    3. 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).
    4. 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.
    5. 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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. 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).
    3. Afzal, Asif & Buradi, Abdulrajak & Jilte, Ravindra & Shaik, Saboor & Kaladgi, Abdul Razak & Arıcı, Muslum & Lee, Chew Tin & Nižetić, Sandro, 2023. "Optimizing the thermal performance of solar energy devices using meta-heuristic algorithms: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    4. Jianzhong Xu & Fu Yan & Kumchol Yun & Lifei Su & Fengshu Li & Jun Guan, 2019. "Noninferior Solution Grey Wolf Optimizer with an Independent Local Search Mechanism for Solving Economic Load Dispatch Problems," Energies, MDPI, vol. 12(12), pages 1-26, June.
    5. Askarzadeh, Alireza, 2014. "Comparison of particle swarm optimization and other metaheuristics on electricity demand estimation: A case study of Iran," Energy, Elsevier, vol. 72(C), pages 484-491.
    6. Modiri-Delshad, Mostafa & Aghay Kaboli, S. Hr. & Taslimi-Renani, Ehsan & Rahim, Nasrudin Abd, 2016. "Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options," Energy, Elsevier, vol. 116(P1), pages 637-649.
    7. 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.
    8. Elsakaan, Asmaa A. & El-Sehiemy, Ragab A. & Kaddah, Sahar S. & Elsaid, Mohammed I., 2018. "An enhanced moth-flame optimizer for solving non-smooth economic dispatch problems with emissions," Energy, Elsevier, vol. 157(C), pages 1063-1078.
    9. Chen, Fei & Liu, Yang, 2022. "Model construction and performance investigation of multi-section compound parabolic concentrator with solar vacuum tube," Energy, Elsevier, vol. 250(C).
    10. Dey, Bishwajit & Misra, Srikant & Garcia Marquez, Fausto Pedro, 2023. "Microgrid system energy management with demand response program for clean and economical operation," Applied Energy, Elsevier, vol. 334(C).
    11. Nguyen, Thang Trung & Vo, Dieu Ngoc & Dinh, Bach Hoang, 2018. "An effectively adaptive selective cuckoo search algorithm for solving three complicated short-term hydrothermal scheduling problems," Energy, Elsevier, vol. 155(C), pages 930-956.
    12. Aokang Pang & Huijun Liang & Chenhao Lin & Lei Yao, 2023. "A Surrogate-Assisted Adaptive Bat Algorithm for Large-Scale Economic Dispatch," Energies, MDPI, vol. 16(2), pages 1-23, January.
    13. Modiri-Delshad, Mostafa & Rahim, Nasrudin Abd, 2014. "Solving non-convex economic dispatch problem via backtracking search algorithm," Energy, Elsevier, vol. 77(C), pages 372-381.
    14. Jayabarathi, T. & Raghunathan, T. & Adarsh, B.R. & Suganthan, Ponnuthurai Nagaratnam, 2016. "Economic dispatch using hybrid grey wolf optimizer," Energy, Elsevier, vol. 111(C), pages 630-641.
    15. Singh, Diljinder & Dhillon, J.S., 2019. "Ameliorated grey wolf optimization for economic load dispatch problem," Energy, Elsevier, vol. 169(C), pages 398-419.
    16. Ajdad, H. & Filali Baba, Y. & Al Mers, A. & Merroun, O. & Bouatem, A. & Boutammachte, N., 2019. "Particle swarm optimization algorithm for optical-geometric optimization of linear fresnel solar concentrators," Renewable Energy, Elsevier, vol. 130(C), pages 992-1001.
    17. Saiyan Liu & Yangyang Xie & Hongyuan Fang & Qiang Huang & Shengzhi Huang & Jingcai Wang & Zhen Li, 2020. "Impacts of Inflow Variations on the Long Term Operation of a Multi-Hydropower-Reservoir System and a Strategy for Determining the Adaptable Operation Rule," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(5), pages 1649-1671, March.
    18. Liu, Zhengguang & Guo, Zhiling & Chen, Qi & Song, Chenchen & Shang, Wenlong & Yuan, Meng & Zhang, Haoran, 2023. "A review of data-driven smart building-integrated photovoltaic systems: Challenges and objectives," Energy, Elsevier, vol. 263(PE).
    19. Khan, Mohd Shariq & Lee, Moonyong, 2013. "Design optimization of single mixed refrigerant natural gas liquefaction process using the particle swarm paradigm with nonlinear constraints," Energy, Elsevier, vol. 49(C), pages 146-155.
    20. Nwulu, Nnamdi I. & Xia, Xiaohua, 2015. "Implementing a model predictive control strategy on the dynamic economic emission dispatch problem with game theory based demand response programs," Energy, Elsevier, vol. 91(C), pages 404-419.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:269:y:2023:i:c:s036054422300213x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.