Performing helicopter maneuvers with optimization methods

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2020-8

Author

Tosun, Fatih

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In order to certificate a helicopter, aviation safety agencies must know that all designed helicopter configurations can withstand all loads resulting from maneuvers defined in certification standards. In other words, the maneuvers defined in regulations must be performed for each appropriate combination of weight and center of gravity. Then, designers have to prove that the helicopter can fly safely across the entire flight spectrum. Therefore, load engineers perform all maneuvers defined in the helicopter usage spectrum in order to analyze all possible load values. In traditional methods, a trial and error approach is used to reach the maneuver. However, performing these maneuvers with a trial and error approach not only causes expensive computing but also requires more engineering effort. Additionally, it may cause some defects in the maneuvers. Therefore, this thesis study aims to achieve the desired helicopter maneuvers using optimization methods in order to reduce the calculation cost and engineering efforts and perform the maneuvers accurately. For this purpose, only selected maneuvers are performed in this thesis. In addition, various optimization methods in different configurations have been applied to solve these maneuvers. Thus, the most useful one among them has been decided by making the necessary comparisons.Finally, the most useful optimization method has been applied to maneuvers frequently performed by helicopters throughout its lifetime.

Subject Keywords

Pilot Control Inputs, Objective Function, Constraints, Design Variable Vector, Maneuver Optimization, Pilot Kontrol Girdileri, Amaç Fonksiyonu, Kısıtlamalar, Tasarım Değişkeni Vektörü, Manevra Optimizasyonu

URI

https://hdl.handle.net/11511/69137

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

F. Tosun, “Performing helicopter maneuvers with optimization methods,” M.S. - Master of Science, Middle East Technical University, 2020.