Aerodynamic low fidelity shape optimization of helicopter rotor blades in hover using genetic algorithms and the adjoint method
© 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved. The Aerodynamic shape design of a helicopter rotor is a challenging problem that is required for designing a rotor disk with high efficiency. This work proposes a CFD validated process to find the optimal blade configuration of a helicopter blade in hover. The adjoint method is used to find the optimal shape of the blade sectional airfoil. Multi-objective optimization is carried out on the Caradonna-Tung rotor with figure of merit and coefficient of thrust as objective functions. Coefficient of Thrust is included in this study to limit the reduction of total thrust due to figure of merit enhancements. The multi-objective genetic algorithms (MOGA) optimization technique is applied with the thrust solidity ratio as a physical constraint, and blade root chord, tip chord, point of taper initiation, twist distribution as design parameters. The aerodynamic performance calculations are performed using the corrected blade element momentum theory (BEMT) that showed an excellent agreement with the wind-tunnel experimental data of the baseline rotor. A CFD-Based simulation is used to evaluate both the baseline Caradonna-Tung and the optimal blade Aerodynamic characteristics with an ideal gas k-ω SST turbulence model using FLUENT’s solver. Results show a great enhancement in the inflow ratio and blade tip vortices of the optimal blade. A CFD sensitivity study of each design variable is performed and showed the validity of the low fidelity optimization, Corrected BEMT, for this problem. This optimization method can be used to achieve satisfactory results with less computational power comparing to CFD based optimization approaches.
AIAA Aerospace Sciences Meeting, 2018
Nagib Elmekawy, A.,
Aerodynamic low fidelity shape optimization of helicopter rotor blades in hover using genetic algorithms and the adjoint method.
AIAA Aerospace Sciences Meeting, 2018(210059).
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