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Helicopter Rotor Blade Planform Optimization Using Parametric Design and Multi-Objective Genetic Algorithm

Yonghu Wenren, Luke D. Allen, Robert B. Haehnel, Ian D. Dettwiller, U.S. Army ERDC
Joon W. Lim, U.S. Army CCDC Av.&MC

May 10, 2022

https://doi.org/10.4050/F-0078-2022-17482

Abstract:
In this paper, an automated framework is presented to perform helicopter rotor blade planform optimization. This framework contains three elements, Dakota, ParBlade, and RCAS. These elements are integrated into an environment control tool, Galaxy Simulation Builder, which is used to carry out the optimization. The main objective of this work is to conduct rotor performance design optimizations for forward flight and hover. The blade design variables manipulated by ParBlade are twist, sweep, and anhedral. The multi-objective genetic algorithm method is used in this study to search for the optimum blade design; the optimization objective is to minimize the rotor power required. Following design parameter substitution, ParBlade generates the modified blade shape and updates the rotor blade properties in the RCAS script before running RCAS. After the RCAS simulations are complete, the desired performance metrics (objectives and constraints) are extracted and returned to the Dakota optimizer. Demonstrative optimization case studies were conducted using a UH-60A main rotor as the base case. Rotor power in hover and forward flight, at advance ratio = 0.3, are used as objective functions. The results of this study show improvement in rotor power of 6.13% and 8.52% in hover and an advance ratio of 0.3, respectively. This configuration also yields greater reductions in rotor power for high advance ratios, e.g., 12.42% reduction at advance ratio 0.4.


Helicopter Rotor Blade Planform Optimization Using Parametric Design and Multi-Objective Genetic Algorithm

  • Presented at Forum 78
  • 13 pages
  • SKU # : F-0078-2022-17482
  • Aircraft Design I

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Helicopter Rotor Blade Planform Optimization Using Parametric Design and Multi-Objective Genetic Algorithm

Authors / Details:
Yonghu Wenren, Luke D. Allen, Robert B. Haehnel, Ian D. Dettwiller, U.S. Army ERDC
Joon W. Lim, U.S. Army CCDC Av.&MC