Helicopter Rotor Blade Multiple-Section Optimization with Performance Considerations

Abstract

This paper presents advancements in a surrogate-based, rotor blade design optimization framework for improved helicopter performance. The framework builds on previous successes by allowing multiple airfoil sections to designed simultaneously to minimize required rotor power in multiple flight conditions. Rotor power in hover and forward flight, at advance ratio micro = 0.3, are used as objective functions in a multi-objective genetic algorithm. The framework is constructed using Galaxy Simulation Builder with optimization provided through integration with Dakota. Three independent airfoil sections are morphed using ParFoil and aerodynamic coefficients for the updated airfoil shapes (i.e., lift, drag, moment) are calculated using linear interpolation from a database generated using C81Gen/ARC2D. Final rotor performance is then calculated using RCAS. Several demonstrative optimization case studies were conducted using the UH-60A main rotor. The degrees of freedom for this case are limited to the airfoil camber, camber crest position, thickness, and thickness crest position for each of the sections. The results of the three-segment case study show improvements in rotor power of 4.3% and 0.8% in forward flight and hover, respectively. This configuration also yields greater reductions in rotor power for high advance ratios, e.g., 6.0% reduction at micro = 0.35, and 8.8% reduction at micro = 0.4.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
May 10, 2021
Accession Number
AD1137635

Entities

People

  • Ian D. Dettwiller
  • Joon W. Lim
  • Luke D. Allen
  • Robert B. Haehnel

Organizations

  • Engineer Research and Development Center
  • United States Army Aviation Branch

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Algorithms
  • Case Studies
  • Computational Fluid Dynamics
  • Computational Science
  • Department Of Defense
  • Engineering
  • Engineers
  • Evolutionary Algorithms
  • Genetic Algorithms
  • Geometry
  • High Performance Computing
  • Information Systems
  • Multiobjective Optimization
  • Optimization
  • Rotary Wing Aircraft
  • Simulations
  • Test And Evaluation

Fields of Study

  • Physics

Readers

  • Computational Modeling and Simulation
  • Fluid Mechanics and Fluid Dynamics.
  • Inertial Navigation Systems.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Biotechnology