Automated Design and Evaluation of Airfoils for Rotorcraft Applications

Abstract

In this work, a methodology is presented and implemented for the automated design and optimization of airfoilsections. The airfoil optimization is conducted using the CMA-ES genetic algorithm with constraints applied tothe airfoils area and pitching moment coefficient. The design variables are formed through a class shape transformation with orthogonal basis modes, allowing for decreased levels of multicollinearity in higher-order design spaces, while still maintaining the completeness of lower-order spaces. A Python framework is developed to automate the generation of airfoil performance tables using the RANS CFD solver OVERFLOW 2.2i allowing the optimization methodology to be extended to rotorcraft applications. An empirical maximum lift coefficient criteria is developed and incorporated into the table generation process to overcome inaccuracies associated with stall prediction in CFD-based methods. The methodology presented is used to perform a singlepoint shape optimization on the tip airfoil of a UH-60A baseline rotor. The new tip section shows substantial improvements in forward-flight performance in exchange for a small reduction in the rotors stall margin. The airfoil optimization and table generation routine shows to be effective for the single design point investigated.This holds promise that the technology developed can later be successfully extended to higher-fidelity automated routines.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2017
Accession Number
AD1043312

Entities

People

  • Jason Stanko

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Aircrafts
  • Airframes
  • Algorithms
  • Boundary Layer
  • Computational Fluid Dynamics
  • Computational Science
  • Experimental Data
  • Fixed Wing Aircraft
  • Fluid Dynamics
  • Fluid Flow
  • Genetic Algorithms
  • Geometry
  • Mach Number
  • Reliability
  • Rotary Wing Aircraft
  • Test And Evaluation
  • Vehicles

Fields of Study

  • Physics

Readers

  • Aerodynamics/Aeronautics.
  • Aerospace Engineering
  • Computational Fluid Dynamics (CFD)

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Biotechnology
  • Space
  • Space - Spacecraft Maneuvers