DESIGN OPTIMIZATION OF INBOARD AIRFOIL FOR A COAXIAL ROTOR USING AN ANN PREDICTION MODEL

M.S. Kim; S.C. Lee; Y.S. Lim; Y.S. Jung

doi:10.6112/kscfe.2026.31.3.060

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2026 Vol.31, Issue 3 Preview Page Next Page

Original Article

DESIGN OPTIMIZATION OF INBOARD AIRFOIL FOR A COAXIAL ROTOR USING AN ANN PREDICTION MODEL 인공신경망 예측 모델 기반 동축 반전 로터의 안쪽 익형 최적 설계

30 September 2026. pp. 60-81

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Abstract

An artificial neural network(ANN)-based surrogate optimization framework was developed to enhance the aerodynamic performance of a lift-offset coaxial rotor’s inner airfoil under reverse flow. At high advance ratios, reverse flow over the inboard retreating blade causes the geometric trailing edge to act as a leading edge, inducing early separation, negative lift, and high profile drag. Although double-ended airfoils mitigate these penalties, the link between sectional improvements and integrated rotor performance remains unclear. Using the Sikorsky DBLN-526 as a baseline, the airfoil geometry was parameterized via the class-shape transformation(CST) method. An ANN model, trained on a 2D RANS CFD database, predicted aerodynamic coefficients, and was coupled with a genetic algorithm for multi-objective optimization. The optimized airfoils were evaluated through XH-59A coaxial rotor performance analysis. Results show that drag reduction at negative deep stall angle of attack provides limited rotor-level benefits. Conversely, drag reduction and lift enhancement at low angles of attack reduce the required pitch and effective angle of attack over the rotor disk, lowering profile drag and torque. The lift-enhancement optimized airfoil improved the rotor lift-to-drag ratio by 4.1% over the baseline. High-speed coaxial rotor inner airfoil design thus requires coupled consideration of sectional characteristics, rotor trim, and effective angle of attack distribution.

Keywords

전산유체역학(CFD)

인공신경망(Artificial neural networks)

CST(Class-Shape Transformation)

Double-ended Airfoil

DBLN-526

유전 알고리즘(Genetic algorithm)

형상최적설계(Shape optimization)

역류 유동(Reverse flow)

동축반전 로터(Coaxial rotor)

로터 성능(Rotor performance)

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Information

Publisher :Korean Society for Computational Fluids Engineering
Publisher(Ko) :한국전산유체공학회
Journal Title :Journal of Computational Fluids Engineering
Journal Title(Ko) :한국전산유체공학회지
Volume : 31
No :3
Pages :60-81
Received Date : 2026-06-01
Revised Date : 2026-07-29
Accepted Date : 2026-08-06
DOI :https://doi.org/10.6112/kscfe.2026.31.3.060

Journal of Computational Fluids Engineering ISSN:1598-6071(Print) 3022-6252(Online) 한국전산유체공학회지

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