Technical Note: Aerodynamic Design of Helicopter Rotor Blade in Forward Flight Using Response Surface Methodology

Sun, Hyosung; Kim, Yushin; Lee, Soogab; Lee, Dong‐Ho

doi:10.4050/jahs.48.300

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…Sun et al 146 used response surface methods for design of a rotor in forward flight. They constructed the response surface model from a flow analysis and solved the optimization problem using GA.…”

Section: Nongradient Methodsmentioning

confidence: 99%

A Survey of Recent Developments in Rotorcraft Design Optimization

Ganguli

2004

Journal of Aircraft

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Section: Nongradient Methodsmentioning

confidence: 99%

A Survey of Recent Developments in Rotorcraft Design Optimization

Ganguli

2004

Journal of Aircraft

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“…The most straightforward approach is to directly optimize the surrogate of an objective function without updating the surrogate during the process (Refs. 11,12,[28][29][30]. These surrogate-based optimization techniques have been applied to blade material and blade ply configuration selection.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Process Model for the Optimization of a Rotorcraft with Categorical Variables

Río¹,

Mavris²

2014

j am helicopter soc

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This paper proposes an improved stochastic process model for the selection of categorical variables, such as airfoils and materials, in rotorcraft design. This process leverages trends in typical rotorcraft conceptual design objectives that are similar across different categories. Also, this paper extends the use of efficient global optimization (EGO) algorithms, which intelligently search design spaces, to the previously proposed stochastic process thereby enabling the use of more computationally intensive tools earlier in the design process. To optimize the EGO infill criterion, a genetic algorithm is developed that is capable of searching domains with categorical variables. The proposed stochastic process model is successfully tested against traditional independent surrogates when approximating the engine shaft horsepower of the UH-60A with a choice of airfoils. Finally, a test to optimize the UH-60A engine shaft horsepower at two flight conditions demonstrates that the proposed extension of the EGO algorithm is more efficient at finding the Pareto fronts than the current state-of-the-art.

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“…Fanjoy and Crossley [7] applied a genetic algorithm to the two-dimensional airfoil section design of helicopter rotor blades based on a panel method. Sun et al [8] performed a study about the geometric parameter optimization of helicopter rotor blades in forward flight using a response surface method coupled with a genetic algorithm based on a free-wake panel method. The response surface method requires one to evaluate the flow solution many times, and thus the application to high fidelity flows in three dimensions is impractical.…”

mentioning

confidence: 99%

Aerodynamic Shape Optimization of Hovering Rotor Blades in Transonic Flow Using Unstructured Meshes

Lee

Kwon

2006

AIAA Journal

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An aerodynamic shape optimization technique has been developed for helicopter rotor blades in hover based on a continuous adjoint method on unstructured meshes. For this purpose, the Euler flow solver and the adjoint sensitivity analysis were formulated on the rotating frame of reference. To handle the repeated evaluation of the flow solution and the sensitivity analysis efficiently, the flow and adjoint solvers were parallelized using a domain decomposition strategy. A solution-adaptive mesh refinement technique was adopted to better resolve the tip vortex. Applications were made to the aerodynamic shape optimization of the Caradonna-Tung rotor blade and the UH-60 rotor blade. The results showed that the present method is effective in determining optimum aerodynamic configurations of rotor blades in hover, which require minimum inviscid torque while maintaining the desired thrust level at transonic flight conditions. Nomenclature d = vector of design variables e o = total internal energy g = arbitrary function of flow variables h o = total enthalty I = objective function of unconstraint optimization I o = objective function of constraint optimization k = arbitrary function of design variables n = design iteration ndv = number of design variables p = static pressure Q = vector of flow variables R = residual vector of governing flow equations R = rotor radius r = radial distance along blade span s = vector of search direction U = inertial velocity component normal to domain boundary u, v, w = Cartesian velocity components in inertial frame of reference = variation operator = arbitrary small number = step size = flow density = vector of adjoint variables Subscript L = left side of control surface R = right side of control surface

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Technical Note: Aerodynamic Design of Helicopter Rotor Blade in Forward Flight Using Response Surface Methodology

Cited by 6 publications

References 10 publications

A Survey of Recent Developments in Rotorcraft Design Optimization

A Survey of Recent Developments in Rotorcraft Design Optimization

Stochastic Process Model for the Optimization of a Rotorcraft with Categorical Variables

Aerodynamic Shape Optimization of Hovering Rotor Blades in Transonic Flow Using Unstructured Meshes

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