Aerodynamic Shape Optimizations of a Blended Wing Body Configuration for Several Wing Planforms

Méheut, Michaël; Arntz, Aurélien; Carrier, Gérald

doi:10.2514/6.2012-3122

Cited by 20 publications

(11 citation statements)

References 3 publications

(3 reference statements)

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“…Reference source not found., based on the Karush-Kuhn-Tucker conditions of constrained problem optimality [17] [19] is used for these studies. At convergence of the constrained optimization problem, the following equation is satisfied:…”

Section: Optimization Stopping Criteria and Convergence Verificationmentioning

confidence: 99%

Gradient-Based Aerodynamic Optimization with the elsA Software

Carrier¹,

Destarac²,

Dumont³

et al. 2014

52nd Aerospace Sciences Meeting

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This paper describes the work performed by ONERA and Airbus to solve several aerodynamic optimization problems proposed in 2013 by the AIAA Optimization Discussion Group (ADODG). Three of the four test cases defined by this group have been addressed, respectively a 2D invicid, non-lifting, transonic airfoil optimization problem, a 2D RANS transonic airfoil optimization problem and a 3D RANS transonic wing optimization problem. All three problems have been investigated using local, gradient-based, optimization techniques and the elsA[1][2] CFD software and its adjoint capability. Through these three optimization exercises, several generic issues introduced by aerodynamic gradient-based optimization have been investigated. Among the investigated aspects are the impact of the geometry parameterization (nature and dimension), of the accuracy of the gradient calculation method, optimization algorithm and presence of constraints in the optimization problem. NomenclatureC p = pressure coefficient CD = total drag coefficient CDp = pressure drag coefficient CDf = friction drag coefficient CDw = wave drag coefficient CDvp = viscous pressure drag coefficient CL = lift coefficient CM = pitching moment coefficient c ref = chord reference d.c. = drag counts (0.0001) Ma = Mach number Re = Reynolds number AoA = Angle of attack f = objective function g = inequality constraint 1

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Section: Optimization Stopping Criteria and Convergence Verificationmentioning

confidence: 99%

Gradient-Based Aerodynamic Optimization with the elsA Software

Carrier¹,

Destarac²,

Dumont³

et al. 2014

52nd Aerospace Sciences Meeting

Self Cite

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“…Kuntawala et al [11,12] studied BWB planform and shape drag minimization using Euler CFD with an adjoint implementation. Meheut et al [13] performed a shape optimization of the AVECA flying wing planform subject to a low-speed takeoff rotational constraint. They optimized a total of 151 design variables, and they used CFD with a frozen-turbulence (Reynolds-averaged Navier-Stokes) RANS adjoint to compute the gradient.…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic Design Optimization Studies of a Blended-Wing-Body Aircraft

Lyu

Martins

2014

Journal of Aircraft

202

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The blended-wing body is an aircraft configuration that has the potential to be more efficient than conventional large transport aircraft configurations with the same capability. However, the design of the blended-wing is challenging due to the tight coupling between aerodynamic performance, trim, and stability. Other design challenges include the nature and number of the design variables involved, and the transonic flow conditions. To address these issues, we perform a series of aerodynamic shape optimization studies using Reynolds-averaged Navier-Stokes computational fluid dynamics with a Spalart-Allmaras turbulence model. A gradient-based optimization algorithm is used in conjunction with a discrete adjoint method that computes the derivatives of the aerodynamic forces. A total of 273 design variables-twist, airfoil shape, sweep, chord, and span-are considered. The drag coefficient at the cruise condition is minimized subject to lift, trim, static margin, and center plane bending moment constraints. The studies investigate the impact of the various constraints and design variables on optimized blended-wing-body configurations. The lowest drag among the trimmed and stable configurations is obtained by enforcing a 1% static margin constraint, resulting in a nearly elliptical spanwise lift distribution. Trim and static stability are investigated at both on-and off-design flight conditions. The single-point designs are relatively robust to the flight conditions, but further robustness is achieved through a multi-point optimization.

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“…III and IV for design, control, and simulation are described. The configuration studied in this paper is a long-range BWB for which the planform results from optimization studies on high-speed performance with constraints on the low-speed pitching moment [34]. The focus of this work is the sizing of control surfaces; thus, the planform is considered constant.…”

Section: Aircraft Flight Dynamics and Controlmentioning

confidence: 99%

“…is supposed known from previous studies [34], and it is compared with the lift gradient computed by AVL for the reference configuration C AVL L δm i (see Fig. 5a).…”

Section: B Computation Of Aerodynamic Models For Discretized Controlmentioning

confidence: 99%