Aerodynamic Studies for Blended Wing Body Aircraft

Qin, Ning; Vavalle, Armando; Moigne, Alan Le; Laban, M.; Hackett, K.; Weinerfelt, Per; Netherland, Nlr

doi:10.2514/6.2002-5448

Cited by 58 publications

(29 citation statements)

References 6 publications

(7 reference statements)

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“…In Figure 15(a), the lift distribution of the planform-optimized configuration (red line) is compared to the previous ones: the trend resembles an averaged elliptical/triangular distribution, which provides the best aerodynamic trade off, as proved by Qin et al 15,16 This is mainly obtained by sweeping the outboard wing further back to 33.8 • , reducing the taper ratio from 0.15 to 0.13 and increasing the nose-down wash out (Table 6). This last modification is responsible for the decrease in local lift coefficient over the outboard wing, as displayed in Figure 15(b).…”

Section: Su2 Euler Planform Optimization: From Config 1 To Configmentioning

confidence: 91%

Aerodynamic Design of a Flying V Aircraft

Faggiano

Vos

Baan³

et al. 2017

17th AIAA Aviation Technology, Integration, and Operations Conference

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A Flying V aircraft is a tailless, V-shaped flying wing with two cylindrical pressurized cabins placed in the wing leading edge and two over-the-wing engines. Elevons provide longitudinal and lateral control while two tip-mounted vertical tails double as winglets. The goal of the presented study is to estimate the lift-to-drag ratio of this configuration at the cruise condition: M = 0.85, h = 13, 000m, and CL = 0.26. A vortex-lattice method is used to rapidly investigate the feasible design space, whereas an Euler solver on an unstructured grid is adopted for a more accurate wave and vortex-induced drag estimation. The profile drag is computed by an empirical method. The NASA Common Research Model is adopted as a benchmark with an estimated lift-to-drag ratio of 18.9. The three-dimensional geometry of the Flying V is generated according to a multi-level parametrization: the planform shape is parametrized with 10 variables, five wing sections are identified and described by a total of 43 parameters, while the winglet planform is defined by 3 additional variables. After a multi-fidelity design space exploration, two design approaches are investigated: a dual-step optimization, where planform and airfoil variables are subsequently varied, and a single-step optimization, where planform and airfoil variables are varied simultaneously. The highest lift-to-drag ratio is attained with the single-step optimized configuration and amounts to 23.7. It is therefore concluded that the Flying V Aircraft can have a 25% higher lift-to-drag ratio than the reference aircraft.

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Section: Su2 Euler Planform Optimization: From Config 1 To Configmentioning

confidence: 91%

Aerodynamic Design of a Flying V Aircraft

Faggiano

Vos

Baan³

et al. 2017

17th AIAA Aviation Technology, Integration, and Operations Conference

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“…An FSQP algorithm is used in the current research and this was originally developed in [16][17][18]. This particular algorithm has been implemented across a wide range of optimization problems, most relevant to the application here is the work of Qin and coworkers [35][36][37][38] where the algorithm was used for CFD-based-constrained optimization of a blended wing body using an inviscid adjoint solver to obtain the sensitivities. The sensitivities are obtained here via finite-differences, since a generic optimization tool, independent of the flowsolver used, is the ultimate objective of this work.…”

Section: Optimization Methodsmentioning

confidence: 99%

High‐fidelity aerodynamic shape optimization of modern transport wing using efficient hierarchical parameterization

Morris

Allen

Rendall

2009

Numerical Methods in Fluids

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SUMMARYAerodynamic shape optimization technology is presented, using an efficient domain element parameterization approach. This provides a method that allows geometries to be parameterized at various levels, ranging from gross three-dimensional planform alterations to detailed local surface changes. Design parameters control the domain element point locations and, through efficient global interpolation functions, deform both the surface geometry and corresponding computational fluid dynamics volume mesh, in a fast, high quality, and robust fashion. This results in total independence from the mesh type (structured or unstructured), and optimization independence from the flow-solver is achieved by obtaining gradient information for an advanced gradient-based optimizer by finite-differences. Hence, the optimization tool can be used in conjunction with any flow-solver and/or mesh generator. Results have been presented recently for two-dimensional aerofoil cases, and shown impressive results; drag reductions of up to 45% were demonstrated using only 22 active design parameters. This paper presents the extension of these methods to three dimensions, with results for highly constrained optimization of a modern aircraft wing in transonic cruise. The optimization uses combined global and local parameters, giving 388 design variables, and produces a shock-free geometry with an 18% reduction in drag, with the added advantage of significantly reduced root moments.

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“…The quality and applicability of results gained by numerical optimization are inherently dependent on the fidelity of the analysis tool used, and so developing a modular approach means that the complexity and cost of each module required at various stages of the design process can be changed, in terms of aerodynamic or fluid dynamic fidelity, number of surface shape design degrees of freedom, and the complexity of the optimization scheme. Computational fluid dynamics (CFD) is at the forefront of aerodynamic analysis capabilities, and application of numerical optimization algorithms with compressible CFD has been used in numerous optimizations of two-dimensional aerofoil sections, 1, 2 three-dimensional aircraft, [3][4][5] and three-dimensional aeroelastic aircraft, 6 and there has been a recent increase in activity in the rotor area. [7][8][9][10][11] The authors have also presented work in this area, having developed a modularised, generic optimization tool, that is flow-solver and mesh type independent, and applicable to any aerodynamic problem.…”

Section: Background and Introductionmentioning

confidence: 99%

Metric-Based Mathematical Derivation of Aerofoil Design Variables

Poole

Allen

Rendall

2014

10th AIAA Multidisciplinary Design Optimization Conference

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Derivation of fundamental geometric aerofoil design variables is considered. An efficient parameterization and deformation scheme is important in an aerodynamic shape parameterization framework to allow flexible deformation of the surface and volume mesh with full design space coverage attainable. To allow full design space coverage a method has been developed to allow the derivation of aerofoil geometric design variables, which is done by the mathematical decomposition of a training library. The resulting geometric modes are independent of parameterization scheme, surface and volume mesh, and flow solver, thus are generally applicable. Furthermore, a benchmark performance measure, the aerofoil technology factor, has been incorporated into the scheme, to allow intelligent, metric-based selection of the training library, to ensure high performance aerofoil modes are extracted. Three criteria for the selection of the training library are considered: high performance; varied performance; random selection. Results are presented for several geometric shape recovery problems, and these show that having a wide variety of aerofoil profiles in the training data produces modes that have greater design space coverage and are able to reconstruct target aerofoils to a greater accuracy than only having a high performance training library. I. Background and IntroductionNumerical simulation methods are now used routinely in industrial design, and increasing computer power has resulted in their integration into the optimization process being widely accepted. Integrating an effective geometry control method with an aerodynamic model and a numerical optimization scheme can result in an aerodynamic shape optimization approach, which is invaluable during design. The quality and applicability of results gained by numerical optimization are inherently dependent on the fidelity of the analysis tool used, and so developing a modular approach means that the complexity and cost of each module required at various stages of the design process can be changed, in terms of aerodynamic or fluid dynamic fidelity, number of surface shape design degrees of freedom, and the complexity of the optimization scheme. Computational fluid dynamics (CFD) is at the forefront of aerodynamic analysis capabilities, and application of numerical optimization algorithms with compressible CFD has been used in numerous optimizations of two-dimensional aerofoil sections, 1, 2 three-dimensional aircraft, 3-5 and three-dimensional aeroelastic aircraft, 6 and there has been a recent increase in activity in the rotor area. 7-11 The authors have also presented work in this area, having developed a modularised, generic optimization tool, that is flow-solver and mesh type independent, and applicable to any aerodynamic problem. [12][13][14] The key aspect of a flexible optimization and design process is an effective geometry parameterization and surface control technique. The effectiveness of these techniques can be measured in terms of being i) flexible enough to allow suff...

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Aerodynamic Studies for Blended Wing Body Aircraft

Cited by 58 publications

References 6 publications

Aerodynamic Design of a Flying V Aircraft

Aerodynamic Design of a Flying V Aircraft

High‐fidelity aerodynamic shape optimization of modern transport wing using efficient hierarchical parameterization

Metric-Based Mathematical Derivation of Aerofoil Design Variables

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