Computational Fluid Dynamics Driven Optimization of Blended Wing Body Aircraft

This study investigates the potential of unconventional aircraft transports through numerical optimization. Three distinct configurations are investigated: a box wing, a C-tip blended wing-body, and a braced wing. Each transport is sized for the same regional mission and is subjected to the same optimization strategy based on the Euler equations. The figure of merit is inviscid pressure drag at transonic speed; the nonlinear constraints are lift, pitching moment, and internal volume. The design variables include the section shape and twist distribution of the main lifting surfaces. It is found that the box-wing, C-tip blended-wing-body, and braced-wing configurations investigated here are, respectively, 34.1, 36.2, and 40.3% more efficient than a similarly optimized conventional tube-and-wing configuration. Each optimization revealed, in one way or another, the importance of accounting for flow nonlinearity during the early stages of unconventional aircraft design. For the blended wing-body, the C tip does not appear to provide a drag benefit over a purely vertical winglet, presumably as a result of the compressibility effects prevalent in the C opening. For the braced wing, compressibility effects also lead to a curious result, where the supporting strut finds itself carrying negative lift at the optimum. Nomenclature= span efficiency L = lift, N n = normal force (section), m q ∞ = freestream dynamic pressure, N∕m 2 W = weight, N x, y, z = chordwise, spanwise, and vertical coordinates, m α = angle of attack, deg

Section: Blended Wing/bodymentioning

confidence: 97%

Euler-Equation-Based Drag Minimization of Unconventional Aircraft Configurations

Gagnon

Zingg

2016

“…They optimized the design in 3D using Euler-based computational fluid dynamics (CFD). Peigin and Epstein [10] used a genetic algorithm and reduced-order methods to perform a multipoint drag minimization of the BWB with 93 design variables. They used a full NavierStokes analysis with reduced-order methods.…”

Section: Introductionmentioning

confidence: 99%

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

Lyu

Martins

2014

203

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.

“…with the derivatives of the likelihood then @ @ l ln 10 X ij 10 l kx i ; x j l k p l R ij R ij (7) and…”

Section: A Krigingmentioning

confidence: 99%

“…Szmelter [3] optimized a transonic wing for a combination of minimum drag and deviation from a target pressure at three different lift coefficients. Epstein and Peigin considered the multipoint optimization of an airfoil [4], a business jet wing [5], a civil airliner wing [6], and a blended wing body aircraft [7]. Multipoint optimization has also been employed in high-speed designs with Kim et al [8] considering the multipoint optimization of a supersonic fighter wing and Cliff et al [9] considering the shape optimization of a high-speed civil transport.…”

mentioning

confidence: 98%

“…This can be further hampered if, as is so often the case, the performance evaluation at each design point requires an iteration to achieve a required lift coefficient. The use of a direct optimization technique such as a genetic algorithm is therefore hampered considerably by the associated simulation cost, and typically only local gradient descent [1][2][3]9], response surface methods [8,10], and methods employing a reduced-order model [5][6][7] are practical.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Efficient Multipoint Aerodynamic Design Optimization Via Cokriging

Toal

Keane

2011

Multipoint objective functions are often employed within aerodynamic optimizations to prevent a reduction in offdesign performance. However, this typically results in the need for a significant number of simulations at a variety of design conditions to calculate the objective function. The following paper attempts to address this problem through the application of a multilevel cokriging model within the optimization process. A large number of single-point design simulations are augmented by a smaller number of multipoint simulations. The technique is shown to result in surrogate models as effective as those produced using a traditional multipoint process when optimizing a transonic airfoil but with a reduction in the total number of simulations.between expensive and cheap data K = total number of design conditions n = total number of sample points p = hyperparameter governing smoothness R = correlation matrix r = correlations between known and unknown points w = design point weighting X = matrix of design points x = vector of design variables y = vector of objective function values Zx = Gaussian process = hyperparameter governing correlation = regression constant = mean = scaling parameter 2 = variance = concentrated log likelihood Subscripts c = cheap data d = difference between cheap and expensive data e = expensive data