A Chimera approach to aerodynamic shape optimization for the compressible, high-Re Navier-Stokes equations

Elliott, J.; Herling, W. W.

doi:10.2514/6.2000-4729

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…The first is based on response surfaces. 9 The second is based on a Boeing developed Sequential Quadratic Programming method using the BFGS Hessian updating method 10 which can be used with finite difference gradients or with the Boeing developed OVERADJ method 11,12 to supply the gradients. MDOPT is most frequently used with OVERFLOW or TRANAIR as the underlying analysis method.…”

Section: Aerodynamic Optimization Methodsmentioning

confidence: 99%

A Study Based on the AIAA Aerodynamic Design Optimization Discussion Group Test Cases

LeDoux

Young

Fugal

et al. 2015

53rd AIAA Aerospace Sciences Meeting

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The fourth model problem from the AIAA Aerodynamic Design Optimization Discussion Group is studied, referred to below as the ADODG CRM wing test case. Two optimization software packages are utilized, TRANAIR design and MDOPT (using both Design of Experiments methods as well as gradient based methods) with OVERFLOW. The resulting designed and associated baseline geometries are cross-analyzed using OVERFLOW and TRANAIR. Previous results using TRANAIR design were run using standard engineering practices and resulted in somewhat non-optimal wings. New results generated using more cycles of design have significantly lower drag and are closer to optimal. In addition, we examine using gradients from an adjoint method in OVERFLOW with the MDOPT tool. Nomenclature a = Local speed of sound b = Wing Span CF D = Computational Fluid Dynamics C D = Drag Coefficient = Drag q∞S ref C L = Lift Coefficient = Lif t q∞S ref C M = Pitching-Moment Coefficient C P = Pressure Coefficient = P −P∞ q∞ C ref = Wing Reference Chord MAC C f = Local Coefficient of Skin Friction count = Drag Coefficient Unit = 0.0001 DP W = Drag Prediction Workshop IBL = Integral Boundary Layer Method M = Mach Number = V a P 0 = Total Pressure q = Dynamic Pressure = 1 2 ρV 2 RAN S = Reynolds-Averaged Navier-Stokes Re = Reynolds number = ρ∞ V∞ C ref µ∞ S ref = Reference Area α = Angle-of-Attack ∆ = Difference in Quantity (New -Baseline) η = Fraction of Wing Semi-Span ∞ = Signifies Freestream Conditions

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Section: Aerodynamic Optimization Methodsmentioning

confidence: 99%

A Study Based on the AIAA Aerodynamic Design Optimization Discussion Group Test Cases

LeDoux

Young

Fugal

et al. 2015

53rd AIAA Aerospace Sciences Meeting

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“…where Q is the steady-state solution of Eq. 4 for a given set of design variables R(X, Q) = 0 (12) Note that this is the discrete equivalent of Eq. 2.…”

Section: A Formulationmentioning

confidence: 99%

“…The adjoint method, as well as the closely related flow-sensitivity (or direct) method, has been extensively analyzed and validated for the Euler and Navier-Stokes equations discretized on structured [11][12][13][14] and unstructured meshes. [15][16][17] When the adjoint equation is derived from the discrete form of the flow equations, which is the approach used in this work, the accuracy of the adjoint solution primarily depends on the accuracy of the linearization of the flow equations.…”

Section: Introductionmentioning

confidence: 99%

Adjoint Formulation for an Embedded-Boundary Cartesian Method

Nemec

Aftosmis

Murman

et al. 2005

43rd AIAA Aerospace Sciences Meeting and Exhibit

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A discrete-adjoint formulation is presented for the three-dimensional Euler equations discretized on a Cartesian mesh with embedded boundaries. The solution algorithm for the adjoint and flow-sensitivity equations leverages the Runge-Kutta time-marching scheme in conjunction with the parallel multigrid method of the flow solver. The matrix-vector products associated with the linearization of the flow equations are computed on-the-fly, thereby minimizing the memory requirements of the algorithm at a computational cost roughly equivalent to a flow solution. Three-dimensional test cases, including a wing-body geometry at transonic flow conditions and an entry vehicle at supersonic flow conditions, are presented. These cases verify the accuracy of the linearization and demonstrate the efficiency and robustness of the adjoint algorithm for complex-geometry problems.

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“…Combinations of the linear and non-linear flow solvers can be invoked to generate flow derivatives more efficiently and/or to enlarge the number of points developed for CD vs. CL performance module input tables. As part of the aerodynamic process a tool (OVERSENS) is in development to provide OVERFLOW chimera grid sensitivities for flow derivatives 17 .…”

Section: Aerodynamicsmentioning

confidence: 99%

MDOPT - A Multidisciplinary Design Optimization System Using Higher Order Analysis Codes

LeDoux¹,

Herling²,

Fatta³

et al. 2004

10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference

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A Multidisciplinary Design Optimization (MDOPT) framework has been developed for air vehicle design and analysis. The developed MDOPT system contains a collection of technology modules for performing optimization studies by means of a Graphical User Interface (GUI), and combining robust numerical optimization schemes with higher order computational analysis. A variety of multidisciplinary objective and constraint functions are available, including aerodynamic, weight, mission performance, and stability and control characteristics. This paper is intended to provide an overview of the MDOPT development and test case results for a generic fighter configuration, funded under an Air Force Contract, F33615-98-2-3014, with Boeing cost match funds. The period of development was established with contract activity beginning in September 1998 and ending April 2002. Continued MDOPT development and design applications are being pursued within Boeing using internal funding. I.

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A Chimera approach to aerodynamic shape optimization for the compressible, high-Re Navier-Stokes equations

Cited by 4 publications

References 4 publications

A Study Based on the AIAA Aerodynamic Design Optimization Discussion Group Test Cases

A Study Based on the AIAA Aerodynamic Design Optimization Discussion Group Test Cases

Adjoint Formulation for an Embedded-Boundary Cartesian Method

MDOPT - A Multidisciplinary Design Optimization System Using Higher Order Analysis Codes

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