Constrained Aerodynamic Optimization of Three-Dimensional Wings Driven by Navier-Stokes Computations

Epstein, Boris; Peigin, S. V.

doi:10.2514/1.10308

Cited by 35 publications

(11 citation statements)

References 13 publications

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“…The method and the corresponding optimization tool OP-TIMAS were successfully applied to the optimization of 2D airfoils [13], 3D transport-type isolated wings [8] and to a demanding test-case of blended-wing-body aircraft [10]. A new technology of aerodynamic design based on the optimization tool is described in [9], where the incorporation of the isolated wing optimization process into the aircraft development project was presented.…”

Section: Introductionmentioning

confidence: 99%

Multiconstrained aerodynamic design of business jet by CFD driven optimization tool

Peigin

Epstein

2008

Aerospace Science and Technology

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Section: Introductionmentioning

confidence: 99%

Multiconstrained aerodynamic design of business jet by CFD driven optimization tool

Peigin

Epstein

2008

Aerospace Science and Technology

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“…The OPTIMAS optimizations conducted here cost about three orders-ofmagnitude more than an objective-function evaluation. For more information on OPTIMAS and NES, see References [2][3][4][5].…”

Section: Optimization and Analysis Methodsmentioning

confidence: 99%

A Pilot Project in Preparation of an Aerodynamic Optimization Workshop with Lessons Learned

Vassberg

Jameson

Peigin

et al. 2008

26th AIAA Applied Aerodynamics Conference

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This paper is an addendum to a recent publication on a Comparative Study of 3D Wing Drag Minimization by Different Optimization Techniques; with a focus on a path forward towards the development of a larger-scale Aerodynamic Optimization Workshop. Three independent optimization efforts were conducted concurrently, each without knowledge of the others' results. The resulting "optimal" wings were then cross analyzed by each of the CFD methods used in the optimization phase, and by a fourth CFD method which was not used to develop any of the geometries. The incremental improvements, as predicted by these cross analyses, are compared to establish a level of uncertainty in the performance improvements and identify any unusual trends. The baseline geometry of this pilot project is the DPW-W1 wing, which was placed into the public domain by the Drag Prediction Workshop as a test case for the third workshop of June 2006. Lessons learned from this collaborative effort are documented and suggestions are provided to aid in the preparation of a possible future workshop on aerodynamic shape optimization.

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“…Recently, there has been a growing interest in evolutionary algorithms that do not make use of the gradient (Gonzalez et al, 2004;LeDoux, et al, 2004;Epstein and Peigin, 2005). While they incur extremely high computational costs, these algorithms have the advantage that they can avoid being trapped in a local minimum.…”

Section: Aerodynamic Optimization and Control Theorymentioning

confidence: 99%

Optimization Methods in Computational Fluid Dynamics

Jameson

2010

Encyclopedia of Aerospace Engineering

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This chapter discusses the role that computational fluid dynamics (CFD) plays in the design of aircraft. An overview of the design process is provided, covering some of the typical decisions that a design team addresses within a multi‐disciplinary environment. On a very regular basis, trade‐offs between disciplines have to be made where a set of conflicting requirements exist. Within an aircraft development project, we focus on the aerodynamic design problem and review how this process has been advanced, first with the improving capabilities of traditional computational fluid dynamics analyses and then with aerodynamic optimizations based on these increasingly accurate methods. We then review the formulation and application of optimization techniques based on control theory for aerodynamic shape design in both potential flow and inviscid compressible flow. The theory is applied to a system defined by the partial differential equations of the flow, with the boundary shape acting as the control. The Frechet derivative of the cost function is determined via the solution of an adjoint partial differential equation, and the boundary shape is then modified in a direction of descent. This process is repeated until an optimum solution is approached. Each design cycle requires the numerical solution of both the flow and the adjoint equations, leading to a computational cost roughly equal to the cost of two flow solutions. Several case studies are provided, which highlight the benefits of utilizing automatic aerodynamic shape optimization.

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Constrained Aerodynamic Optimization of Three-Dimensional Wings Driven by Navier-Stokes Computations

Cited by 35 publications

References 13 publications

Multiconstrained aerodynamic design of business jet by CFD driven optimization tool

Multiconstrained aerodynamic design of business jet by CFD driven optimization tool

A Pilot Project in Preparation of an Aerodynamic Optimization Workshop with Lessons Learned

Optimization Methods in Computational Fluid Dynamics

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