Multipoint and Multi-Objective Aerodynamic Shape Optimization

Nemec, Marian; Zingg, David W.; Pulliam, Thomas H.

doi:10.2514/1.10415

Cited by 218 publications

(126 citation statements)

References 38 publications

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“…The first application to fluid dynamics is due to Pironneau [73]. The method was then extended by Jameson to perform airfoil shape optimization [37], and since then it has been used to optimize airfoils suitable for multipoint operation [67] and to design laminar-flow airfoils [22]. The adjoint method has been extended to three-dimensional problems, leading to applications such as the aerodynamic shape optimization of complete aircraft configurations [54,76,77] and shape optimization considering both aerodynamics and structures [43,62].…”

Section: Adjoint Methodsmentioning

confidence: 99%

Review and Unification of Methods for Computing Derivatives of Multidisciplinary Computational Models

2013

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. Review and unification of methods for computing derivatives of multidisciplinary computational models. AIAA Journal, 51(11):2582-2599. doi:10.2514 Abstract This paper presents a review of all existing discrete methods for computing the derivatives of computational models within a unified mathematical framework. This framework hinges on a new equationthe unifying chain rule-from which all the methods can be derived. The computation of derivatives is described as a two-step process: the evaluation of the partial derivatives and the computation of the total derivatives, which are dependent on the partial derivatives. Finite differences, the complex-step method, and symbolic differentiation are discussed as options for computing the partial derivatives. It is shown that these are building blocks with which the total derivatives can be assembled using algorithmic differentiation, the direct and adjoint methods, and coupled analytic methods for multidisciplinary systems. Each of these methods is presented and applied to a common numerical example to demonstrate and compare the various approaches. The paper ends with a discussion of current challenges and possible future research directions in this field.

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Section: Adjoint Methodsmentioning

confidence: 99%

Review and Unification of Methods for Computing Derivatives of Multidisciplinary Computational Models

2013

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“…The only aerodynamic input to this computation is the overall lift-to-drag ratio of the aircraft. While a single analysis point is sufficient to estimate the liftto-drag ratio, single-point optimizations (especially in the transonic regime) can produce optimal performance at a single operating point at the cost of significant degradation in other important off-design conditions [52]. To address this issue, we consider multi-point aerostructural optimizations that compute the average performance over multiple flight conditions.…”

Section: Design and Maneuver Conditionsmentioning

confidence: 99%

Multipoint High-Fidelity Aerostructural Optimization of a Transport Aircraft Configuration

Kenway

Martins

2014

Journal of Aircraft

312

184

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This paper presents multi-point high-fidelity aerostructural optimizations of a long-range wide-body transonic transport aircraft configuration. The aerostructural analysis employs Euler CFD with a 2 million cell mesh and a structural finite element model with 300 000 degrees of freedom. The coupled adjoint sensitivity method is used to efficiently compute gradients, enabling the use of gradient-based optimization with respect to hundreds of aerodynamic shape and structural sizing variables. The NASA Common Research Model is used as the baseline configuration, together with a wingbox structure that was designed for this study. Two design optimization problems are solved: one where takeoff gross weight is minimized, and another where fuel burn is minimized. Each optimization uses a multi-point formulation with 5 cruise conditions and 2 maneuver conditions. Each of the optimization problems have 476 design variables, including wing planform, airfoil shape, and structural thickness variables. Optimized results are obtained within 36 hours of wall time using 435 processors. The resulting optimal configurations are discussed and analyzed for the aerostructural trade-offs resulting from each objective. The takeoff gross weight minimization results in a 4.2% reduction in takeoff gross weight with a 6.6% fuel burn reduction, while the fuel-burn optimization resulted in an 11.2% fuel burn reduction with no significant change in the takeoff gross weight.

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“…The composite objective function is J w ft J ft 1 w ft J lt (6) where the subscript ft denotes fully turbulent conditions, and lt denotes free transition. The Pareto front computed by varying w ft is shown in Fig.…”

Section: Off-design Performancementioning

confidence: 99%

“…Introduction S EVERAL algorithms have been developed that can efficiently perform aerodynamic shape optimization [1][2][3][4][5][6]. The designer specifies an objective, operating conditions, constraints, and a set of parameters that define the range of possible geometries.…”

mentioning

confidence: 99%

Aerodynamic Optimization Under a Range of Operating Conditions

Zingg

Elias

2006

AIAA Journal

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In aerodynamic design, good performance is generally required under a range of operating conditions, including off-design conditions. This can be achieved through multipoint optimization. The desired performance objective and operating conditions must be specified, and the resulting optimization problem must be solved in such a manner that the desired performance is achieved. Issues involved in formulating multipoint optimization problems are discussed. A technique is proposed for automatically choosing sampling points within the operating range and their weights to obtain the desired performance over the range of operating conditions. Examples are given involving lift-constrained drag minimization over a range of Mach numbers. Tradeoffs and their implications for the formulation of multipoint problems are presented and discussed.

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Multipoint and Multi-Objective Aerodynamic Shape Optimization

Cited by 218 publications

References 38 publications

Review and Unification of Methods for Computing Derivatives of Multidisciplinary Computational Models

Review and Unification of Methods for Computing Derivatives of Multidisciplinary Computational Models

Multipoint High-Fidelity Aerostructural Optimization of a Transport Aircraft Configuration

Aerodynamic Optimization Under a Range of Operating Conditions

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