An asymmetric suboptimization approach to aerostructural optimization

Chittick, Ian R.; Martins, Joaquim R. R. A.

doi:10.1007/s11081-008-9046-2

Cited by 55 publications

(39 citation statements)

References 24 publications

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“…They subsequently considered the design of a subsonic, forward-swept transport aircraft wing [10]. This failure of sequential optimization to produce the optimal result is further explained by Chittick and Martins [11]. When performing sequential optimization, the optimizer does not have the information necessary for optimal aeroelastic tailoring.…”

Section: Introductionmentioning

confidence: 99%

“…We present multi-point optimizations that minimize two different objectives: takeoff gross weight (TOGW) and fuel burn. By optimizing with respect to all the design variables simultaneously, we ensure that we find a true multidisciplinary optimum that minimizes the corresponding objective function, as opposed to a sequential suboptimal result [11,10].…”

Section: Introductionmentioning

confidence: 99%

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Multipoint High-Fidelity Aerostructural Optimization of a Transport Aircraft Configuration

Kenway

Martins

2014

Journal of Aircraft

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315

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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multipoint High-Fidelity Aerostructural Optimization of a Transport Aircraft Configuration

Kenway

Martins

2014

Journal of Aircraft

Self Cite

315

184

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“…Another example is aerostructural optimization, in which a nonlinear aerodynamics solver may require an order of magnitude more time to run than a linear structural solver [120]. By decomposing the optimization problem, we can balance the processor work loads by allowing discipline analyses with lower computational costs to perform more optimization on their own.…”

Section: Distributed Architecturesmentioning

confidence: 99%

“…The ASO architecture [120] is a new distributed-MDF architecture. It was motivated by high-fidelity aerostructural optimization, where the aerodynamic analysis typically requires an order of magnitude more time to complete than the structural analysis [195].…”

Section: J Asymmetric Subspace Optimization (Aso)mentioning

confidence: 99%

Multidisciplinary Design Optimization: A Survey of Architectures

2013

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Multidisciplinary design optimization (MDO) is a field of research that studies the application of numerical optimization techniques to the design of engineering systems involving multiple disciplines or components. Since the inception of MDO, various methods (architectures) have been developed and applied to solve MDO problems. This paper provides a survey of all the architectures that have been presented in the literature so far. All architectures are explained in detail using a unified description that includes optimization problem statements, diagrams, and detailed algorithms. The diagrams show both data and process flow through the multidisciplinary system and computational elements, which facilitates the understanding of the various architectures, and how they relate to each other.A classification of the MDO architectures based on their problem formulations and decomposition strategies is also provided, and the benefits and drawbacks of the architectures are discussed from both a theoretical and experimental perspective. For each architecture, several applications to the solution of engineering design problems are cited. The result is a comprehensive but straightforward introduction to MDO for non-specialists, and a reference detailing all current MDO architectures for specialists.

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“…Since simultaneous optimization yields better results than those obtained through performing sequential optimization [26,15,27], the real potential of including aeroservoelastic synthesis in the aircraft design process can only be realized with a simultaneous optimization approach. In a traditional design process, the aircraft configuration is designed first, and the control system is designed second.…”

Section: Introductionmentioning

confidence: 99%

Aeroservoelastic Design Optimization of a Flexible Wing

Haghighat

Martins

Liu

2012

Journal of Aircraft

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In this paper a multidisciplinary design optimization framework is developed that integrates control system design with aerostructural design. The equations of motion are derived for a flexible aircraft and used to perform aeroservoelastic analysis. The objective of this framework is to go beyond the current limits of aircraft performance through simultaneous design optimization of aerodynamic shape, structural sizing and control system. The control system uses load alleviation to reduce the critical structural loads. Time-domain analysis of the aircraft performing an altitude change maneuver and encountering an atmospheric gust is included in the design process. The optimal trade-off between aerodynamics, structures and control system is found by maximizing the endurance subjected to stress and maneuverability constraints. Two cases -with and without load alleviation system -are considered. Due to the proposed MDO framework, the inclusion of load alleviation system in design leads to a significant increase in endurance performance.

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An asymmetric suboptimization approach to aerostructural optimization

Cited by 55 publications

References 24 publications

Multipoint High-Fidelity Aerostructural Optimization of a Transport Aircraft Configuration

Multipoint High-Fidelity Aerostructural Optimization of a Transport Aircraft Configuration

Multidisciplinary Design Optimization: A Survey of Architectures

Aeroservoelastic Design Optimization of a Flexible Wing

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