Parallel Solution Methods for Aerostructural Analysis and Design Optimization

Kennedy, Graeme J.; Martins, Joaquim R. R. A.

doi:10.2514/6.2010-9308

Cited by 49 publications

(68 citation statements)

References 43 publications

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“…The latter approach was used to produce the results presented in Section IV. More details on these approaches can be found in previous work by the authors [32,45,34].…”

Section: Aerostructural Analysismentioning

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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309

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 latter approach was used to produce the results presented in Section IV. More details on these approaches can be found in previous work by the authors [32,45,34].…”

Section: Aerostructural Analysismentioning

confidence: 99%

“…The structural solver used in this work is the toolkit for the analysis of composite structures (TACS) by Kennedy and Martins [45]. For the thin-shell problems we encounter in wing structural models, it is possible to have matrix condition numbers that exceed O 10 9 .…”

Section: Aerostructural Analysismentioning

confidence: 99%

Multipoint High-Fidelity Aerostructural Optimization of a Transport Aircraft Configuration

Kenway

Martins

2014

Journal of Aircraft

Self Cite

309

184

View full text Add to dashboard Cite

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“…In many cases, such as Newton-based methods, the computational effort of an algorithm depends heavily on the run time and memory requirements of the derivative computation. Examples of such algorithms include Newton-Krylov methods applied to the solution of the Euler equations [34] and coupled aerostructural equations [11,12,42,44]. There are generalpurpose numerical libraries that implement many of these methods [6,33].…”

Section: Introductionmentioning

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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“…This approach usually exhibits slow convergence rates. Re-ordering the sequence of disciplines can improve the convergence rate of Gauss-Seidel [102], but even better convergence rates can be achieved through the use of Newton-based methods [103]. Because of the sequential nature of the Gauss-Seidel iteration, we cannot evaluate the disciplines in parallel and cannot apply our convention for compacting the XDSM.…”

Section: E Multidisciplinary Feasible (Mdf)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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Parallel Solution Methods for Aerostructural Analysis and Design Optimization

Cited by 49 publications

References 43 publications

Multipoint High-Fidelity Aerostructural Optimization of a Transport Aircraft Configuration

Multipoint High-Fidelity Aerostructural Optimization of a Transport Aircraft Configuration

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

Multidisciplinary Design Optimization: A Survey of Architectures

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