Coupled aerostructural topology optimization using a level set method for 3D aircraft wings

Dunning, Peter D.; Stanford, Bret; Kim, Hyunsun

doi:10.1007/s00158-014-1200-1

Cited by 50 publications

(22 citation statements)

References 37 publications

(49 reference statements)

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“…We also reinitialize the implicit function to a signed distance function every 30 iterations during optimization. For full details of our numerical implementation of the conventional level‐set method in 3D, see .…”

Section: Level‐set Topology Optimization With Buckling Constraintsmentioning

confidence: 99%

Level‐set topology optimization with many linear buckling constraints using an efficient and robust eigensolver

Dunning

Ovtchinnikov

Scott

et al. 2016

Numerical Meth Engineering

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SUMMARYLinear buckling constraints are important in structural topology optimization for obtaining designs that can support the required loads without failure. During the optimization process, the critical buckling eigenmode can change; this poses a challenge to gradient-based optimization and can require the computation of a large number of linear buckling eigenmodes. This is potentially both computationally difficult to achieve and prohibitively expensive. In this paper, we motivate the need for a large number of linear buckling modes and show how several features of the block Jacobi conjugate gradient (BJCG) eigenvalue method, including optimal shift estimates, the reuse of eigenvectors, adaptive eigenvector tolerances and multiple shifts, can be used to efficiently and robustly compute a large number of buckling eigenmodes. This paper also introduces linear buckling constraints for level-set topology optimization. In our approach, the velocity function is defined as a weighted sum of the shape sensitivities for the objective and constraint functions. The weights are found by solving an optimization sub-problem to reduce the mass while maintaining feasibility of the buckling constraints. The effectiveness of this approach in combination with the BJCG method is demonstrated using a 3D optimization problem.

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Section: Level‐set Topology Optimization With Buckling Constraintsmentioning

confidence: 99%

Level‐set topology optimization with many linear buckling constraints using an efficient and robust eigensolver

Dunning

Ovtchinnikov

Scott

et al. 2016

Numerical Meth Engineering

Self Cite

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show abstract

“…In general, aerodynamic objectives do not have this property and so a careful formulation that recovers it may be required, and explicit penalization of nondiscreteness has also been proposed (Stanford and Ifju 2009). Alternative topology optimization methods such as the level-set approach (e.g., Dunning et al 2015) inherently produce a solid-void topology. However, the numerical challenges of the density-based approach are better understood (e.g., control of feature sizes) and their natural ability to introduce new holes, and be driven by a general optimizer (which easily allows other variables to be included in the optimization) is not shared by all level-set approaches.…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic-driven topology optimization of compliant airfoils

Gomes

Palacios

2020

Struct Multidisc Optim

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A strategy for density-based topology optimization of fluid-structure interaction problems is proposed that deals with some shortcomings associated to non stiffness-based design. The goal is to improve the passive aerodynamic shape adaptation of highly compliant airfoils at multiple operating points. A two-step solution process is proposed that decouples global aeroelastic performance goals from the search of a solid-void topology on the structure. In the first step, a reference fully coupled fluid-structure problem is solved without explicitly penalizing non-discreteness in the resulting topology. A regularization step is then performed that solves an inverse design problem, akin to those in compliant mechanism design, which produces a discrete-topology structure with the same response to the fluid loads. Simulations are carried out with the multi-physics suite SU2, which includes Reynolds-averaged Navier-Stokes modeling of the fluid and hyper-elastic material behavior of the geometrically nonlinear structure. Gradient-based optimization is used with the exterior penalty method and a large-scale quasi-Newton unconstrained optimizer. Coupled aerostructural sensitivities are obtained via an algorithmic differentiation based coupled discrete adjoint solver. Numerical examples on a compliant aerofoil with performance objectives at two Mach numbers are presented.

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“…Alternatively, three-dimensional parametrizations are used where the optimizer is allowed to place material anywhere in the wing. These problems are typically solved using solid-isotropic-material-with-penalization (SIMP) methods [8][9][10] or level-set methods [11,12], as demonstrated by, amongst others, Maute and Reich [13], Dunning et al [14], and Kambampati et al [15] for aircraft wings and wing boxes. A combination of a conventional wing structure layout with topologically optimized parts is investigated by Stanford and Dunning [16] where an orthogonal rib-spar layout is used but where topology optimization is applied to the ribs and spars.…”

Section: Introductionmentioning

confidence: 99%

Design Methodology for Aeroelastic Tailoring of Additively Manufactured Lattice Structures Using Low-Order Methods

Opgenoord

Willcox

2019

AIAA Journal

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Additively-manufactured wings can be aeroelastically tailored, which offers potential for increased air vehicle efficiency via lighter structures. This paper develops a methodology to design aeroelastically-tailored wings using additively-manufactured lattice structures. Adaptive meshing techniques are used to design the topology of the lattice to align with the load direction and the lattice is optimized to minimize the structural weight and to improve the flutter margin. To alleviate the computational effort of aeroelastically tailoring a structure, a low-order model for the dynamics of the lattice structure is developed. This structural low-order model is coupled to a previously-developed physics-based transonic flutter model to compute the aeroelastic behavior of wings with internal lattice structures. The structural low-order model is validated by comparing against the full-order structural dynamics model. Finally, the design methodology is applied to the design of an internal structure of an aircraft wing to increase that wing's flutter speed.

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Coupled aerostructural topology optimization using a level set method for 3D aircraft wings

Cited by 50 publications

References 37 publications

Level‐set topology optimization with many linear buckling constraints using an efficient and robust eigensolver

Level‐set topology optimization with many linear buckling constraints using an efficient and robust eigensolver

Aerodynamic-driven topology optimization of compliant airfoils

Design Methodology for Aeroelastic Tailoring of Additively Manufactured Lattice Structures Using Low-Order Methods

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