New strategies for flexibility analysis and design under uncertainty

“…tain process parameters flow rates, temperatures, and so on Ž . and external unmeasured uncertainty ambient conditions ŽWalsh and Perkins, 1996;Raspanti et al, 2000;Bahri et al, . 1996;Bandoni et al, 1994;Figueroa et al, 1996 .…”

Section: Introductionmentioning

confidence: 99%

Optimal process design with model parameter uncertainty and process variability

Rooney

Biegler

2003

AIChE Journal

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“…The Kreisselmeier-Steinhauser (KS) function is used to combine all stresses into a single constraint. [42][43][44] The optimization constraints are listed in Table 3.…”

Section: Resultsmentioning

confidence: 99%

A CAD-Free Approach to High-Fidelity Aerostructural Optimization

Kenway

Kennedy

Martins

2010

13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference

193

119

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Geometry parametrization for high-fidelity multidisciplinary optimization is an important and complex problem. We present a CAD-free geometry parametrization method using a free-from deformation volume approach. This approach yields several important advantages over other parametrization techniques, the most of important of which is the efficient computation of analytic derivatives for gradient-based optimization. A parallel, hybrid, algebraic-linear-elasticity mesh perturbation scheme which produces high quality perturbed meshes with low computational effort is also presented. We couple an Euler CFD solver with a finite-element model that uses fourth-order degenerate shell elements. As a demonstration problem, we perform the aerostructural redesign of a subsonic wing for transonic flight conditions. We show that this optimization problem captures some of the complex multidisciplinary trade-offs inherent in wing design.

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“…It was proved that the KS function is always bigger than the maximum of the aggregated functions and that the limit of KS function will be equal to the maximum when the parameter q approaches positive infinity [22], i.e., KSðÁuÞ ! maxfÁu s g; for all q > 0; ð23Þ…”

Section: Criteria Of Stiffness and Optimization Problemmentioning

confidence: 99%

Stiffness optimization of geometrically nonlinear structures and the level set based solution

Xia

Shi

2016

Int. J. Simul. Multisci. Des. Optim.

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-Load-normalized strain energy increments between consecutive load steps are aggregated through the Kreisselmeier-Steinhauser (KS) function, and the KS function is proposed as a stiffness criterion of geometrically nonlinear structures. A topology optimization problem is defined to minimize the KS function together with the perimeter of structure and a volume constraint. The finite element analysis is done by remeshing, and artificial weak material is not used. The topology optimization problem is solved by using the level set method. Several numerical examples in two dimensions are provided. Other criteria of stiffness, i.e., the end compliance and the complementary work, are compared.

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New strategies for flexibility analysis and design under uncertainty

Cited by 105 publications

References 22 publications

Optimal process design with model parameter uncertainty and process variability

Optimal process design with model parameter uncertainty and process variability

A CAD-Free Approach to High-Fidelity Aerostructural Optimization

Stiffness optimization of geometrically nonlinear structures and the level set based solution

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