Multi-fidelity robust aerodynamic design optimization under mixed uncertainty

Shah, Harsheel R.; Hosder, Serhat; Koziel, Slawomir; Tesfahunegn, Yonatan A.; Leifsson, Leifur

doi:10.1016/j.ast.2015.04.011

Cited by 62 publications

(15 citation statements)

References 21 publications

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“…Most examples of design using optimization under mixed uncertainties to date use a weighted sum combination of average statistical moments and intervals of statistical moments [9][10][11][12]. For example, a common choice is…”

Section: B Existing Approaches For Optimization Under Uncertaintymentioning

confidence: 99%

“…This poses the question of how to use this mixed uncertainty information within an optimization. Existing approaches for design using optimization under mixed uncertainties are few: most examples to date use a weighted sum combination of average statistical moments and intervals of statistical moments [9][10][11][12]. However, such a formulation does not necessarily represent the goal of OUU (this is discussed further in Section II:B), and so in this work we develop an approach to optimization under mixed probabilistic and interval uncertainties that overcomes some of the limitations of the current state-of-the-art.…”

Section: Introductionmentioning

confidence: 99%

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Extending Horsetail Matching for Optimization Under Probabilistic, Interval, and Mixed Uncertainties

2018

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This paper presents a new approach for optimization under uncertainty in the presence of probabilistic, interval and mixed uncertainties, avoiding the need to specify probability distributions on uncertain parameters when such information is not readily available. Existing approaches for optimization under these types of uncertainty mostly rely on treating combinations of statistical moments as separate objectives, but this can give rise to stochastically dominated designs. Here, horsetail matching is extended for use with these types of uncertainties to overcome some of the limitations of existing approaches. The formulation delivers a single, differentiable metric as the objective function for optimization. It is demonstrated on algebraic test problems, the design of a wing using a low-fidelity coupled aero-structural code, and the aerodynamic shape optimization of a wing using computational fluid dynamics analysis.

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Section: B Existing Approaches For Optimization Under Uncertaintymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extending Horsetail Matching for Optimization Under Probabilistic, Interval, and Mixed Uncertainties

2018

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“…Finally we compare horsetail matching to the method commonly suggested in the literature for optimization under mixed uncertainties: using a weighted sum of averages and intervals of statistical moments of the CDFs that make up the horsetail plot. [25][26][27][28] The following weighted sum of three objectives is optimized:…”

Section: Comparsion To the Weighted Sum Approachmentioning

confidence: 99%

“…24 Examples of robust optimization under mixed uncertainties to date almost exclusively use a weighted sum combination of average statistical moments and intervals of statistical moments. [25][26][27][28] For example,…”

Section: Introductionmentioning

confidence: 99%

Horsetail Matching for Optimization Under Probabilistic, Interval and Mixed Uncertainties

Cook

Jarrett

Willcox

2017

19th AIAA Non-Deterministic Approaches Conference

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The importance of including uncertainties in the design process of aerospace systems is becoming increasingly recognized, leading to the recent development of many techniques for optimization under uncertainty. Most existing methods represent uncertainties in the problem probabilistically; however, in many real life design applications it is often difficult to assign probability distributions to uncertainties without making strong assumptions. Existing approaches for optimization under different types of uncertainty mostly rely on treating combinations of statistical moments as separate objectives, but this can give rise to stochastically dominated designs. Horsetail matching is a flexible approach to optimization under any mix of probabilistic and interval uncertainties that overcomes some of the limitations of existing approaches. The formulation delivers a single, differentiable metric as the objective function for optimization. It is demonstrated on algebraic test problems and the design of a flying wing using a coupled aero-structural analysis code.

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“…Instead we use the closed loop receding horizon control to allow for feedback during the time evolution (see also [2]). To deal with the stochastic nature of the model we employ the space mapping approach [4,36], which allows for the control of a high fidelity model (here the stochastic one) by the optimization of a surrogate model (here the deterministic one).…”

Section: Introductionmentioning

confidence: 99%

Space mapping-based receding horizon control for stochastic interacting particle systems: dogs herding sheep

Totzeck

Pinnau

2020

J.Math.Industry

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Control of stochastic interacting particle systems is a non-trivial task due to the high dimensionality of the problem and the lack of fast algorithms. Here, we propose a space mapping-based approximation of the stochastic control problem by solutions of the deterministic one. In combination with the receding horizon control technique this yields a reliable and fast numerical scheme for the closed loop control of stochastic interacting particle systems. As a numerical example we consider the herding of sheep with dogs. The numerical results underline the feasibility of our approach and further show stabilizing behaviour of the closed loop control. MSC: 60H10; 34F05; 49J15

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Multi-fidelity robust aerodynamic design optimization under mixed uncertainty

Cited by 62 publications

References 21 publications

Extending Horsetail Matching for Optimization Under Probabilistic, Interval, and Mixed Uncertainties

Extending Horsetail Matching for Optimization Under Probabilistic, Interval, and Mixed Uncertainties

Horsetail Matching for Optimization Under Probabilistic, Interval and Mixed Uncertainties

Space mapping-based receding horizon control for stochastic interacting particle systems: dogs herding sheep

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