An Efficient Aerodynamic Shape Optimization Framework for Robust Design of Airfoils Using Surrogate Models

Maruyama, Daïgo; Liu, Dishi; Görtz, Stefan

doi:10.7712/100016.2450.8838

Cited by 14 publications

(17 citation statements)

References 12 publications

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“…At this condition a strong near-normal shock wave is present over the upper surface and the addition of a shock control bump is expected to considerably reduce drag by weakening the shock intensity. Previous studies have been done for the aerodynamic shape optimization of airfoils under uncertainty [15,16]. However, no focus was given in obtaining a robust global solution that reduces both the number of optimization iterations and function evaluations in the uncertainty quantification stage.…”

Section: Problem Definitionmentioning

confidence: 99%

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An Efficient Bi-Level Surrogate Approach for Optimizing Shock Control Bumps under Uncertainty

Sabater

Goertz

2019

AIAA Scitech 2019 Forum

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Section: Problem Definitionmentioning

confidence: 99%

“…Schillings [12] and Maruyama [15] have shown that for UQ, the accuracy of the surrogate can be improved if the initial sampling follows the distribution of the input uncertainties ξ. In the design of experiments, more samples should be placed along the mean, than along the tails of the input PDFs.…”

Section: Design Of Experimentsmentioning

confidence: 99%

An Efficient Bi-Level Surrogate Approach for Optimizing Shock Control Bumps under Uncertainty

Sabater

Goertz

2019

AIAA Scitech 2019 Forum

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“…The use of Robust Optimization in aerodynamic shape optimization is increasing in popularity in order to come up with designs less sensitive against operational and geometrical uncertainties [1,2,3,4] . In opposition to deterministic optimization, where the Quantity of Interest, QoI, is a single value to be optimized, in robust optimization the QoI is a random variable.…”

Section: Introductionmentioning

confidence: 99%

“…On the one hand, the complexity of the optimization increases exponentially with the number of design parameters [5]. On the other hand, at each iteration of the optimization, a complete propagation of the uncertainty is required in order to come up with an accurate estimation of the 2 Christian Sabater and Stefan Görtz statistic to be minimized [3]. A possible solution to the first problem is the use of adjoint methods [6].…”

Section: Introductionmentioning

confidence: 99%

Gradient-Based Aerodynamic Robust Optimization Using the Adjoint Method and Gaussian Processes

Sabater¹,

Görtz²

2020

Computational Methods in Applied Sciences

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The use of robust design in aerodynamic shape optimization is increasing in popularity in order to come up with configurations less sensitive to operational conditions. However, the addition of uncertainties increases the computational cost as both design and stochastic spaces must be explored. The objective of this work is the development of an efficient framework for gradient-based robust design by using an adjoint formulation and a non-intrusive surrogate-based uncertainty quantification method. At each optimization iteration, the statistic of both the quantity of interest and its gradients are efficiently obtained through Gaussian Processes models. The framework is applied to the aerodynamic shape optimization of a 2D airfoil. With the presented approach it is possible to reduce both the mean and standard deviation of the drag compared to the deterministic optimum configuration. The robust solution is obtained at a reduced run time that is independent of the number of design parameters. This is a preprint of the book Chapter: Sabater, C. and Görtz, S. "Gradient-Based Aerodynamic Robust Optimization using the Adjoint Method and Gaussian Processes". In: Gaspar-Cunha A. et. al. Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences. Springer.

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“…For multi-disciplinary optimisation see Refs 20-22. For design, tools based on surrogate models (23) , and tools for robust design (design under uncertainty) (24) are also considered.…”

Section: Introductionmentioning

confidence: 99%

DLR transonic inverse design code, extensions and modifications to increase versatility and robustness

Streit¹,

Hoffrogge²

2017

Aeronaut. j.

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The DLR inverse design code computes the wing geometry for a prescribed target pressure distribution. It is based on the numerical solution of the integral inverse transonic small perturbation (TSP) equations. In this work, several extensions and modifications of the inverse design code are described. Results are validated with corresponding redesign test cases. The first modification concerns applications for high transonic Mach numbers or cases with strong shocks. The introduced modifications enable converged design solutions for cases where the original method failed. The second modification is the extension of the code to general non-planar wings. Previously, the design code was restricted to non-planar wing designs with small dihedral or to nacelle design. A third modification concerns aerofoil/wings designed for wind-tunnel design. In order to design a swept wing between two wind-tunnel walls, the solution method was extended to two symmetry planes. The introduced extensions and modifications have increased the robustness and range of applicability of the inverse design code.

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An Efficient Aerodynamic Shape Optimization Framework for Robust Design of Airfoils Using Surrogate Models

Abstract: Abstract. This paper deals with developing an efficient Robust Design Optimization (RDO) framework. The goal is to obtain an aerodynamic

Cited by 14 publications

References 12 publications

An Efficient Bi-Level Surrogate Approach for Optimizing Shock Control Bumps under Uncertainty

An Efficient Bi-Level Surrogate Approach for Optimizing Shock Control Bumps under Uncertainty

Gradient-Based Aerodynamic Robust Optimization Using the Adjoint Method and Gaussian Processes

DLR transonic inverse design code, extensions and modifications to increase versatility and robustness

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