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

Sabater Campomanes, Christian and Görtz, Stefan (2020) Gradient-Based Aerodynamic Robust Optimization Using the Adjoint Method and Gaussian Processes. In: Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences Computational Methods in Applied Sciences, 55. Springer, Cham. pp. 211-226. doi: 10.1007/978-3-030-57422-2_14. ISBN 978-3-030-57422-2.

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Official URL: https://link.springer.com/chapter/10.1007%2F978-3-030-57422-2_14

Abstract

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.

Item URL in elib:https://elib.dlr.de/138501/
Document Type:Book Section
Title:Gradient-Based Aerodynamic Robust Optimization Using the Adjoint Method and Gaussian Processes
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Sabater Campomanes, ChristianUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Görtz, StefanUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:24 November 2020
Journal or Publication Title:Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Volume:55
DOI:10.1007/978-3-030-57422-2_14
Page Range:pp. 211-226
Editors:
EditorsEmailEditor's ORCID iDORCID Put Code
António, Gaspar-CunhaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Jacques, PeriauxUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Kyriakos C, GiannakoglouUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Nicolas R., GaugerUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Domenico, QuagliarellaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
David, GreinerUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Publisher:Springer, Cham
Series Name:Computational Methods in Applied Sciences
ISBN:978-3-030-57422-2
Status:Published
Keywords:Robust design, Optimization under uncertainty, Adjoint method, Gaussian Processes, Computational fluid dynamics
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:other
DLR - Research area:Aeronautics
DLR - Program:L - no assignment
DLR - Research theme (Project):L - no assignment
Location: Braunschweig
Institutes and Institutions:Institute for Aerodynamics and Flow Technology > CASE, BS
Deposited By: Sabater Campomanes, Christian
Deposited On:07 Dec 2020 09:12
Last Modified:20 Jun 2021 15:54

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