Multi-objective optimization of transonic airfoils using variable-fidelity models, co-kriging surrogates, and design space reduction

dc.contributor.advisor Leifur Leifsson
dc.contributor.author Amrit, Anand
dc.contributor.department Aerospace Engineering
dc.date 2018-08-11T12:06:10.000
dc.date.accessioned 2020-06-30T03:01:47Z
dc.date.available 2020-06-30T03:01:47Z
dc.date.copyright Fri Jan 01 00:00:00 UTC 2016
dc.date.embargo 2001-01-01
dc.date.issued 2016-01-01
dc.description.abstract <p>Computationally efficient constrained multi-objective design optimization of transonic airfoils is considered. The proposed methodology focuses on fixed-lift design aimed at finding the best possible trade-offs between the conflicting objectives. The algorithm exploits the surrogate-based optimization principle, variable-fidelity computational fluid dynamics (CFD) models, as well as auxiliary approximation surrogates (here, using kriging). The kriging models constructed within a reduced design space. The optimization process has three major stages: (i) design space reduction which involves the identification of the extreme points of the Pareto front through single-objective optimization, (ii) construction of the kriging model and an initial Pareto front generation using multi-objective evolutionary algorithm, and (iii) Pareto front refinement using co-kriging models. For the sake of computational efficiency, stages (i) and (ii) are realized at the level of low-fidelity CFD models. The proposed algorithm is applied to the multi-objective optimization of a transonic airfoil at a Mach number of 0.734 and a fixed lift coefficient of 0.824. The shape is parameterized with eight B-spline control points. The fluid flow is taken to be inviscid. The high-fidelity model solves the compressible Euler equations. The low-fidelity model is the same as the high-fidelity one, but with a coarser description and is much faster to execute. With the proposed approach, the entire Pareto front of the drag coefficient and the pitching moment coefficient is obtained using 100 low-fidelity samples and 3 high-fidelity model samples. This cost is not only considerably lower (up to two orders of magnitude) than the cost of direct high-fidelity mode optimization using metaheuristics without design space reduction, but, more importantly, renders multi-objective optimization of transonic airfoil shapes computationally tractable, even at the level of accurate CFD models.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/etd/15148/
dc.identifier.articleid 6155
dc.identifier.contextkey 8928994
dc.identifier.doi https://doi.org/10.31274/etd-180810-4751
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/15148
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/29332
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/etd/15148/Amrit_iastate_0097M_15731.pdf|||Fri Jan 14 20:36:32 UTC 2022
dc.subject.disciplines Aerospace Engineering
dc.subject.keywords Aerospace Engineering
dc.subject.keywords design space reduction
dc.subject.keywords optimization
dc.subject.keywords space mapping
dc.subject.keywords transonic
dc.title Multi-objective optimization of transonic airfoils using variable-fidelity models, co-kriging surrogates, and design space reduction
dc.type article
dc.type.genre thesis
dspace.entity.type Publication
relation.isOrgUnitOfPublication 047b23ca-7bd7-4194-b084-c4181d33d95d
thesis.degree.discipline Aerospace Engineering
thesis.degree.level thesis
thesis.degree.name Master of Science
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