Aerodynamic shape design using evolutionary algorithms and new gradient-assisted metamodels

Giannakoglou, KC; Papadimitriou, DI; Kampolis, IC

dc.contributor.author	Giannakoglou, KC	en
dc.contributor.author	Papadimitriou, DI	en
dc.contributor.author	Kampolis, IC	en
dc.date.accessioned	2014-03-01T01:23:33Z
dc.date.available	2014-03-01T01:23:33Z
dc.date.issued	2006	en
dc.identifier.issn	0045-7825	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17014
dc.subject	Adjoint formulation	en
dc.subject	Aerodynamics	en
dc.subject	Artificial neural networks	en
dc.subject	Evolutionary algorithms	en
dc.subject	Shape optimization	en
dc.subject	Turbomachinery	en
dc.subject.classification	Engineering, Multidisciplinary	en
dc.subject.classification	Mathematics, Interdisciplinary Applications	en
dc.subject.classification	Mechanics	en
dc.subject.other	Compressible flow	en
dc.subject.other	Evolutionary algorithms	en
dc.subject.other	Multilayer neural networks	en
dc.subject.other	Optimization	en
dc.subject.other	Radial basis function networks	en
dc.subject.other	Turbomachinery	en
dc.subject.other	Adjoint formulation	en
dc.subject.other	Metamodels	en
dc.subject.other	Shape design	en
dc.subject.other	Shape optimization	en
dc.subject.other	Aerodynamics	en
dc.subject.other	Aerodynamics	en
dc.subject.other	Compressible flow	en
dc.subject.other	Evolutionary algorithms	en
dc.subject.other	Multilayer neural networks	en
dc.subject.other	Optimization	en
dc.subject.other	Radial basis function networks	en
dc.subject.other	Turbomachinery	en
dc.title	Aerodynamic shape design using evolutionary algorithms and new gradient-assisted metamodels	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.cma.2005.12.008	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.cma.2005.12.008	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	Aerodynamic shape design and optimization problems based on evolutionary algorithms and surrogate evaluation tools, i.e., the so-called metamodels, have recently found widespread use. Using metamodels, trained either separately from or during the optimization loop, a considerable reduction in the overall computing cost can be achieved. To support metamodel-based evolutionary algorithms, a class of new metamodels which utilize both known responses and response gradients for their training is proposed. The new gradient-assisted metamodels are extensions of standard multi-layer perceptrons and radial basis function networks. To demonstrate the prediction capabilities of the proposed metamodels and investigate different implementation modes within search algorithms along with the relevant CPU cost, a number of 2D and 3D aerodynamic shape (namely airfoils and turbomachinery blades) design problems are analyzed. Single- and two-objective problems, aiming at designing shapes that reproduce known pressure distributions at specific operating points, are considered. The exact evaluation tool is a numerical solver of the compressible fluid flow equations. The necessary gradient of the objective function is obtained by formulating and numerically solving adjoint equations. (c) 2006 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE SA	en
heal.journalName	Computer Methods in Applied Mechanics and Engineering	en
dc.identifier.doi	10.1016/j.cma.2005.12.008	en
dc.identifier.isi	ISI:000239680100027	en
dc.identifier.volume	195	en
dc.identifier.issue	44-47	en
dc.identifier.spage	6312	en
dc.identifier.epage	6329	en