Shape sensitivity analysis in flow models using a finite-difference approach

Download
  1. (PDF, 1 MB)
  2. Get@NRC: Shape sensitivity analysis in flow models using a finite-difference approach (Opens in a new window)
DOIResolve DOI: http://doi.org/10.1155/2010/209780
AuthorSearch for: ; Search for: ; Search for:
TypeArticle
Journal titleMathematical Problems in Engineering
Volume2010
Pages122; # of pages: 22
AbstractReduced-order models have a number of practical engineering applications for unsteady flows that require either low-dimensional approximations for analysis and control or repeated simulation over a range of parameter values. The standard method for building reduced-order models uses the proper orthogonal decomposition (POD) and Galerkin projection. However, this standard method may be inaccurate when used “off-design” (at parameter values not used to generate the POD). This phenomena is exaggerated when parameter values describe the shape of the flow domain since slight changes in shape can have a significant influence on the flow field. In this paper, we investigate the use of POD sensitivity vectors to improve the accuracy and dynamical system properties of the reduced-order models to problems with shape parameters. To carry out this study, we consider flows past an elliptic cylinder with varying thickness ratios. Shape sensitivities (derivatives of flow variables with respect to thickness ratio) computed by finite-difference approximations are used to compute the POD sensitivity vectors. Numerical studies test the accuracy of the new bases to represent flow solutions over a range of parameter values.
Publication date
LanguageEnglish
AffiliationNational Research Council Canada (NRC-CNRC); NRC Industrial Materials Institute
Peer reviewedYes
NRC number52490
NPARC number15236579
Export citationExport as RIS
Report a correctionReport a correction
Record identifier08c5e174-f722-4b13-9909-5b340b012cb5
Record created2010-05-10
Record modified2016-05-09
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)