Variable-resolution shape optimisation: low-fidelity model selection and scalability
by Leifur Leifsson; Slawomir Koziel
International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO), Vol. 6, No. 1, 2015

Abstract: Computationally-efficient aerodynamic shape optimisation can be realised using surrogate-based methods. By shifting the optimisation burden to a cheap and yet reasonably accurate surrogate model, the design cost can be substantially reduced, particularly if the surrogate exploits an underlying physics-based low-fidelity model (e.g., the one obtained by coarse-discretisation computational fluid dynamics (CFD) simulation). The knowledge about the physical system of interest contained in the low-fidelity model allows us to construct an accurate representation of the original, high-fidelity CFD model, using a small amount of high-fidelity data and dramatically reduce the overall design cost. Two fundamental issues in such a process are a proper selection of the quality of the low-fidelity model (e.g., the model 'mesh coarseness' that may affect both the optimisation cost and the reliability of the design process), as well as the scaling properties of the surrogate-based design process with respect to the dimensionality of the design space. Our investigations are carried out for specific variable-resolution optimisation methodologies exploiting two types of correction methods: shape-preserving response prediction and space mapping.

Online publication date: Sun, 19-Apr-2015

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email