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

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