Article: Variable-resolution shape optimisation: low-fidelity model selection and scalability Journal: International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO) 2015 Vol.6 No.1 pp.1 - 21 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. Inderscience Publishers - linking academia, business and industry through research

Title: Variable-resolution shape optimisation: low-fidelity model selection and scalability

Authors: Leifur Leifsson; Slawomir Koziel

Addresses: Department of Aerospace Engineering, Iowa State University, Ames, IA 50011, USA ' Engineering Optimization and Modeling Center, School of Science and Engineering, Reykjavik University, Menntavegur 1, 101 Reykjavik, Iceland

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.

Keywords: aerodynamic shape optimisation; variable resolution modelling; computational fluid dynamics; CFD; space mapping; shape-preserving response prediction; SPRP; scalability; model selection; simulation.

DOI: 10.1504/IJMMNO.2015.068902

International Journal of Mathematical Modelling and Numerical Optimisation, 2015 Vol.6 No.1, pp.1 - 21

Received: 12 Jun 2014
Accepted: 24 Jul 2014
Published online: 19 Apr 2015 *

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Title: Variable-resolution shape optimisation: low-fidelity model selection and scalability

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