Surrogate Models for Aerodynamic Shape Optimisation

Selvakumar Ulaganathan () and Nikolaos Asproulis ()
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Selvakumar Ulaganathan: Ghent University, iMINDS, INTEC
Nikolaos Asproulis: Cranfield University

A chapter in Surrogate-Based Modeling and Optimization, 2013, pp 285-312 from Springer

Abstract: Abstract The main challenges in full-scale aerospace systems development are related to the level of our understanding with respect to the systems behaviour. Computational modelling, through high-fidelity simulations, provides a viable approach towards efficient implementation of the design specifications and enhancing our understanding of the system’s response. Although high-fidelity modelling provides valuable information the associated computational cost restricts its applicability to full-scaled systems. This chapter presents a Computational Fluid Dynamics optimisation strategy based on surrogate modelling for obtaining high-fidelity predictions of aerodynamic forces and aerodynamic efficiency. An Aerodynamic Shape Optimisation problem is formulated and solved using Genetic Algorithm with surrogate models in the place of actual computational fluid dynamics algorithms. Ordinary Kriging approach and Hammersley Sequence Sampling plan are used to construct the surrogate models.

Keywords: Surrogate models; Surrogate-based optimisation; Aerodynamic shape optimisation; Supervised machine learning (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4614-7551-4_12

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DOI: 10.1007/978-1-4614-7551-4_12

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