To read the full version of this content please select one of the options below:

Adaptive response prediction for aerodynamic shape optimization

Leifur Leifsson (Department of Aerospace Engineering, Iowa State University, Ames, Iowa, USA)
Slawomir Koziel (Gdansk University of Technology, Gdansk, Poland)

Engineering Computations

ISSN: 0264-4401

Article publication date: 3 July 2017

Abstract

Purpose

The purpose of this paper is to reduce the overall computational time of aerodynamic shape optimization that involves accurate high-fidelity simulation models.

Design/methodology/approach

The proposed approach is based on the surrogate-based optimization paradigm. In particular, multi-fidelity surrogate models are used in the optimization process in place of the computationally expensive high-fidelity model. The multi-fidelity surrogate is constructed using physics-based low-fidelity models and a proper correction. This work introduces a novel correction methodology – referred to as the adaptive response prediction (ARP). The ARP technique corrects the low-fidelity model response, represented by the airfoil pressure distribution, through suitable horizontal and vertical adjustments.

Findings

Numerical investigations show the feasibility of solving real-world problems involving optimization of transonic airfoil shapes and accurate computational fluid dynamics simulation models of such surfaces. The results show that the proposed approach outperforms traditional surrogate-based approaches.

Originality/value

The proposed aerodynamic design optimization algorithm is novel and holistic. In particular, the ARP correction technique is original. The algorithm is useful for fast design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search spaces, which is challenging using conventional methods because of excessive computational costs.

Keywords

Citation

Leifsson, L. and Koziel, S. (2017), "Adaptive response prediction for aerodynamic shape optimization", Engineering Computations, Vol. 34 No. 5, pp. 1485-1500. https://doi.org/10.1108/EC-02-2016-0070

Publisher

:

Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited