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     Research Journal of Applied Sciences, Engineering and Technology


An Improved Parallel Optimization Framework for Transonic Airfoil Design

Mohammad Kashif Zahir and Zhenghong GAO
School of Aeronautics Northwestern Polytechnical University, Xi
Research Journal of Applied Sciences, Engineering and Technology  2013  22:5209-5216
http://dx.doi.org/10.19026/rjaset.5.4266  |  © The Author(s) 2013
Received: September 12, 2012  |  Accepted: October 09, 2012  |  Published: May 25, 2013

Abstract

Variable Fidelity Optimization (VFO) is used to obtain a minimum drag transonic airfoil, at constant lift, subject to thickness and pitching moment constraints using several low fidelity solvers. VFO has emerged as an attractive method of performing, both, high-speed and high-fidelity optimization. VFO uses computationally inexpensive low-fidelity models, complemented by a surrogate to account for the difference between the high- and low-fidelity models, to obtain the optimum of the function efficiently and accurately. The authors’ original Variable Fidelity (VF) framework is modified for increased efficiency and accuracy by incorporating parallel evaluation and more constraints to the optimization problem. The method is found to be efficient and capable of finding the optimum that closely agrees with the results of high-fidelity optimization alone.

Keywords:

Airfoil optimization, Hicks-Henne, Kriging, parallel processing, surrogate models, variable-fidelity,


References


Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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