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Gradient-based multifidelity optimisation for aircraft design using Bayesian model calibration

Published online by Cambridge University Press:  27 January 2016

A. March ,
K. Willcox  and
Q. Wang
A. March*
Affiliation:
Massachusetts Institute of Technology, Massachusetts, USA
*
amarch@mit.edu
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Abstract

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Optimisation of complex systems frequently requires evaluating a computationally expensive high-fidelity function to estimate a system metric of interest. Although design sensitivities may be available through either direct or adjoint methods, the use of formal optimisation methods may remain too costly. Incorporating low-fidelity performance estimates can substantially reduce the cost of the high-fidelity optimisation. In this paper we present a provably convergent multifidelity optimisation method that uses Cokriging Bayesian model calibration and first-order consistent trust regions. The technique is compared with a single-fidelity sequential quadratic programming method and a conventional first-order trust-region method on both a two-dimensional structural optimisation and an aerofoil design problem. In both problems adjoint formulations are used to provide inexpensive sensitivity information.

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 115 , Issue 1174 , December 2011 , pp. 729 - 738
Copyright
Copyright © Royal Aeronautical Society 2011 

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