Multi-fidelity data fusion for the design of multidisciplinary systems under uncertainty

This thesis presents a multi-fidelity methodology to enable the incorporation of high-fidelity data into a conceptual design process. The methodology is based upon a fidelity weighted combination of Gaussian Process surrogate models that takes into account both the quality of the Gaussian Process approximation and the confidence of the designer in the disciplinary model being approximated. The methodology is demonstrated on the stability and control analysis of a Blended-Wing-Body aircraft's center of gravity limits. The results show that low-fidelity data is enhanced by the presence of high-fidelity data in key areas of the design space. At the same time, the presence of even sparse high-fidelity data is key to reducing the variance in the stability and control analysis, thereby improving the quality of the predictions of the center of gravity limits.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Aeronautics and Astronautics.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Feldstein, Alexander W

Advisor dc:contributor.advisor

• Karen E. Willcox.

• Aeronautics and Astronautics.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/119295

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/119295