Picture of aircraft jet engine

Strathclyde research that powers aerospace engineering...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by Strathclyde researchers involved in aerospace engineering and from the Advanced Space Concepts Laboratory - but also other internationally significant research from within the Department of Mechanical & Aerospace Engineering. Discover why Strathclyde is powering international aerospace research...

Strathprints also exposes world leading research from the Faculties of Science, Engineering, Humanities & Social Sciences, and from the Strathclyde Business School.

Discover more...

Investigating Bayesian robust experimental design with principles of global sensitivity analysis

He, Fei and Yue, Hong and Brown, Martin (2010) Investigating Bayesian robust experimental design with principles of global sensitivity analysis. In: 9th International Symposium on Dynamics and Control of Process Systems (DYCOPS 2010), 2010-07-05 - 2010-07-07.

Full text not available in this repository. (Request a copy from the Strathclyde author)


The purpose of model-based experimental design is to maximise the information gathered for quantitative model identification. Instead of the commonly used optimal experimental design, robust experimental design aims to address parametric uncertainties in the design process. In this paper, the Bayesian robust experimental design is investigated, where both a Monte Carlo sampling strategy and local sensitivity evaluation at each sampling point are employed to achieve the robust solution. The link between global sensitivity analysis (GSA) and the Bayesian robust experimental design is established. It is revealed that a lattice sampling based GSA strategy, the Morris method, can be explicitly interpreted as the Bayesian A-optimal design for the uniform hypercube type uncertainties.