Efficient Robust Design with Stochastic Expansions

Author

Yi Zhang
Serhat Hosder, Missouri University of Science and TechnologyFollow

Editor(s)

Koziel, Slawomir and Leifsson, Leifur

Abstract

This chapter describes the application of a computationally efficient uncertainty quantification approach, non-intrusive polynomial chaos (NIPC)-based stochastic expansions, for robust design under mixed (aleatory and epistemic) uncertainties and demonstrates this technique on robust design of a beam and on robust aerodynamic optimization. The approach utilizes stochastic response surfaces obtained with NIPC methods to approximate the objective function and the constraints in the optimization formulation. The objective function includes the stochastic measures, which are minimized simultaneously to ensure the robustness of the final design to both aleatory and epistemic uncertainties. The results of the optimization case studies show the computational efficiency and accuracy of the robust design with stochastic expansions, which may be applied to any stochastic optimization problem in science and engineering.

Recommended Citation

Y. Zhang and S. Hosder, "Efficient Robust Design with Stochastic Expansions," Surrogate-Based Modeling and Optimization, Springer Verlag, Jan 2013.

The definitive version is available at https://doi.org/10.1007/978-1-4614-7551-4_11

Department(s)

Mechanical and Aerospace Engineering

Keywords and Phrases

Aerodynamics; Optimization; Uncertainty Quantification; Robust Design; Stochastic Expansions; Computational Fluid Dynamics

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2013 Springer Verlag, All rights reserved.

Publication Date

01 Jan 2013