Probabilistic collocation for simulation-based robust concept exploration

Abstract

In the early stages of an engineering design process it is necessary to explore the design space to find a feasible range that satisfies design requirements. When robustness of the system is among the requirements, the robust concept exploration method can be used. In this method, a global metamodel, such as a global response surface of the design space, is used to evaluate robustness. However, for large design spaces, this is computationally expensive and may be relatively inaccurate for some local regions. In this article, a method is developed for successively generating local response models at points of interest as the design space is explored. This approach is based on the probabilistic collocation method. Although the focus of this article is on the method, it is demonstrated using an artificial performance function and a linear cellular alloy heat exchanger. For these problems, this approach substantially reduces computation time while maintaining accuracy.

Keywords:

• Probabilistic collocation
• robust concept exploration
• robust design
• polynomial chaos expansion
• compromise decision support problem

Acknowledgements

Financial support from NSF grant DMI-060259 SACE: Statistics Aided Computer Experiments, Georgia Tech Savannah and The Center for Computational Materials Design are gratefully acknowledged. During his studies at Georgia Tech, Markus Rippel was also supported by the German Academic Exchange Service (DAAD) and the Federation of German-American Clubs (VDAC). The authors are grateful to Carolyn Conner Seepersad, Hae-Jin Choi and Marco Gero Fernández for developing the MATLAB code for the LCA analysis and Matthew Marston for providing a Java version of DSIDES. Furthermore, the authors gratefully acknowledge the help from Wei Chen and James R. Hockenberry.