Advances in Explicit Design Space Decomposition for Computational Design

University Of Arizona, Tucson AZ

Investigators

Abstract

The research objective of this award is to develop a new paradigm for the computational design of complex systems. This research will aid engineers to find optimal design solutions that will minimize performance objectives while satisfying requirements under the presence of uncertainties. This is made possible by defining explicitly the boundaries of the regions of the design space corresponding to specific system behaviors. This Explicit Design Space Decomposition (EDSD), which is achieved using support vector machines, has been shown to be promising and particularly useful for problems that are discontinuous or binary and involve costly computer simulations. The research extends the EDSD approach following three main avenues. First, a multifidelity scheme will be developed that will enable the designers to exploit the wealth of information coming from various sources (analytical models, engineer experience, simulations, and physical experiments). Through adaptive sampling, the multifidelity approach can lead to a drastic reduction in the number of calls to expensive computer simulations. Second, the new approach will quantify the inaccuracy of the explicit boundaries and incorporate this information in the assessment of probabilities of failure. Finally, this work proposes to unify the EDSD approach with existing approaches based on response approximations such as Kriging. If successful, these methods will lead to a more flexible computational design framework, particularly for complex systems. In fact, EDSD and the proposed advances are able to handle problems with non-smooth behaviors, reduce computational time, propagate uncertainties, and combine vastly different sources of information. The associated benefits such as cost minimization, design cycle time reduction, and improved reliability, will provide a competitive advantage to companies. The techniques are applicable to many engineering disciplines and are particularly useful in the biomedical field where both clinical data and computational models are used (e.g., for hip fracture prediction). The methods will be disseminated to the engineering and research community through their implementation in the free DAKOTA software package from Sandia National Laboratory.