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.
Nowadays, the design of complex systems such as a car or an airplane requires advanced computer tools and methods to optimize them (e.g., minimum cost) or improve their reliability. However, there are still major difficulties that need to be addressed to conceive the next generation of computer-based designs. For instance, minimizing the weight of a car for lower gaz consumption while ensuring that the car is crashworthy and reliable is still a major challenge. Intellectual Merit: The grant has enabled the development of new techniques that tackle some of the bottlenecks encountered in traditional approaches. For instance, the methods solve the difficulties related to complex behaviors (e.g., discontinuities) and high sensitivity to uncertainty. It also tackles issues related to computational times that stills plagues the design of complex systems. The approaches developped during this grant are based on machine learning techniques and offer a new level of flexibility in computational design. In fact, the techniques have been applied to a wide range of high-impact applications: airplane wing design, automotive design for crashworthiness, hip fracture prediction, and the reduction of vibrations. The outcomes of this projects are: The development of several novel approaches for design optimization and reliability assessement. The applications of these techniques to high-impact design problems that could not be solved otherwise. Finally, the codes for the methods developped during this grant will be made freely available online. Broader Impact: By providing new computational design techniques and associated benefits such as cost minimization, design cycle time reduction, and improved reliability, the outcome of this research 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).
