Efficient forward uncertainty propagation strategies in complex systems

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Thanks to recent advances in computing capabilities, Computer-Aided-Engineering (CAE) tools can be used today to support large-scale engineering design. CAE tools allow to rapidly explore the performance of a design by building virtual prototypes, rather than building multiple expensive physical prototypes, and to assess operating conditions not reproducible with experiments.However, the performance results yielded by these virtual prototypes might be significantly different from those obtained by testing the actual structure. This can be caused by uncertainties model parameters (such as geometry, material properties, and joints) arising from manufacturing variability or lack of knowledge. As a result, in recent years extensive research has been devoted to the development of analysis methods which can take into account uncertainties to develop robust designs which are relatively insensitive to parameter uncertainties. Nonetheless, developing efficient and accurate forward uncertainty propagation strategies through large scale models (which usually include a large number of uncertain parameters) is still one of the major challenges in this research area, especially when uncertain parameters are modelled using mixed probabilistic and non-probabilistic uncertainty descriptions.This overseas travel grant application seeks to fund a short visit (10 days, 5 July 2017 to 14 July 2017 - excluding travel time) to the Optimization and Uncertainty Quantification department of Sandia National Laboratories in Albuquerque (US). This small travel grant will enable close collaboration between Dr Cicirello and the Optimization and Uncertainty Quantification team to work intensively on the problem, and to facilitate and accelerate future collaborative projects.