Epistemic model uncertainty is a significant source of uncertainty that affects a multidisciplinary system. In order to achieve a reliable design, it is critical to ensure that the disciplinary/subsystem simulation models are trustworthy, so that the aggregated uncertainty of system quantities of interest (QOIs) is acceptable. Uncertainty reduction can be achieved by gathering additional experiments and simulations data; however resource allocation for multidisciplinary design optimization (MDO) remains a challenging task due to the complex structure of a multidisciplinary system. In this paper, we develop a novel approach by integrating multidisciplinary uncertainty analysis (MUA) and multidisciplinary statistical sensitivity analysis (MSSA) to answer the questions about where (sampling locations), what (disciplinary responses), and which (simulations versus experiments) for allocating more resources. To manage the complexity in making the above decisions, a sequential procedure is proposed. First, the input space of a multidiscipline system is explored to identify the locations with unacceptable amounts of uncertainty with respect to the system QOIs. Next, these input locations are selected through a correlation check so that they are sparsely located in the input space, and their corresponding critical responses are identified based on MSSA. Finally, using a preposterior analysis, decisions are made about what type of resources (experimental or computational) should be allocated to the critical responses at the chosen input locations. The proposed method is applied to a benchmark electronic packaging problem to demonstrate how epistemic uncertainty is gradually reduced via gathering more data.

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