In the last few years, the growing need of highly reliable and time-effective strategies to perform preliminary design of complex systems has led industries to adopt the Model Based System Engineering (MBSE) approach. In MBSE, systems are split into multiple sub-systems and the relevant physical phenomena are described via analytical or numerical models. When a significant number of design variables are to be considered, a smart approach to reduce the number of analyses to perform would be to make use of the Global Sensitivity Analysis (GSA) to higlight those variables that have a more significant influence on the system output. Moreover, an even more significant reduction of computational cost to perform the GSA can be achieved if the complex system modelled via the MBSE approach is exported under the Functional Mock-Up Interface (FMI) norm. In this context, this paper proposes an original approach to address the study of two constructive solutions of an acceleration measuring device typically used on airbags for which the use of a new solution characterized by a porous material is compared with a classical one.

As a measure to manage the climate impact of aviation, significant enhancements to aviation technologies and operations are necessary. When assessing these enhancements and their respective impacts on the climate, it is important that we also quantify the associated uncertainties. This is important to support an effective decision and policymaking process. However, such quantification of uncertainty is challenging, especially in a complex system that comprises multiple interacting components. The uncertainty quantification task can quickly become computationally intractable and cumbersome for one individual or group to manage. Recognizing the challenge of quantifying uncertainty in multicomponent systems, we utilize a divide-and-conquer approach, inspired by the decomposition-based approaches used in multidisciplinary analysis and optimization. Specifically, we perform uncertainty analysis and global sensitivity analysis of our multicomponent aviation system in a decomposition-based manner. In this work, we demonstrate how to handle a high-dimensional multicomponent interface using sensitivity-based dimension reduction and a novel importance sampling method. Our results demonstrate that the decomposition-based uncertainty quantification approach can effectively quantify the uncertainty of a feed-forward multicomponent system for which the component models are housed in different locations and owned by different groups.