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While CAE methods allow improving nominal product design using virtual prototypes, uncertainty and variability in properties and manufacturing processes lead to scatter in actual performances. Uncertainty must hence be incorporated in the CAE process to guarantee the robustness and reliability of the design. This paper presents an overview of uncertainty-based design in automotive and aerospace engineering. Fuzzy methods take uncertainty into account, whereas reliability analysis and a reliability-based design optimization framework can deal with variability. Key enabling technologies to alleviate the computational burden, such as workflow automation, substructuring and design of experiments, are discussed, and industrial applications are presented.

One of the scientific fields where, for already more than 20 years, system identification plays a crucial role is this of structural dynamics and vibro-acoustic system optimization. The experimental approach is based on the “Modal Analysis” concept. The present paper reviews the test procedure and system identification principles of this approach. The main focus though is on the real problems with which engineers, performing modal analysis on complex structures on a daily basis, are currently confronted. The added value of several new testing approaches (laser methods, smart transducers…) and identification algorithms (spatial domain, subspace, maximum likelihood,..) for solving these problems is shown. The discussed elements are illustrated with a number of industrial case studies.