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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.

This paper discusses the requirement for CAE methods to properly take into account the variabilities and uncertainties that characterizes design input properties without leading to oversized structures. Optimizing the structural behaviour while taking into account expected variability and uncertainty in the structure and its model, requires the adoption of a reliability-based design optimization approach. This paper starts with an overview of the problem of simulation uncertainty. The key focus is then on the description of the most commonly used methods and enabling tools for reliability analysis and reliability based design optimization. The theory is illustrated by real automotive design problems.

Since its first publication in the beginning of the eighties, Transfer Path Analysis (TPA) has evolved into a widely used tool for noise and vibration troubleshooting and internal load estimation, and this for single source as well as multivariate problems. One of the main bottlenecks preventing its even more widespread use in the actual vehicle development process is the test time to build the full data model, requiring not only in-operation tests but also extensive Frequency Response Function tests. As a consequence, several new approaches have appeared over the past years attempting to circumvent this limitation, such as Fast and Multilevel TPA and Operational TPA. The latter method attracts quite some attention as it only requires operational data measured at the path references and target locations.

The subjective quality of sounds is a topic of increasing importance in the automotive industry. The first consideration is to describe the perceptual characteristics of this quality by means of jury tests or appropriate metrics. Once a NVH problem is determined in terms of an appropriate Sound Quality description, an in-depth analysis of the underlying physical phenomena must be made and engineering solutions newel to be proposed and validated This involves: • the detailed analysis of the signal structure in the time, frequency and order domain and identifying the signal Components Critical to the relevant sound quality dimension • the Correlation of the critical signal components to specific sources noise or vibration transmission paths and vibro-acoustic system characteristics. Ultimately this should lead to the prediction of the effect of feasible modifications in sound quality terms through the use of engineering models.