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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.

Traditionally, vibration analysis in operating conditions (on the road or on a bench) had to be combined with experimental modal analysis in controlled laboratory conditions in order to understand the modal behaviour of the structure. This requires additional measurements, costs and time. However, in many applications, the real operating conditions may differ significantly from those applied during the modal test and hence the vibration modes from the modal test might not be representative for the active modes in operation conditions. The need for a capability of doing a modal analysis on data from operating conditions is obvious. Over the last years, several modal parameter estimation techniques have been proposed and studied for modal parameter extraction from output-only data. Each method needs to make a number of assumptions and has some limitations.

Experimental identification of structural dynamics models is usually based on the modal analysis approach. In the classical modal parameter estimation approach, the baseline data which are processed are Frequency Response Functions measured in laboratory conditions. However, in many applications, the real operating conditions may differ significantly from those applied during the modal test. Hence, the need arises to identify a modal model in operational conditions. This issue is even more complicated by the fact that in most cases, only response data are measurable while the actual loading conditions are unknown. Therefore, the system identification process will need to base itself on output-only data.

This paper describes the application of Statistical Energy Analysis (SEA) and Experimental SEA (ESEA) to calculating the transmission of air-borne and structure-borne noise in a mid-sized sedan. SEA can be applied rapidly in the early stages of vehicle design where the degree of geometric detail is relatively low. It is well suited to the analysis of multiple paths of vibrational energy flow from multiple sources into the passenger compartment at mid to high frequencies. However, the application of SEA is made difficult by the geometry of the vehicle's subsystems and joints. Experience with current unibody vehicles leads to distinct modeling strategies for the various frequency ranges in which airborne or structure-borne noise predominates. The theory and application of ESEA to structure-borne noise is discussed. ESEA yields loss factors and input powers which are combined with an analytical SEA model to yield a single hybrid model.

The data measured during an ISO 362 pass-by-noise test are strongly non-stationary due to the fast acceleration of the vehicle and its moving position with respect to the ISO microphone position. Nevertheless, one would like to obtain an understanding of the relative contribution of the various noise generating components during the test. Since the classical signal analysis procedures based on the FFT calculation and auto/crosspower averaging for coherence/correlation analysis are no longer applicable, as they implicitly assume signal (and process) stationarity, an approach based on Autoregressive Vector (ARV) modelling of a set of measurement signals was developed and applied. An ARV model is calculated directly from a set of time data of limited duration.