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Fast-running metamodels (surrogates or response surfaces) that approximate multivariate input/output relationships of time-consuming CAE simulations facilitate effective design trade-offs and optimizations in the vehicle development process. While the cross-validated nonparametric metamodeling methods are capable of capturing the highly nonlinear input/output relationships, it is crucial to ensure the adequacy of the metamodel error estimates. Moreover, in order to circumvent the so-called curse-of-dimensionality in constructing any nonlinear multivariate metamodels from a realistic number of expensive simulations, it is necessary to reliably eliminate insignificant inputs and consequently reduce the metamodel prediction error by focusing on major contributors. This paper presents a robust data-adaptive nonparametric metamodeling procedure that combines a convergent variable screening process with a robust 2-level error assessment strategy to achieve better metamodel accuracy.

For the assessment of vehicle acoustics in the early design stages of a vehicle program, the use of full vehicle SEA models is becoming the standard analysis method in the US automotive industry. One benefit is that OEM's and Tier 1 suppliers are able to cascade lower level acoustic performance targets for NVH systems and components. Detailed SEA system level models can be used to assess the performance of systems such as dash panels, floors and doors, however, the results will be questionable until test data Is available. Correlation can be accomplished with buck testing, which is a common practice in the automotive industry for assessing the STL (sound transmission loss) of vehicle level components. The opportunity to conduct buck testing can be limited by the availability of representative bodies to be cut into bucks and the availability of a transmission loss suite with a suitably large opening.