Search Results

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.

Reductions of hardware costs as well as implementations of new innovative functions are the main drivers of today's automotive electronics. Indeed more and more resources are spent on adapting existing solutions to different environments. At the same time, due to the increasing number of networked components, a level of complexity has been reached which is difficult to handle using traditional development processes. The automotive industry addresses this problem through a paradigm shift from a hardware-, component-driven to a requirement- and function-driven development process, and a stringent standardization of infrastructure elements. One central standardization initiative is the AUTomotive Open System ARchitecture (AUTOSAR). AUTOSAR was founded in 2003 by major OEMs and Tier1 suppliers and now includes a large number of automotive, electronics, semiconductor, hard- and software companies.