The Application of Multi-Attribute Optimization as a Systems Engineering Tool in an Automotive CAE Environment
Multi-Attribute Optimization (MAO) is proposed as a tool for delivering high value products within the systems engineering approach taken in the automotive industry. This work focuses on MAO methods that use Computer Aided Engineering (CAE) analyses to build a metamodel of system
A review of the literature and current Jaguar Land Rover optimization methods showed that the number of samples required to build a metamodel could be estimated using the number of input variables. The application of these estimation methods to a concept airbox design showed that this guidance may not be sufﬁcient to fully capture the complexity of system behaviour in the metamodelling method. The use of the number of input variables and their ranges are proposed as a new approach to the scaling of sample sizes. As a corollary to the issue of the sample size required for accurate metamodelling, the sample required to estimate the error was also examined. This found that the estimation of the global error by additional samples may be impractical in the industrial context.
CAE is an important input to the MAO process and must balance the efﬁciency and accuracy of the model to be suitable for application in the optimization process. Accurate prediction of automotive attributes may require the use of new CAE techniques such as multi-physics methods. For this, the ﬂuid structure interaction assessment of the durability of internal components in the fuel tank due to slosh was examined. However, application of the StarCD-Abaqus Direct couple and Abaqus Combined Eularian Lagrangian was unsuitable for this fuel slosh application. Further work would be required to assess the suitability of other multi-physics methods in an MAO architecture. Application of the MAO method to an automotive airbox shows the potential for improving both product design and lead time.