The Integration Framework for Architecture Development (IFAD) is an integrated framework that provides fast and consistent discipline analysis results and identifies discipline consequences corresponding to vehicle design changes. This information is valuable for balancing and integration in the early design phase. In this paper, the IFAD framework is utilized to conduct an example multi-objective multi-disciplinary optimization to evaluate vehicle performance trade-offs for a hypothetical vehicle. We consider design changes on high-level geometrical dimensions including front overhang, rear overhang and vehicle width at rocker. We also study vehicle configurations including choice of materials and tires and choice of powertrains. A commonly used multi-objective genetic algorithm (MOGA) technique, Non-dominated Sorting Genetic Algorithm (NSGAII [1]) is chosen because of the mixed types of design variables involved (i.e., continuous design variables representing high-level geometrical dimensions and discrete design variables representing vehicle configurations such as powertrain selection and material choice). Vehicle performance analyses in a range of disciplines such as geometry, aerodynamics and energy are carried out automatically through IFAD. The use of response surface modeling (RSM) is desired due to the large number of evaluations typical for a MOGA application. A comparison of the engineering performance trade-offs based on two different sets of performance objectives is presented.

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