Building complex systems from models that have been developed separately without modifying existing code is a challenging task faced on a regular basis in multiple contexts including design verification. To address this issue an approach has been developed for automating dynamic system model composition by defining the minimum set of information that is necessary to the composition process. In this paper a design and implementation of this approach for standard Modelica is presented in the context of an application case study -the verification of a new design for spoiler activation against requirements.

A procedure for the optimal design of an active suspension with skyhook control scheme is presented. A 15 Degrees of Freedom vehicle model has been implemented and, once correlated with test data, the control strategy has been plugged in and tuned in function of the vehicle manufacturer target requirements. Achievable performances of the vehicle equipped with active components have been estimated through the implementation of the "quarter car model", successively extended towards the full controlled vehicle. The active damper system, to be manufactured by Tenneco, has been separately modeled and the suspension design definition has been optimized with reference to performance achievements and power consumption requirements.Vehicle Dynamics, Control Design, Multibody systems, Multidisciplinary Design Optimization, Multiphysics Modeling.

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Abstract. The architecture design process requires to define several design alternatives and to compare them in order to choose the most relevant system architecture given a set of objectives. Nevertheless, designers are generally constraint to restrict their studies to a small set of alternatives due to time constraints and combinatorial aspects of the problem. The objective of our method is to assist them by automatically generating a larger number of design alternatives. The proposed algorithm will first generate alternatives by adding components to the architecture and allocate them to functions. The originality of our approach is that it takes into account two rules that ensure the viability (component-to-component consistency) and the validity (function-to-component consistency) of the generated architectures. Once a set of consistent alternatives are generated, we use them as an input of a multi-objective genetic algorithm to propose a set of Pareto-optimal alternatives.

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