This paper discusses the approach for architecture design space optimization of aeronautical systems investigated in the DEFAINE project. In system architecture optimization problems, hierarchical relations between design variables may exist. This means that the quantity and type of some variables are dependent on the value of other variables. To address this challenge, a nested optimization strategy is proposed, where an outer loop deals with the independent design variables and an inner loop with the dependent ones. As the characteristics of the dependent variables may become known only during workflow execution, a dynamic workflow formulation approach is developed to introduce these variables into the MDAO workflow, automatically, during execution. The proposed methodology is implemented using a suite of technologies provided by partners of the DEFAINE consortium to enable formulation and execution of collaborative MDAO problems. This implementation is then applied to the design and optimization of the structural layout of a UAV aileron. The goal is to minimize the aileron mass, subjected to failure criteria constraints. In this use case, hierarchical relations between variables are present. The aileron skin is discretised into material zones, each one defined by a (dependent) design variable. The number of zones, hence the number of design variables, is not known a priori as it depends on the number and position of ribs and spars(both independent variables). A preliminary implementation of the proposed approach proved effective in dealing with the hierarchical mixed-integer design space. Trade off studies comparing different aileron architectures could be efficiently set up and executed. Some limitations in the implemented workflow management technology, however prevent performing a full nested architecture optimization. Therefore, to demonstrate the effectiveness of the proposed nested approach, a parallel study was conducted on the same aileron, using a less generic and manual implementation of the nested MDAO workflow. This second study successfully minimized the mass and cost of the aileron structure, thereby proving the merit of the proposed nested approach to address system architecture optimization. @en

Most MDO problems currently do not include manufacturing as an optimization domain. Within the H2020 project AGILE 4.0 the intent is to bring manufacturing into the MDO domain using MBSE techniques developed within the project. To demonstrate how manufacturing can be brought into the MDO domain application cases are set up that resemble MDO problems from industry. In this paper, the MDO techniques will be used for the design of a Flap for a regional jet. The manufacturing aspect is represented by including the manufacturing cost of the flap in a Design Of Experiments (DOE). In this DOE different flap kinematic mechanisms and different flap sizes and paths are explored. The DOE is set up using the MDO toolset developed within AGILE 4.0. It allows for an automatic definition of the DOE workflow. The DOE results show that the choice of flap configuration has a significant effect on the Flap manufacturing cost, the flap wright and the landing performance of the aircraft. Next steps will be to investigate more flap configurations, improved the manufacturing cost model used and to set up a true flap optimization.

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