The research and innovation AGILE project developed the next generation of aircraft Multidisciplinary Design and Optimization processes, which target significant reductions in aircraft development costs and time to market, leading to more cost-effective and greener aircraft solutions. The high level objective is the reduction of the lead time of 40% with respect to the current state-of-the-art. 19 industry, research and academia partners from Europe, Canada and Russia developed solutions to cope with the challenges of collaborative design and optimization of complex products. In order to accelerate the deployment of large-scale, collaborative multidisciplinary design and optimization (MDO), a novel methodology, the so-called AGILE Paradigm, has been developed. Furthermore, the AGILE project has developed and released a set of open technologies enabling the implementation of the AGILE Paradigm approach. The collection of all the technologies constitutes AGILE Framework, which has been deployed for the design and the optimization of multiple aircraft configurations. This paper focuses on the application of the AGILE Paradigm on seven novel aircraft configurations, proving the achievement of the project’s objectives. @en

High aspect ratio strut braced aircraft can significantly reduce the induced drag. The inherent anisotropic behaviour of the composite material along with their weight saving potential can improve the performance of the aircraft during the flight. Thus, a composite strut braced aircraft is one of the promising candidates to achieve the targets set by European commission in Flightpath 2050 report. In this paper, multidisciplinary design analysis and optimization framework for strut braced aircrafts, is set-up involving tools provided by AGILE partners distributed worldwide. In the workflow, composite aeroelastic analysis and tailoring capability has been integrated with use of surrogate modelling. A design of experiment of the workflow with wing planform parameters as design variables is performed and a surrogate model is build. The optimization with an objective to reduce the fuel mass is performed using the surrogate of the workflow. @en