Agility and Design Margins
Between 2000 and 2015, digital simulation technologies benefited from huge investments. Today, they must lead to even more speed, precise calculations and automated operation. Moreover, the system architect’s decisions must guarantee significantly sound system performances, despite uncertainties that may be due to many causes, such as a changing environment that is not always perfectly controlled, or lack of knowledge of the system, for instance. This is a major mathematical and algorithmic
To meet the challenge, SystemX and its partners have launched the AMC project in order to explore mathematical approaches, coupled with a systems engineering approach by using and adapting the agile method that has shown its worth in other fields such as software development.
The AMC project, Agility and Design Margins, launched in February 2017 for a period of four years, has two major objectives:
- Allow considering margins in simulation-based design processes: in order to make simulation helpful for decision making, it must quantify and integrate design margins. The AMC project will draw up a methodology for managing margins through uncertainty propagation analysis.
- Demonstrate the contribution of collaborative and multi-disciplinary engineering approaches, according to the principle of the agile method, for complex model simulation projects. The purpose is to promote collaboration between several actors: the systems architect, the simulation architect and domains experts, by using a system engineering approach.
The AMC project is based on two industrial usage cases:
- The orbital stage of a space vehicle: the considered engineering scenario is based on prototyping an orbital stage that allow a second mission. New functions will be developed based on existing architecture, and will be integrated using a simulation-based decision approach.
- The economical autonomous vehicle: the project plans to study the trade-off between respecting the automated driving decisions and energy savings. This will require the integration of architecture comparison methods and analyze the impact of adding new functions, in order to optimize the global performance of the vehicle.