How can powerful emergent behaviours, that have been found in advanced airborne self- separation, be maintained while moving the 4D planning and tactical functionalities back to the ground, as is the case with SESAR?
In order to understand and improve the emergent behaviours of SESAR at multiple time scales, the EMERGIA project will use innovative complexity science techniques. EMERGIA aims to dramatically reduce the risks that negative emergent behaviours have to be repaired late at huge operational costs, and will shorten the period needed to optimize the system architecture and design of SESAR.
The application of the innovative complexity science techniques to this advanced airborne-self separation has shown that its 4D planning and tactical layers work so well together that this leads to very powerful positive emergent behaviours, even beyond expectations of the concept developers. As a result of these powerful positive emergent behaviours, the advanced airborne self-separation concept considered can safely accommodate very high en route traffic demands.
In order the EMERGIA to address the research question above it will take three steps. The first step is to use the innovative complexity science techniques to identify the emergent behaviours of SESAR at multiple time scales. The second step is to compare these emergent behaviours to the powerful positive emergent behaviours of the advanced airborne self-separation ConOps, and to learn improving SESAR in case of significant difference in emergent behaviours. The third step is to evaluate the improved SESAR ConOps on its emergent behaviours, again by using the innovative complexity science techniques.
The expected results of the EMERGIA project are that any potentially negative emergent behaviours of SESAR are identified, and that this information is used to improve the SESAR system architecture and design such that their emergent behaviours become positive.