Simulation of the Aviation Transport System (ATS) is an indispensable corner stone for research and planning in aviation. Sustainable and energy-efficient aviation requires a significantly advanced simulation of the ATS for four major reasons:
A significantly advanced ATS simulation is central to unlock the full potential of SE²A technologies and to successfully introduce and operate a SE²A ATS.
The project „SOAP - Simulation and Optimization of Air transport Processes“ is part of the Cluster of Excellence „SE²A – Sustainable and Energy-Efficient Aviation“. As an interdisciplinary research center of the Technische Universität Braunschweig, the German Aerospace Center (DLR), the Leibniz University Hannover (LUH) and the University of Art Braunschweig (HBK), it is the objective of SE²A the investigation of new technologies for a sustainable and energy efficient air transport system.
The four main objectives of the SOAP project are:
The following figure gives an overview on the SOAP interaction of all involved PIs and their respective work programs within the project.
The Air Traffic Simulation environment will be used to conduct sensitivity analysis for the determination and classification of parameters that will have a major impact on the most important KPIs of the air traffic system. These parameters could be from the flight performance of novel aircraft (like cruising speed or altitude, climb and descent performance, endurance, etc.), the flight characteristics (e.g. reduced bank angle capabilities due to 1G wing) or Turn-Around-Times (due to different refueling or boarding processes). The results of the KPIs will be analyzed to find unsteady states like reduction of flights per day and aircraft. The sensitivity analysis will help to identify the drawbacks of novel and innovative aircraft designs.
Using an optimization model to determine optimal fleet renewal schemes over time, taking into account and introducing different novel aircraft types, brings the advantage of a better picture of the economic and ecological consequences of their respective adoption rates. Resorting to AdAS to decide the final feasibility of fleet decisions brings a level of detail into the optimization that cannot be reached by pure optimization techniques and as such significantly enhances the informative power of the model. Building an automated coupling between the optimization and the simulation will enable us to test and compute the renewal strategies for multiple different sets of input parameters, e.g., vary over introduction years, and so helps in evaluating the different transition pathways. After the integrated optimization/simulation-runs, the respective cumulative economic and ecological key figures for the chosen fleet over time can be computed via the set variable values and compared for different test scenarios.
The Fleet Renewal Optimization should be used to show for different sets of novel aircraft types, vetted by AdAS, how the market adoption over time might look, to give a picture of potential success. To this end, an automated coupling between the Air Traffic Simulation environment and a general purpose Mixed-Integer Programming solver needs to be designed and implemented. The optimization solver then needs to be able to call upon AdAS to decide feasibility for pre-determined sets of renewal decision.
Since the necessary multiple calls to the Air Traffic Simulation environment in its full extent will be too time consuming, it is additionally necessary to develop a suitable scaled version of the simulation that allows to only include certain parts of the whole system, while maintaining key characteristics.
To complete the transport path, intermodality to and from the airport is added. To ensure a profound multi-scale understanding of the underlying structures, processes and their interrelations, apt simulations for all logistics flows will be designed. The latter include material, personnel, energy and most importantly information. Using a multi-tier supply chain with the airport representing the equivalent of an OEM, the collaboration network between the feeding nodes will be established. Likewise, the airport serves as single source for the reverse network.
To coordinate the different entities within the network, a scale down approach for available information will be used. Starting with a centralized computation approach, availability of information is reduced to a cooperative/non-cooperative distributed and a decentralized approach. The respective methods capture behaviour of economically or personally independent entities, which/who are reluctant to share their information. Additionally, as in other supply networks, the airport cannot be seen as the leading node but as part of a coordination problem.
To achieve the 4-hour-door-to-door goal while keeping other KPIs and processes in mind, scaled versions of the airport will be designed to properly address interfaces between airport and hinterland operations. In particular, an abstraction to the strategic level will be used to address structural properties of transport flows, a tactical version for flow planning, and an operational one for evaluation and validation.
