Data-driven trajectory prediction is one of the key pillars of the future ATM system. Recent research focuses on using novel data sources and machine learning algorithms to improve the performance of 4D trajectory prediction, enabling safer and more efficient routing of aircraft. In this paper a framework for data sourcing and preparation for such predictors is presented, as well as a comparison of three of the best performing prediction algorithms from literature to a baseline method. Currently such comparisons are lacking, making it hard to determine which techniques provide the best results. Using an ADS-B antenna and various online data sources a trajectory set of 40,000 trajectories is built. Two clustering methods are tested and it is found that clustering trajectories using Density Based Clus- tering for Applications with Noise (DBSCAN) performs poorly on our data set of arriving flights. Too many trajectories are classified as outliers while DBSCAN is not capable of separating the trajectories in distinct clusters. A clustering method based on the STARs of the airport is proposed, which performs better in terms of accuracy and efficiency. Finally, a baseline simulation using Aircraft Performance Models is compared to a deep neural network, a Long Short-Term Memory (LSTM) network and to Gradient Boosting Machines (GBM) for trajectory prediction. It is found that the latter outperforms the other methods overall, while it was expected that predictors based on LSTMs would provide more accurate results. It is concluded that long-term dependencies in trajectory data, on which LSTMs perform well, are less important than categorical indicators, on which GBMs perform better, in trajectory prediction.

The first launch of the Ariane 6 launch vehicle is planned for 2020, however in its current design no significant part of the launcher will be reusable. A current trend in the global space market is decreasing the costs of spacecraft launches through recovery, retrieval and refurbishment of parts of launchers. As a first step towards this market demand, it is to be investigated whether it is cost-effective to recover, refurbish and reuse the key components of the first stage of the Ariane 6, which are contained in the Vulcain Aft Bay (VuAB). This is where the engine, fuel lines, thrust frame and electronics are attached. The team has the task to develop a cost effective way of reusing the Vulcain Aft Bay. In the preceding report, multiple concepts were analysed and one concept was selected to complete the conceptual design. This concept consists of an Inflatable Heat Shield for re-entry, a Parafoil to control the flight at lower altitudes and a Mid-Air retrieval using a helicopter to perform a soft landing. A functional analysis was performed to define concept specific functions. This was done by means of a Functional Flow Diagram and Functional Breakdown Structure. In order to fulfil these functions, simulations were created of the most critical moments of the mission. One set of simulations analyse the trajectory of the system throughout the mission, predicting the location of landing. The other set of simulations is used to predict the critical load cases of the system. The aforementioned simulations were used to design the individual components of the system. By integrating these simulations and managing the iteration process, the overall system characteristics and configuration were established. The system was then analysed for sustainability, reliability, risk, maintainability and safety. The requirement compliance of the system was then updated, detailing which requirements have achieved full compliance, marginal compliance, no compliance and which have not been sufficiently investigated. Proposals to make all requirements fully compliant are included in a feasibility analysis. These include different design approaches for the team and design changes to the VuAB to accommodate the recovery system. From there strategies on future verification and validation activities was set forth together with operational, refurbishment and production plans. These plans and the design of the system were used to create an updated business model with return on investment figures, which predict a significant cost reduction on a per launch basis within five years. Finally a set of future recommendations and plan is proposed for the continuation of the project.