Development of a CFD data-driven surrogate model using the neural network approach for prediction of aircraft performance characteristics

Evaluation of aircraft performance for design, certification, and maintenance purposes requires aerodynamic knowledge for the entire flight envelope of an aircraft. Simplified models that relate geometric properties and flight conditions of an aircraft to its aerodynamic properties are simply not sufficient anymore, as non-linear aerodynamic...

Amplitude Dynamics of Motion Sickness

Increased susceptibility to motion sickness, due to the transition away from driving, will be one of the major hurdles in the widespread use of automated vehicles. Sustained exposure to motion sickness can lead to the disuse of automated vehicle technology among users. Thus, there is a need to mitigate motion sickness. To do so, a robust model...

Distributed FDI using Recurrent Neural Networks for Thruster Faults in Formation Flying Satellites

Certain Formation Flying missions rely on their orbital control thrusters to maintain the formation, making a Fault Detection and Isolation (FDI) system that is capable of detecting thruster faults very valuable. In addition, communication between satellites in a formation can be expensive. In this thesis, a distributed FDI approach was...

A Machine Learning Approach to Evaluating Aircraft Deviations from Planned Routes

With the increasing trend in air traffic demand and evidence of large deviations from filed flight plans, airspace capacity is not being optimally utilized. In order to improve air traffic flow and capacity management systems, so that air traffic control operators can handle more aircraft safely, air traffic predictability needs to be improved....