Project 1: Certification by simulation for advanced air mobility
Advanced Air Mobility (AAM) is referred to a set of vehicles that provide air transport services in a metropolitan area using powered-electric vertical take-off and landing (eVTOL) aircraft. Several companies are developing eVTOLs to replace helicopters when the time is right. AAM offers opportunities for the cities of the future, but it presents several challenges. The project focuses on the development of eVTOL digital twin models to be used for vehicle certification, able to evaluate mobility schemes, emergency procedures, together with handling qualities and pilot’s workload. The aeroelastic behaviour of these aircraft will be considered as well, to highlight potential aeromechanical instabilities, and to provide a robust tool including aeroelastic certification of the eVTOL critical parts.
Supervisors: Dr. Annie Liang (RMIT) and Dr. Vincenzo Muscarello (RMIT) and Prof. Giuseppe Quaranta (POLIMI) and Michele Sesana (TXT e-Solutions)
Project 2: Airspace risk analysis and evaluation for advanced air mobility with eVTOL aircraft
The project focuses on airspace risk analysis and evaluation for advanced air mobility with eVTOL aircraft. Safety is paramount in aviation operation. Emerging aviation operations with eVTOL in low altitude airspace bring challenges to airspace risk management. Complexed operational environment and new avionics technologies on board require a new safety risk analysis and evaluation methodology, which may totally be different from traditional commercial aircraft in high altitude airspace.
Supervisors: Dr. Annie Liang (RMIT) and Dr. Vincenzo Muscarello (RMIT) and Prof. Giuseppe Quaranta (POLIMI) and Michele Sesana (TXT e-Solutions)
Project 3: eVTOL aircraft pilot abnormal behaviour identification and prediction with wearable sensors
The project focuses on identify the abnormal behaviours of eVTOL aircraft pilot during the flight missions. Pilots play an important role in future advanced air mobility. Complexed and dense operational environment around hot spot areas may cause eVTOL aircraft pilots’ fatigue and then perform some abnormal behaviours, which bring dangers to the safe operation. By using wearable sensors, we could monitor the signal from pilots and help identify and predict those abnormal behaviour, to mitigation the risks. The research experiments will conduct via flight simulator other than real flight activities. Numerical data collection and machine learning algorithm will be applied to generate real-time warnings if necessary.
Supervisors: Dr. Annie Liang (RMIT) and Dr. Vincenzo Muscarello (RMIT) and Prof. Giuseppe Quaranta (POLIMI) and Michele Sesana (TXT e-Solutions)
