Flight testing is an important phase during the development of an aircraft to validate the design. Aircraft are properly instrumented to generate large amounts of information that need to be to be properly evaluated and analysed. Flight test programmes take several years and are significant cost contributor to the aircraft production life cycle. ADMITTED aims to increase the quality and productivity of an experiment, leading to a required test point reduction or increased predictive capabilities. This is achieved by adopting a complex hardware architecture to support big data analysis and implementing specific algorithms to support data correlation, time series management and statistical analysis. Furthermore, to support flight test engineers, novel approaches based on machine learning are provided to support the technicians in detecting specific flight conditions. The same platform is also adapted to support the development of the Next Generation Civil Tilt Rotor Technology Demonstrator.
The Next Generation Civil Tilt Rotor is a research and development project for the definition of an innovative and advanced concept for a new generation Tiltrotor.
Along with a cruise speed of over 500 km/h and a 2500 kg payload it will allow for an increased productivity and efficiency. It will be an “all weather” rotorcraft and will have a positive impact both on the mobility of goods and people and on environment due to reduced emissions and noise.
ADMITTED Key Factor
• A big data platform to collect and handle thousands of hours of flight
• Novel Machine Learning algorithms to detect flight conditions
• Data fusion for multiple sources and analysis with AI techniques
• Support the development of the Next Generation Civil Tilt Rotor
• 3 Flying aircraft prototypes
• Up to 30.000 parameters for each flight condition
• 600.000 total flight conditions
• 4.000 flights