Shaping “Next Generation Civil Tilt Rotor (NGCTR)”

Clean Sky is the largest European research programme developing innovative, cutting-edge technology aimed at reducing CO2, gas emissions and noise levels produced by aircraft.
Funded by the EU’s Horizon 2020 programme, Clean Sky contributes to strengthening European
aero-industry
 collaboration, global leadership and competitiveness.  

Empowering rotorcraft testing and design by Big Data and Artificial Intelligence

ADMITTED key factors

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

Numbers

3

Flying aircraft prototypes

Up to 30.000

parameters for each flight condition

600.000

total flight conditions

4.000

flights

Topic Leader

Consortium

About

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. 

This project ADMITTED has received funding from the Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No GA832003