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ADMITTED
Advanced Data Methods for Improved Tiltrotor Test and Design
The ADMITTED Project
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.
Projects Facts
- Grant agreement ID: 832003
- Start date: 1 February 2019
- End date: 30 November 2023
- Funded under: H2020-EU.3.4.5.3.
- Joint Undertaking: Clean Sky 2
- Overall budget: € 1 718 330
Shaping the “Next Generation Civil Tilt Rotor (NGCTR)”
Funded by the EU’s Horizon 2020 programme, Clean Sky contributes to strengthening European aero-industry collaboration, global leadership and competitiveness.
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.
The Research: Empowering rotorcraft testing and design by Big Data and Artificial Intelligence
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
Numbers
• 3 Flying aircraft prototypes
• Up to 30.000 parameters for each flight condition
• 600.000 total flight conditions
• 4.000 flights
